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Humanizing Artificial Intelligence
Abstract
If human intelligence is the learned ability to gain from experience and the capacity to handle unfamiliar situations and manipulate abstract concepts while using experience and knowledge to change the world, then the concept of artificial intelligence (AI)—a huge advance in data processing and computing—would not easily compare with true human intelligence. In this Viewpoint, AI broadly encompasses machine learning, natural language processing, expert systems that emulate the decision making and reasoning of human experts, and other related applications.
... Just like in other professions, AI is undergoing a transformation and is now being used in dentistry. From helping with clinical diagnosis and treatment planning to scheduling and managing regular appointments, AI can do many simple jobs in the dental clinic more accurately, with fewer workers, and with fewer mistakes than humans [3,4]. ...
... Essentially, the field of artificial intelligence has witnessed a meteoric rise in technological advancements over the past decade. Nevertheless, it remains unclear from the existing literature how AI-related data might aid in the detection, prevention, and treatment of dental problems [4]. We conducted a review that addressed various modalities of artificial intelligence, its applications and outcomes in dentistry to gain a better understanding of the current trends of AI in this field, especially in oral and dental diagnoses. ...
... At its core, AI relies on algorithms and computational models to process large datasets, identify patterns, and make predictions or decisions based on those patterns [3]. These algorithms, often rooted in machine learning and deep learning techniques, enable systems to improve their performance over time as they are exposed to more data [4]. ...
Artificial intelligence (AI) is revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes.
... In particular, the prospect of increasing our reliance on automation raises concerns about loss of compassion and humanity in interactions with the subjects of data [10][11][12]. Indeed, overreliance on algorithms risks increasing bias by a range of societal factors such as age, gender, ethnicity, ability, and socioeconomic status [12][13][14]. Furthermore, there are privacy concerns: given that AI potentially involves novel uses of sensitive data, there is a need to ensure that this data (and by extension, its subjects) are still protected [15][16][17]. ...
... We argue that the qualities of nurses and AI-driven lifestyle monitoring systems in long-term care complement each other, leading to increased value when combined. Nurses excel in the relationship domain, offering emotional support, empathy, and compassion, and working toward the benefit of other humans [13,24], also known as the humane element of nursing and recognized as part of fundamental care [47]. They are good at considering contextual variables to get a holistic view of a patient, are compassionate, and can make genuine connections with the persons for whom they provide care [13]. ...
... Nurses excel in the relationship domain, offering emotional support, empathy, and compassion, and working toward the benefit of other humans [13,24], also known as the humane element of nursing and recognized as part of fundamental care [47]. They are good at considering contextual variables to get a holistic view of a patient, are compassionate, and can make genuine connections with the persons for whom they provide care [13]. However, there are limitations to human capabilities. ...
Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult’s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.
... Another potential trend in this field is the humanizing AI. According to Irani [20], even with a huge advance in data processing and computing via AI, it would not easily compare with the true potential of human intelligence. Creativity is one of the greatest AI challenges facing the human mind. ...
... Human minds can generate counterfactuals, imaginative flights, and dreams. Using AI surprises are welcome in some fields as art and music; however, surprises in medical diagnosis or treatment are unwelcome [20]. Nonetheless, the point of greatest concern is yet another [20], if AI is going to make clinicians better at caring for humans, the data sets being used must be representative of society and not biased by sex, race, ethnicity, socioeconomic status, age, ability, and geography [21]. ...
... Using AI surprises are welcome in some fields as art and music; however, surprises in medical diagnosis or treatment are unwelcome [20]. Nonetheless, the point of greatest concern is yet another [20], if AI is going to make clinicians better at caring for humans, the data sets being used must be representative of society and not biased by sex, race, ethnicity, socioeconomic status, age, ability, and geography [21]. This need for representation is not only a data science issue, but also a moral one. ...
The potential of AI for Sports Medicine has been highlighted in the literature. However,few results are found when the command line(“artificial intelligence”) AND (“sports medicine”) was applied in three of the main data-bases. These findings show how much we still have to improve in this area of knowledge and this chapter is going to start this process by presenting advances, potential trends, and future challenges of AI in Sports Medicine.
... Patient access to medical records and self-directed testing can introduce challenges to caring for patients, such as misinterpretation of tests results and unclear responsibility for any resulting harm [53]. The critical examination of unintended consequences emphasizes the need for ethical considerations to ensure optimal patient care [54]. ...
... AI's value in healthcare stems from its ability to support, rather than replace, the patient-physician relationship, which is an essential component of the fundamentally social enterprise of healthcare. Poorly implemented AI risks marginalizing humanity, while wise implementation can free up physicians' cognitive load, allowing them to be better caregivers [54]. For instance, AI health coaches integrated into care teams can enhance patient engagement, whereas detached, one-size-fits-all coaches may alienate patients [86]. ...
This chapter explores the ethical implications and successful implementations of artificial intelligence (AI) in primary care and family medicine residency programs. It begins by highlighting the transformative potential of AI in revolutionizing decision-making processes and enhancing proactive care in healthcare settings. Ethical considerations for healthcare providers encompass various facets, including legal implications, healthcare recipient confidentiality, autonomy, as well as the changing responsibilities of doctors amidst the age of artificial intelligence. The impacts on healthcare professionals and training programs emphasize incorporation of AI training into syllabi and the significance of interdisciplinary collaboration. Case studies showcase successful AI implementations, such as PainChek® for pain assessment and IDx-DR for diabetic ocular pathologies detection, while also addressing ethical dilemmas and strategies for mitigation. Future perspectives advocate for tailor-made ethical guidelines, education and training programs, and collaborative efforts to ensure responsible AI integration while upholding ethical standards and patient-centric care. Overall, the chapter emphasizes the critical need for ethical frameworks and collaborative approaches to harness AI’s potential in primary care effectively.
... This is a significant factor to consider for the patient population who would, in any case, enter the emergency department, regardless of possible overtriaging. Eventually, the purpose of a CDSS in the emergency department is to relieve the health care personnel's burden, so that time and effort can be optimally allocated [20,21]. Additionally, to fully gain the benefits of a CDSS, it is important that the CDSS is properly implemented in health care professionals' everyday work and workflow [22]. ...
... To the authors' knowledge, this is the first study to assess the diagnostic performance and triage sensitivity of a CDSS in a broad clinical setting, using patient-submitted data. The patient sample is representative of the normal adult patient population entering the emergency department in Finland and is presumably unbiased by demographic variance, which strengthens the findings of this study [20]. ...
Background:
Computerized clinical decision support systems (CDSSs) are increasingly adopted in health care to optimize resources and streamline patient flow. However, they often lack scientific validation against standard medical care.
Objective:
The purpose of this study was to assess the performance, safety, and usability of a CDSS in a university hospital emergency department setting in Kuopio, Finland.
Methods:
Patients entering the emergency department were asked to voluntarily participate in this study. Patients aged 17 years or younger, patients with cognitive impairments, and patients who entered the unit in an ambulance or with the need for immediate care were excluded. Patients completed the CDSS web-based form and usability questionnaire when waiting for the triage nurse's evaluation. The CDSS data were anonymized and did not affect the patients' usual evaluation or treatment. Retrospectively, 2 medical doctors evaluated the urgency of each patient's condition by using the triage nurse's information, and urgent and nonurgent groups were created. The International Statistical Classification of Diseases, Tenth Revision diagnoses were collected from the electronic health records. Usability was assessed by using a positive version of the System Usability Scale questionnaire.
Results:
In total, our analyses included 248 patients. Regarding urgency, the mean sensitivities were 85% and 19%, respectively, for urgent and nonurgent cases when assessing the performance of CDSS evaluations in comparison to that of physicians. The mean sensitivities were 85% and 35%, respectively, when comparing the evaluations between the two physicians. Our CDSS did not miss any cases that were evaluated to be emergencies by physicians; thus, all emergency cases evaluated by physicians were evaluated as either urgent cases or emergency cases by the CDSS. In differential diagnosis, the CDSS had an exact match accuracy of 45.5% (97/213). The usability was good, with a mean System Usability Scale score of 78.2 (SD 16.8).
Conclusions:
In a university hospital emergency department setting with a large real-world population, our CDSS was found to be equally as sensitive in urgent patient cases as physicians and was found to have an acceptable differential diagnosis accuracy, with good usability. These results suggest that this CDSS can be safely assessed further in a real-world setting. A CDSS could accelerate triage by providing patient-provided data in advance of patients' initial consultations and categorize patient cases as urgent and nonurgent cases upon patients' arrival to the emergency department.
... A number of authors have suggested that AI has significant potential to counter this trend and make the practice of medicine "more human" (Israni and Verghese 2019;Meskó et al. 2018;Topol 2019). In particular, they suggest, automating the input and retrieval of patient data with AI might allow clinicians to return the patient to the centre of their attention. ...
What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?
... It is sometimes argued that the use of AI and ML could allow clinicians more time to spend with their patients (Israni and Verghese 2019;Topol 2019). However, the various tasks associated with maintaining AI and ML systems could equally lead to increased administrative burdens for clinicians that could further interfere with the quality of care and empathy in the doctor-patient relationship (Maddox et al. 2019;Sparrow and Hatherley 2020). ...
Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this paper, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) -- and under-estimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.
... As artificial intelligence (AI) advances, there is a shift towards computational processing that mimics human brain functionality. The simple binary system-from input (true or false) to output (yes or no)-is insufficient for the complex encoding and decoding processes, which requires additional states, such as unknown or other states [6,7]. Consequently, signal adapters limited to binary computation rules are insufficient for these emerging requirements [8,9]. ...
Electronic technology, based on signal conversion induced by voltage stimulation, forms the core foundation of the state-of-the-art intelligent devices, tools, and equipment. Such conversions are inherently binary and limited because they rely solely on voltage, which presents challenges for many emerging frontier applications. Here, a two-dimensional ordered conjugated system of reduced graphene oxide/polypyrrole (rGO/PPy) has been developed. Multi-stimulus response signal adapters have been constructed, utilizing the electrical anisotropy inherent in the rGO/PPy system. This electrical anisotropy, derived from the quasi-two-dimensional geometry of rGO/PPy, enables the device to produce distinct electrical signals in response to various stimuli. With effective responses to light and pressure, the two most common input stimuli other than voltage, it can output quaternary/denary signals and visual optical signals, as well as enables information encryption using passive devices. Furthermore, the signal adapter demonstrates high cyclic stability under repeated pressure and/or light loading. The successful development of this low-cost, scalable signal adapter paves the way for the next-generation of intelligent systems, promising advancements in human-computer interaction, electronic skin, biological implant equipment, and related fields.
... The workload can be doubled and a geometric increase in computational engineering can be realized when multiple variables are added to an experiment. The documentation work of medical staff can be reduced by integrating these variables with AI [79]. The results are more credible and predictable because of the computing power. ...
Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.
... These are specifically beneficial for intricate data formats, including imagery, as they can represent an image and its hierarchical attributes, such as macroscopic patterns, shapes, edges and corners. Deep or multi-layered NNs are considered universal approximation systems [9,10]. In the presence of a series of mathematical constraints, NNs can associate any input and approximate (for instance, an X-ray image displaying a decayed tooth) to a designated output (for example, "decayed tooth"). ...
AI is expanding and thriving quickly across every sector. It can acquire knowledge from handling tasks and human expertise that usually necessitate human-like intelligence. It has also been utilized in dentistry and medicine. Peri-implantitis is a pathological phenomenon observed in tissues surrounding dental implants, marked by inflammation in the peri-implant mucosa and continuous depletion of supporting bone. Recently, developments in advanced implant dentistry, imaging methods and digital transformation have come together to introduce novel dental implantology procedures. For the detection of peri-implant disorders, particularly peri-implantitis, AI employing 2D radiographs can increase patient care in implant dentistry. This narrative review will describe the role of Al in dentistry and medicine, specifically in the detection of peri-implantitis.
... This can reduce diagnostic errors due to human oversight. AI-powered systems help medical professionals by freeing up their time for more challenging tasks; AI can provide a more vital doctor-patient relationship, which is particularly important in primary care, allowing doctors to provide more holistic and individualized treatments 26,27 . ...
General practices (GPs), called family physicians in certain countries, are the cornerstone of primary health care. The increase in average lifespan and, thereby, the number of chronic diseases has recently increased the workload of GPs and decreased the time spent on the patient. Implementations of Artificial intelligence (AI)-powered systems are essential in GPs to facilitate the jobs of health professionals. Implementing AI-driven systems is expected to help health professionals diagnose and treat. AI involves the machine simulation of human cognitive capabilities, encompassing a range of technologies, including deep learning and machine learning. AI is currently being used across various applications in medicine and continues to evolve, and its role in medicine is expected to become increasingly prominent. AI-enhance sensor systems can continuously monitor physiological parameters and generate personalized medicinal therapy. However, the employment of AI in GPs is still in the very early phase. AI is a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise. This review will focus on applying artificial intelligence in general practices (GPs).
... 120 (4) AI could be utilized for record keeping, leveraging speech, voice, and text recognition elements, reducing space and time for data records. 121 (5) Patients could directly use AI as a promising platform to make the entire theranostic process personalized by collecting data from wearable devices or POC devices. (6) AI could overcome on-off-medicine, where patients barely visit doctors in a real-life setting due to many factors like high out-of-pocket costs, which could be minimized by continuous non-invasive monitoring. ...
Point of care (POC) diagnostic devices provide a method for rapid accurate identification of disease through analysis of biologically relevant substances. This review focuses on the utility of POC testing for early detection of periodontitis, a critical factor in treating the disease. Accessing the oral cavity for biological sampling is less invasive when compared to other internal test sites, and oral fluids contain biomarkers indicative of periodontitis. The ease of access makes the mouth an excellent target location for the development of POC devices. In this review, accepted standards in industry by which these devices must adhere, provided by the World Health Organization such as REASSURED and CLIA, are discussed. An overview is provided for many periodontal biomarkers currently being investigated as a means of predicting periodontal disease and its progression. POC devices currently being investigated for the identification and monitoring of periodontal disease such as paper-based and lab-on-a-chip based devices are outlined. Limitations of current POC devices on the market are provided and future directions in leveraging biomarkers as an adjunctive method for oral diagnosis along with AI-based analysis systems are discussed. Here, we present the ESSENCE sensor platform, which combines a porous non-planar electrode with enhanced shear flow to achieve unprecedented sensitivity and selectivity. The combination of the ESENCE chip with an automated platform allows us to meet the WHO's ASSURED criteria. This platform promises to be an exciting POC candidate for early detection of periodontitis.
... On the one hand, introduction of AI has the potential to increase practitioner's ability to care and express care [35]. On the other hand, the introduction of cold, hard algorithms instead of human minds and bodies has the potential do decrease the amount and value of care extended to the patient [27] citing [133] . ...
This article presents a scoping review of the relevant research discussing the ethics of medical artificial intelligence (AI). Relevant moral and human values can inform the design of ethical medical AI. The value sensitive design (VSD) framework offers a method by which to do this. But much conceptual work must be done in order to apply the VSD framework to medical AI. The goal of this review is to survey existing literature discussing moral and human values (such as responsibility, transparency, and respect for autonomy) in medical AI development. Ultimately, the objective of the review is to advance the VSD methodology for medical AI technologies, in which relevant moral values will inform technology design. Papers were considered eligible if they fulfilled three criteria: (1) provided a discussion of one or multiple moral, ethical, or human values (2) regarding AI or machine learning in a (3) medical, clinical, or health context. We conducted our search on five databases: OVID Embase, OVID Medline, IEEE, Web of Science, and Scopus. After performing our search, we screened title and abstract, then the full text of each paper for relevance using the Covidence platform. After the final sample was determined, we extracted relevant information by coding the papers using NVivo. We found nearly 100 moral values discussed regarding medical AI. In this search we were able to find not only which values are most discussed and how, but also specific, unique, and emerging moral values in the medical AI domain.
... AI lacks intuition and the urgency of beneficence to rescue patients; AI crumbles with erratic patient behaviors (although this criticism can equally apply to humans); AI models are not mature enough to respond to statements like "I want to commit suicide."; AI cannot admit mistakes nor advocate for colleagues and justice; and AI has no humanistic interactions or perceptions (Verghese et al., 2018) such as eye contact, authenticity, creativity, love, empathetic rather than stoic approaches, caring for patients (Stokes and Palmer, 2020), kindnes keeping in mind that much of the pain is psychologically fought, and trust toward medical staff often boosts quick recovery (Israni and Verghese, 2019). ...
Purpose
The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.
Design/methodology/approach
This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.
Findings
The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.
Originality/value
This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.
... Not only is it produced, but also audiences engage with it. AI can be a potent tool for artists to reach new and original expression levels during the creative process [23]. However, it also raises ethical and philosophical issues that cast doubt on the nature of art and the place of the artist in its production. ...
... In agreement with Chan and Hu [11], they are ready to embrace this new technology but in a collaboration where people maintain control and are not replaced by AI [17,20,37,38]. Finally, in line with the literature, they attributed "anthropomorphic" qualities to the language model (1 student referred to ChatGPT using the gender pronoun "he"), possibly explained by the establishment of a personal connection between the student and the language model while engaging in human-like conversations in combination with student's own gender-related perceptions and interaction style [39]. ...
Background
The recent artificial intelligence tool ChatGPT seems to offer a range of benefits in academic education while also raising concerns. Relevant literature encompasses issues of plagiarism and academic dishonesty, as well as pedagogy and educational affordances; yet, no real-life implementation of ChatGPT in the educational process has been reported to our knowledge so far.
Objective
This mixed methods study aimed to evaluate the implementation of ChatGPT in the educational process, both quantitatively and qualitatively.
Methods
In March 2023, a total of 77 second-year dental students of the European University Cyprus were divided into 2 groups and asked to compose a learning assignment on “Radiation Biology and Radiation Protection in the Dental Office,” working collaboratively in small subgroups, as part of the educational semester program of the Dentomaxillofacial Radiology module. Careful planning ensured a seamless integration of ChatGPT, addressing potential challenges. One group searched the internet for scientific resources to perform the task and the other group used ChatGPT for this purpose. Both groups developed a PowerPoint (Microsoft Corp) presentation based on their research and presented it in class. The ChatGPT group students additionally registered all interactions with the language model during the prompting process and evaluated the final outcome; they also answered an open-ended evaluation questionnaire, including questions on their learning experience. Finally, all students undertook a knowledge examination on the topic, and the grades between the 2 groups were compared statistically, whereas the free-text comments of the questionnaires were thematically analyzed.
Results
Out of the 77 students, 39 were assigned to the ChatGPT group and 38 to the literature research group. Seventy students undertook the multiple choice question knowledge examination, and examination grades ranged from 5 to 10 on the 0-10 grading scale. The Mann-Whitney U test showed that students of the ChatGPT group performed significantly better (P=.045) than students of the literature research group. The evaluation questionnaires revealed the benefits (human-like interface, immediate response, and wide knowledge base), the limitations (need for rephrasing the prompts to get a relevant answer, general content, false citations, and incapability to provide images or videos), and the prospects (in education, clinical practice, continuing education, and research) of ChatGPT.
Conclusions
Students using ChatGPT for their learning assignments performed significantly better in the knowledge examination than their fellow students who used the literature research methodology. Students adapted quickly to the technological environment of the language model, recognized its opportunities and limitations, and used it creatively and efficiently. Implications for practice: the study underscores the adaptability of students to technological innovations including ChatGPT and its potential to enhance educational outcomes. Educators should consider integrating ChatGPT into curriculum design; awareness programs are warranted to educate both students and educators about the limitations of ChatGPT, encouraging critical engagement and responsible use.
... This might include not just diagnostic aid technology but also data from patients' voices and speech to help the dentist keep track of the record and help in saving time. 135 Using these continually obtained data will reduce the effect of "on-off-medicine," in which patients only meet the doctor for a few seconds. However, the health issues develop over the year leading to an increase and decrease in the symptoms of the disease over time. ...
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
... This fact allows humanizing the RS and assisting their end-users. Humanizing Artificial Intelligence (AI) [17,30] is one of the major challenges faced by science and would help address its application in medical [13], ethical [31] and customer service [32] issues, among others. ...
Recommender systems that include some reliability measure of their predictions tend to be more conservative in forecasting, due to their constraint to preserve reliability. This leads to a significant drop in the coverage and novelty that these systems can provide. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, which enables the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.
... In short, AI has the potential to improve the efficiency, accuracy, and quality of dental care, leading to better outcomes for patients. 2 ...
Artificial Intelligence (AI) is rapidly advancing in the field of dentistry, offering new and innovative solutions to improve the accuracy and efficiency of oral healthcare. In recent years, AI has been used in a variety of dental specialties, including oral pathology, prosthodontics, endodontic, periodontics, and implant dentistry. AI algorithms can analyze dental images, assist with diagnosis, improve treatment planning, automate routine tasks, and predict outcomes. These advancements have the potential to significantly improve the quality of dental care, leading to better outcomes for patients. However, it is important to consider both the benefits and potential limitations of AI in dentistry, and to ensure that these systems are used in a responsible and ethical manner. This abstract highlights the impact of AI in dentistry, and the potential for continued advancements in this field to revolutionize oral healthcare.
... A Swedish expert on water in his research suggested that there will be water stress when the water availability will fall below 1000 cubic meters per person per day. 1 Now that almost in every household people prefer to use R.O filter water for drinking purpose and for other household uses like cleaning utensils, washing, bathing etc. it is a concern that more the amount of water is processed through the R.O system there is increase in the wastage of water in the process. 2,3 One of the most important technologies for purifying water is the reverse osmosis approach (RO). This method utilizes semipermeable membrane for the purpose of removing large particles, molecules and ions from drinking water. ...
Reverse osmosis (RO) is a water purification process that uses a partial permeable membrane to remove ions, unwanted molecules and larger particles from drinking water. In reverse osmosis, an applied pressure is used to overcome osmotic pressure, a colligative property that is driven by chemical potential differences of the solvent, a thermodynamic parameter. In the process of reverse osmosis the amount of water that is drained is a concern area for the people using the R.O. filtration device in their household because it wasted about 70% of the water to purify just one liter of water. This R.O. waste water can be utilized for various purposes such as washing vehicle like car bike etc, cleaning toilet this study is aimed to assess the knowledge reverse osmosis waste water utilization among general public at Indore.
1.To assess the pretest knowledge regarding reverse osmosis (R.O) waste water utilization among general public. 2. To assess the posttest knowledge regarding reverse osmosis waste water utilization among general public. 3. To evaluate the effectiveness of structured teaching program on reverse osmosis (R.O) waste water utilization among general public.
H1- there will be significant difference between pretest and posttest knowledge who received structured teaching program regarding the utilization of waste R.O water.
Quantitative, pre-experimental, one group pretest posttest design was adopted for the study. Total of 60 general public selected by using simple randomized sampling technique was used.
Structured knowledge questionnaire.
Data was analyzes using descriptive and inferential statistics.
In the pre-test majority of the sample (44 out of 60, 73.3%) had inadequate knowledge and in the post-test, majority (54 out of 60, 90%) had adequate knowledge regarding reverse osmosis. A paired‘t’ test was done and it showed a‘t’ value of 22.34 at 0.05 level of significance, this indicates the effectiveness of structured teaching programme in enhancing the knowledge of the general public. There was no association found between the mean pre-test knowledge of the general public. There was no association found between the mean pre-test knowledge scorer with the selected socio-demographic variable such as age (χ2 = 8.643), gender (χ2 = 4.455), education qualification (χ2 = 4.706), Occupation (χ2 = 2.531), number of family member (χ2 = 5.653) and previous knowledge about reverse osmosis filter water (χ2 =0.393).
There is a significant difference between the mean pre-test and post-test knowledge score among general public regarding reverse osmosis waste water utilization.
... YZ'nin tanısal desteği sadece radyolojik tanı ile sınırlı kalmaz, aynı zamanda ses ve konuşma tanıma, el yazısı tanıma ya da farklı diller arası çeviri desteği sağlayarak da kayıt tutma süresi kısaltılabilir. 12 Radyolojik veriler ışığında verimliliği ve doğruluğu artırmak için YZ büyük bir potansiyele sahiptir, ancak bu durum beraberinde pek çok tuzak ve önyargı da getirmektedir. Halihazırda klinik ortamlarda hasta bakımı için YZ'yi kullanma deneyimi çok azdır. ...
Ağız, Diş ve Çene Radyolojisinde Yapay Zekâ Uygulamaları Neler Yapabiliyor?
Yapay zekâ (YZ) çalışmaları sayesinde son birkaç yıldır tıbbi tanılar alanında oldukça büyük ilerlemeler elde edilmiştir ve bu ilerlemeler daha çok görüntü işlenmesi alanlarında gerçekleşmektedir. Sağlık alanındaki verilerin hem mevcut hem de potansiyel büyüklüğü göz önüne alındığında, meydana gelen büyük verinin YZ için ortaya çıkarabileceği hedef sayılar, çalışmaların başlangıcı için gerekli te-melleri yaratmıştır. Diş hekimliği alanında hem nitelikli tespit sağlaması ile teşhisi güçlendirmesi, hem radyologlar arasındaki raporlama ve planlama farklılıklarını ortadan kaldırması hem de görüntüleme tet-kiklerinin performansını iyileştirmek için YZ algoritmaları yüksek başarılı olanaklar sunabilir. Bu ma-kalede, diş hekimliğinde YZ algoritmaları kullanımı, çalışma alanları, etik, eğitim, rutin pratiğe katkıları ve diş hekimliği radyolojisinin nasıl etkileyebileceği ve bu yöntemlerin kullanımı ile alanımızın nasıl ge-lişmekte olduğunu ele almaktayız. Anah tar Ke li me ler: Yapay zekâ; derin öğrenme; makine öğrenmesi; radyoloji; eğitim ABS TRACT Great advances have been made in the field of medical diyagnosis in the last few years with regard to artificial intelligence (AI) studies, and these advancements mostly include image processing. Considering the current and potential data size in the health field, the target numbers that the big data can reveal for AI created the necessary foundations for the initiation of these studies. AI algorithms can offer highly successful opportunities in dentistry, to provide qualified detectionto increase diyagnostic accuracy, to eliminate reporting and planning differences between radiologists, and to improve the performance of imaging examinations. In this article, we discuss the usage of AI algorithms in dentistry, fields of study, ethics, education, contributions to routine practice, how dental radiology can affect and how our field is developing with the use of these methods. İ lk duyduğumuz andan itibaren çok merak uyandıran konu; sağlık bilimleri ve yapay zekâ (YZ) birlikteliğinin ortaya çıkarabileceği potansiyeller, güvenilirlik, etik ve veri güvenliği ile ilgili pek çok soruyu içermektedir. YZ tarafından koyulan tanı deneyimli doktorlar kadar verimli olabilir mi? Yeterince güvenilir midir? Acaba mesleki becerileri-mizi ele geçirir mi? Toplum tanı koyabilmesi için doktoru mu tercih eder, yoksa YZ ye-terli midir? Veriler nerede saklanacak? Diş hekimliği alanında da kullanılabilir mi? vs. Yapay zekâ ne demek? Kısaca açıklanacak olursa, YZ kendisine verilen görüntüyü işlemesi sırasında belli anatomik noktaları öğrenerek hareket eder ve işlenen veri sayısı arttıkça daha iyi öğrenir. Veri seti ve etiketleme oluşturulduktan sonra, çıktı alabilmek için eğitim yapmak gerekir ve böylece öğrenme gerçekleşir. Görüntü işleme sırasında eti-ketleri oluşturan kişilerin mesleki yeterliliği ve deneyimi de eğitim sonucunun daha ayırt edici olmasına katkıda bulunur. YZ uygulamaları ile konulan tanıların, alanında uzman olan kişilerden bile daha yüksek doğruluk oranına sahip olduğu çeşitli çalışmalar tara-fından ortaya konmuştur. 1,2
... The collection of digital health data allows artificial intelligence to provide suitable interaction between different levels of data. With the fastpaced life, artificial intelligence helps dentists to keep records and provide their recorded data actively [1,11]. Continuous non-invasive digital monitoring helps the patients to learn about their health and the importance of their visits to their practitioner [12]. ...
Artificial Intelligence has exploded as a research subject in the 21st century as the hardware requirements of theoretical Artificial intelligence in the 19th and 20th century have translated into reality. This has led to rapid progress with the realization of multiple kinds of neural networks, as well as improvements along classical Machine Learning models like Decision Trees, Support Vector Machines etc. Most recently, the focus in Machine Learning has now shifted towards generative networks with Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) leading modern research topics. With advent of the Internet of Medical Things (IoMT) and modern time leaps in the domain of Artificial Intelligence with the explosion of Machine and Deep Learning, Medical AI has grasped the central stage in modern research. Applications of Medical AI vary from detections of various cancerous tumors to prediction of arrhythmia attacks. The augmentation of such AI embedded techniques into the medical profession has streamlined analysis as well as aided professionals in reaching a more efficient and accurate diagnosis. This has also translated in the field of dentistry where strong deep learning architectures such as Convolutional neural networks in the form of Resnet, Inception, GoogleNet etc have been used to process raw images and detect a range of dental diseases. This paper aims to provide an overview of progress made in the field of Machine Learning particularly focusing on its medical and dental applications. Different facets of Machine Learning are discussed with respect to their strengths, shortcomings, and the way artificial intelligence has been used to tackle problems in the medical field. Finally, a descriptive overview about state-of-the-art machine learning reliant applications that are being used in different dental subfields is discussed along with current challenges the industry faces today.
... It is sometimes argued that the use of AI and ML could allow clinicians more time to spend with their patients. 29,30 However, the various tasks associated with maintaining AI and ML systems could equally lead to increased administrative burdens for clinicians that could further interfere with the quality of care and empathy in the doctor-patient relationship. 31,32 This risk seems particularly acute in the case of MAMLS, because healthcare institutions will likely need to significantly expand the scope of their data collection policies and procedures to be able to provide the continuous stream of new data that training MAMLS will require. ...
Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature.
Target audience: The target audiences for this tutorial are the developers of ML AI systems, healthcare regulators , the broader medical informatics community, and practicing clinicians.
Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit - diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) - and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.
... AI and each industry involved offer the new impetus for the innovative application of enterprises and industry and the improvement of human life and provide convenience for the acquisition of information [7,8]. In terms of data analysis, AI can help human resource practitioners become more prospective [9]. Information technology is developed continuously and rapidly; translation medicine, evidence-based medicine, and pharmacoeconomics are rapidly developed; the research of clinical medicine is strongly advocated by the state; and the demand for scientific research still grows constantly [10]. ...
Under the background of artificial intelligence (AI), a human resource information management system was designed to facilitate hospital human resource management and improve hospital management efficiency. Based on AI, SOA was constructed and Java2 platform enterprise edition (J2EE) was combined with Java to design and research hospital human resource information management system. In addition, the function and performance required by the system were tested. The results showed that the designed system showed high safety in requirement analysis and performance. The function focused mainly on the systematic analysis of personnel management, recruitment management, organization and personnel management, and patient medical information. The constructed system could work normally and achieve the efficiency of hospital human resource management. The evaluation response time of system home page access was less than 1 second when 300 users were concurrent, and the utilization rate of service CPU was lower than 50% without abnormal memory fluctuation. The concurrent response time of all 20 managers online was less than 5 seconds, and the utilization rate of the service was lower than 70%. When the information of 100 employees in the system was queried concurrently, the average CPU utilization of the database server exceeded 90%. After performance optimization, the test result showed that the transaction response time was reduced to 0.23 seconds, which met the target requirement. In conclusion, the proposed intelligent human resource management system could reduce hospital management cost and the high sharing of human resource information provided a reference for the decision-making system of hospital leaders.
... The role of intelligent systems in dentistry is based on the paradigm that any medical decision requires the physician to have adequate knowledge on the subject matter which can be reinforced by AI. 28 Data in the healthcare field is aggregated universally, and is available in heterogeneous clumps such as demographic data, socioeconomic data, medical history, clinical and investigational data all of which can be integrated and correlated by AI. 29 Artificial intelligence plays a role in research and interactive patient care. The ability of the patients to monitor their own heart rate, blood pressure or oral status through wearables, acts as a strong motivation factor. ...
Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.
... In summary, we propose 3 key sociotechnical considerations for integrating AI in the healthcare delivery system: AI technology has the potential to support human decision making for diagnosing, treating, and monitoring patients and extend the capabilities of clinicians (Israni & Verghese, 2019;Shneiderman, 2020). This requires a humancentered approach where the technology is designed to augment and support the work of clinicians (Endsley, 2017;Shneiderman, 2020;Vasey et al., 2021). ...
In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g., diagnostic accuracy) as well as reduce the burden on clinicians (e.g., documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.
... AI may streamline routine work and increase the face-to-face time doctors/dentists and their patients have ("humanising care"). 5 This may not only come via diagnostic assistance systems, but voice, speech, and text recognition and translation, enabling doctors/dentists to reduce time for record keeping 24 . Continuous non-invasive monitoring of health and behaviour will enable a much deeper, individual understanding of the drivers and processes underlying health and disease. ...
Training Tomorrows Dentists with Artificial Intelligence
... 7,20 Today, advances in artificial intelligence tend to further consolidate the culturally coded "objective" masculine view and threaten to marginalize the "soft skills" that are coded as feminine, just as other well-described unintended consequences of artificial intelligence tend to further disadvantage minority groups. 21 Failing Our Patient s ...
This chapter focuses on the possibilities and risks of applying the Generative AI with the Healthcare System's Elements. The authors also discuss the role of Generative AI which has paved way for Drug Discovery, improvement of diagnosis as well as development of personalized medicine. In this aspect, they discuss the various ethical concerns such as Data Privacy and Algorithms' limitations that result from the use of AI in Healthcare. Reflective to the above elements, the chapter captures ways through which these challenges can be addressed. In sharing the ways of managing the risks and protecting patient's rights this chapter gives a step-by-step solution of how Generative AI can transform Healthcare for the better while paying attention to moral and pragmatic concerns. This chapter provides useful information on the topic for Healthcare Practitioners, Policy Makers and AI Specialists in the development of Artificial Intelligence in care delivery.
This position paper, written by medical students and physicians primarily from Canadian universities, argues for the crucial integration of artificial intelligence (AI) competencies into undergraduate medical education (UGME) across Canada. The authors highlight the rapid advancements and increasing applications of AI in healthcare, emphasizing the necessity for future physicians to be equipped to work alongside and critically evaluate these technologies. The paper outlines key principles, concerns regarding the current lack of AI education, and specific recommendations for developing AI learning objectives aligned with existing medical competency frameworks. Furthermore, it discusses ethical and societal implications of AI in healthcare, such as bias and data privacy, and promotes the role of student-led initiatives in fostering AI education and advocacy within medical schools. The appendices provide a snapshot of current AI-related curricular and extracurricular offerings in Canadian medical schools, along with suggested delivery methods and resources for educators.
Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.
The integration of Artificial Intelligence (AI) into primary healthcare has the potential to alleviate the challenges faced by the healthcare system of Hong Kong. This article aims to explore the benefits and challenges of AI applications in addressing the primary healthcare challenges in Hong Kong. It also examines the potential of AI solutions to overcome the challenges faced by the healthcare system in this region. Furthermore, by highlighting the advantages of AI applications in primary healthcare, the article provides recommendations for integrating AI into the primary healthcare system, along with policy and regulatory considerations. This information can serve as a valuable reference for leveraging advanced AI technology to improve healthcare outcomes and accessibility. By proactively addressing concerns and upholding the principles of responsible AI integration, Hong Kong can establish these intelligent technologies within its primary healthcare sector in an ethical and equitable manner.
This study integrates map information projection methods with machine learning algorithms to analyze the distribution of rare earth mines in mainland China. The information obtained through the map information projection method includes the latitude and longitude of the deposits, deposit type labels, and deposit names. This approach helps to overcome challenges related to the sensitivity of geological information. The acquired information was organized into a simple dataset containing only latitude and longitude information and a complete dataset containing additional information. These datasets were used to simulate the early and later stages of the research project, respectively. The K-Means algorithm was applied to the simple dataset, and the results demonstrated good clustering performance through specific validation. The Support Vector Machine (SVM) algorithm was applied to the complete dataset, and the analysis showed excellent classification performance, with relevant metrics (Accuracy, Precision, Recall, F1 Score) all around 90%. The experiments demonstrate that K-Means and SVM are suitable for information analysis in earth sciences and that they complement each other in research projects, being particularly applicable to the early and later stages of the project, respectively.The findings contribute to a more nuanced understanding of rare earth mineral distributions and underscore the potential for machine learning techniques to revolutionize geological sciences.
Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.
This millennium is rightly called the age of technology. Digital technology has replaced the traditional ways of life, and here, health sciences and education are no exceptions. Artificial intelligence (AI) is now being employed in many ways in imparting education. Research and innovations are constantly benefitting from AI for data collection and processing data in a split second. Moreover, AI is used for compiling decisive guidelines, comments, advice, and even final decisions. AI is bound to expand exponentially. This chapter discusses its application including moral principles influencing its use in medical education.
Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.
Yapay zeka, genellikle 21. yüzyılın en dönüştürücü teknolojisi olarak kabul edilmektedir. Yapay zekanın giderek daha fazla entegre edildiği alanlardan biri de sağlık hizmetleridir. Bu geniş kapsam içinde, yapay zekanın derin etkilerini görmeye başlayan özel bir disiplin ise ortodonti alanıdır. Bu derlemenin amacı, yapay zekanın ortodontide entegrasyonu üzerine daha fazla tartışmayı teşvik etmek ve hastanın bakımında artan doğruluk, verimlilik ve kişiselleştirme getirerek bu alanı dönüştürme ve geliştirme potansiyeline odaklanmaktır.
This chapter explores the scholarship that addresses the relationship between artificial intelligence (AI) and strategic management. While technology offers organizations many unique benefits, scholars and practitioners question the limits of AI with regard to the adoption and dissemination of strategic managerial decisions. This chapter offers judgment work as an important competency during this Fourth Industrial Revolution (Industry 4.0) as it helps managers to differentiate between tasks that can be automated by AI and tasks that can be augmented by the strategic use of AI. Implications for business education are discussed as well as suggestions for future research.
One of the ultimate goals of artificial intelligence is to achieve the capability of memory evolution and adaptability to changing environments, which is termed adaptive memory. To realize adaptive memory in artificial neuromorphic devices, artificial synapses with multi-sensing capability are required to collect and analyze various sensory cues from the external changing environments. However, due to the lack of platforms for mediating multiple sensory signals, most artificial synapses have been mainly limited to unimodal or bimodal sensory devices. Herein, we present a multi-modal artificial sensory synapse (MASS) based on an organic synapse to realize sensory fusion and adaptive memory. The MASS receives optical, electrical, and pressure information and in turn generates typical synaptic behaviors, mimicking the multi-sensory neurons in the brain. Sophisticated synaptic behaviors, such as Pavlovian dogs, writing/erasing, signal accumulation, and offset, were emulated to demonstrate the joint efforts of bimodal sensory cues. Moreover, associative memory can be formed in the device and be subsequently reshaped by signals from a third perception, mimicking modification of the memory and cognition when encountering a new environment. Our MASS provides a step toward next-generation artificial neural networks with an adaptive memory capability.
Aim: The aim of present study was intended to assess the dental teaching faculty of various dental colleges across India were how much aware and know about the role of Artificial Intelligence (AI)in Dentistry. Study Design: A descriptive cross-sectional study was performed using a pre-designed questionnaire through various social media. Place and Duration of Study: Through e-mails, and Whats App, the Questionnaire was sent to the participants of various dental colleges across India from 6th February 2023 to 31st March 2023. Methodology: Validated and pre-tested questionnaire was used and sent to dental teaching faculty of various dental institutions across India. A total of 407 responses were received. The received data was entered in Microsoft Excel sheet. Descriptive Statistical Analysis was done by using IBM SPSS version 26. Results: A total of 407 faculty members participated. Associate Professors/Readers stood in first place (149) and the least participation from Assistant Professors/Senior Lecturers (43). Gender wise males are dominated (217) than females (190). 42.4% of Professors were aware about the applications of AI already using in Dentistry and 93.9% of the participants of the opinion that in the future, AI is threat for the dentists. Conclusion: Almost all the participants expressed their opinion on AI based treatment which will be accepted by the patients and all the teaching faculty members knows about the latest advances of AI in dentistry.
Internet of Things (IoT) is an expansion of internet services for machines, enabling
machine-to-machine communication. It is defined as the domain of cyber-physical systems to
communicate and record events [1]. The domain has significant applications in agriculture [2],
environmental monitoring [3], health [4], and transportation [5]. As already discussed in preceding
chapters, the most active research domain and the most critical application of the Internet of things
is its healthcare application named the Internet of medical things (IoMT).
IoMT is an assemblage of things (devices) connected over the Internet, providing health
care or healthcare-related services to the user [6]. It is an infrastructure that links medical devices,
applications, and services, performing intricate tasks systematically. The link permits healthcare
personnel to monitor the patient condition remotely, efficiently performing clinical operations and
procedures. The facility profoundly affects the medical services’ effectiveness for remote users
with limited medical facilities or having difficulty accessing medical facilities frequently.
Additionally, the wearable IoMT devices make monitoring very easy through machine-to-machine
communication, linking the tiny gadget to the healthcare monitoring unit or even a doctor’s
smartphone who can track the activities of his/her patients seamlessly [7].
The digital disruptions in the modern era have introduced artificial intelligence in the
healthcare sector. Artificial intelligence (AI) technology has allowed the machine to discover, fit
and enhance based on the various datasets used to train AI. This has allowed the private and public
sector to work on developing systems by improving technology, thereby handling health and
services [8]. The prominent factors behind this progress are undoubtedly the smartphones and
IoMT, which facilitate innovation for improving lives. The remote monitoring and assistance
without the physical healthcare professional presence have allowed many healthcare assistance
applications to be possible. Digitization has allowed the term data to be more significant than ever
in the healthcare sector, where information is empowering the tools based on analysis and
intelligent programming. To handle the analysis of such datasets and extract useful information
and decision factors from it, the use of intelligent systems and their applications has greatly
increased [9].
Artificial intelligence (AI) has grown considerably in almost all fields of life in the recent
past. To better the healthcare sector and achieve a smart health ecosystem, the potential of existing
technologies like AI needs to be incorporated in giving better services. AI can serve as the major
enabler for the IoMT domain assisting medical experts in all forms of healthcare services ranging
from clinical decisions to automated diagnosis and much more [10]. Incorporating machine
learning and widely researched deep machine learning methods can be highly proficient in
decision-making based on the existing medical data analysis. Combining the above IoMT used
case with the AI, patient monitoring can be carried out using AI-assisted interfaces, allowing
continuous monitoring with less professional intervention with higher scalability. Using AI�assisted smart homes, robots, and virtual assistants can help provide care to the elderly and disabled
patients with minimal human interaction. Additionally, the use of analysis tools on the data
gathered from the linked IoMT devices/sensors can help predict healthcare situations like
pandemic and epidemic diseases. Similarly, during emergency cases, the AI IoMT systems can
help the professionals take the best measures for saving lives [11].
Sr. Editor. Uno de los desafíos clave en la inteligencia artificial (IA) es cómo llevar el conocimiento informal a una computadora. El creciente desarrollo de la información, las bases de datos y las formas de conocimiento han hecho que se desarrollen tecnologías que ayuden a realizar tareas complejas en áreas de la Odontología. El papel de la IA dispone estas herramientas que aportan nuevas perspectivas para el diagnóstico, clasificación, pronóstico y la planificación del tratamiento odontológico. Por medio de la presente carta al editor se expone la importancia de la intervención de la IA en la práctica clínica odontológica.
Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR.
Computer Vision and Internet of Things
Book
Computer Vision and Internet of Things
Technologies and Applications
Edited ByLavanya Sharma, Mukesh Carpenter
Edition1st Edition
First Published2022
eBook Published20 May 2022
Pub. LocationNew York
ImprintChapman and Hall/CRC
DOIhttps://doi.org/10.1201/9781003244165
Pages320
eBook ISBN9781003244165
SubjectsComputer Science, Engineering & Technology
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ABSTRACT
Computer Vision and Internet of Things: Technologies and Applications explores the utilization of Internet of Things (IoT) with computer vision and its underlying technologies in different applications areas. Using a series of present and future applications – including business insights, indoor-outdoor securities, smart grids, human detection and tracking, intelligent traffic monitoring, e-health departments, and medical imaging – this book focuses on providing a detailed description of the utilization of IoT with computer vision and its underlying technologies in critical application areas, such as smart grids, emergency departments, intelligent traffic cams, insurance, and the automotive industry.
Key Features
• Covers the challenging issues related to sensors, detection, and tracking of moving objects with solutions to handle relevant challenges
• Describes the latest technological advances in IoT and computer vision with their implementations
• Combines image processing and analysis into a unified framework to understand both IOT and computer vision applications
• Explores mining and tracking of motion-based object data, such as trajectory prediction and prediction of a particular location of object data, and their critical applications
• Provides novel solutions for medical imaging (skin lesion detection, cancer detection, enhancement techniques for MRI images, and automated disease prediction)
This book is primarily aimed at graduates and researchers working in the areas of IoT, computer vision, big data, cloud computing, and remote sensing. It is also an ideal resource for IT professionals and technology developers.
TABLE OF CONTENTS
Part Part 1|50 pages
Introduction to Computer Vision and Internet of Things
Chapter 1|15 pages
Rise of Computer Vision and Internet of Things
ByLavanya Sharma
Abstract
Chapter 2|10 pages
IoE: An Innovative Technology for Future Enhancement
BySudhriti Sengupta
Abstract
Chapter 3|12 pages
An Overview of Security Issues of Internet of Things
ByLavanya Sharma, Sudhriti Sengupta, Nirvikar Lohan
Abstract
Chapter 4|10 pages
Use of Robotics in Real-Time Applications
ByLavanya Sharma, Mukesh Carpenter
Abstract
Part Part 2|46 pages
Tools and Technologies of IoT with Computer Vision
Chapter 5|10 pages
Preventing Security Breach in Social Media: Threats and Prevention Techniques
ByLavanya Sharma
Abstract
Chapter 6|11 pages
Role of Image Processing in Artificial Intelligence and Internet of Things
BySudhriti Sengupta
Abstract
Chapter 7|22 pages
Computer Vision in Surgical Operating Theatre and Medical Imaging
ByMukesh Carpenter, Dharmendra Carpenter, Vinod Kumar Jangid, Lavanya Sharma
Abstract
Part Part 3|135 pages
IoT with Computer Vision for Real-Time Applications
Chapter 8|12 pages
Self-Driving Cars: Tools and Technologies
ByKavish Gupta, Deepa Gupta, Lavanya Sharma
Abstract
Chapter 9|30 pages
IoT and Remote Sensing
ByYaman Hooda
Abstract
Chapter 10|35 pages
Synthetic Biology and Artificial Intelligence
ByVukoman Jokanović
Abstract
Chapter 11|16 pages
Innovation and Emerging Computer Vision and Artificial Intelligence Technologies in Coronavirus Control
ByMukesh Carpenter, Vinod Kumar Jangid, Dharmendra Carpenter, Lavanya Sharma
Abstract
Chapter 12|17 pages
State of the Art of Artificial Intelligence in Dentistry and Its Expected Future
ByVukoman Jokanović, M. Živković, S. Živković
Abstract
Chapter 13|21 pages
Analysis of Machine Learning Techniques for Airfare Prediction
ByJaskirat Singh, Deepa Gupta, Lavanya Sharma
Abstract
Part Part 4|68 pages
Challenging Issues and Novel Solutions
Chapter 14|12 pages
CapsNet and KNN-Based Earthquake Prediction Using Seismic and Wind Data
BySandeep Dwarkanath Pande, Soumitra Das, Pramod Jadhav, Amol D. Sawant, Shantanu S. Pathak, Sunil L. Bangare
Abstract
Chapter 15|16 pages
Computer-Aided Lung Cancer Detection and Classification of CT Images Using Convolutional Neural Network
BySunil L. Bangare, Lavanya Sharma, Aditya N. Varade, Yash M. Lokhande, Isha S. Kuchangi, Nikhil J. Chaudhari
Abstract
Chapter 16|24 pages
Real-Time Implementations of Background Subtraction for IoT Applications
ByBelmar Garcia-Garcia, Thierry Bouwmans, Kamal Sehairi, El-Hadi Zahzah
Abstract
Chapter 17|14 pages
The Role of Artificial Intelligence in E-Health: Concept, Possibilities, and Challenges
ByPriyanka Gupta, Hardeo Kumar Thakur, Alpana
Abstract
Objective
Various health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.
Methods
A secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).
Results
Data from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).
Conclusion
Our study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.
Background
Artificial intelligence (AI) can be described as the use of computers to perform tasks that formerly required human cognition. The American Medical Association prefers the term ‘augmented intelligence’ over ‘artificial intelligence’ to emphasize the assistive role of computers in enhancing physician skills as opposed to replacing them. The integration of AI into emergency medicine, and clinical practice at large, has increased in recent years, and that trend is likely to continue.
Discussion
AI has demonstrated substantial potential benefit for physicians and patients. These benefits are transforming the therapeutic relationship from the traditional physician–patient dyad into a triadic doctor–patient–machine relationship. New AI technologies, however, require careful vetting, legal standards, patient safeguards, and provider education. Emergency physicians (EPs) should recognize the limits and risks of AI as well as its potential benefits.
Conclusions
EPs must learn to partner with, not capitulate to, AI. AI has proven to be superior to, or on a par with, certain physician skills, such as interpreting radiographs and making diagnoses based on visual cues, such as skin cancer. AI can provide cognitive assistance, but EPs must interpret AI results within the clinical context of individual patients. They must also advocate for patient confidentiality, professional liability coverage, and the essential role of specialty-trained EPs.
While it is well known that the homogeneity of clinical trial participants often threatens the goal of attaining generalizable knowledge, researchers often cite issues with recruitment, including a lack of interest from participants, shortages of resources, or difficulty accessing particular populations, to explain the lack of diversity within sampling. It is proposed that social media might provide an opportunity to overcome these obstacles through affordable, targeted recruitment advertisements or messages. Recruiters are warned, however, to be cautious using these means, since risks related to privacy and transparency can take on a new hue.
Background:
The 2013 pooled cohort equations (PCEs) are central in prevention guidelines for cardiovascular disease (CVD) but can misestimate CVD risk.
Objective:
To improve the clinical accuracy of CVD risk prediction by revising the 2013 PCEs using newer data and statistical methods.
Design:
Derivation and validation of risk equations.
Setting:
Population-based.
Participants:
26 689 adults aged 40 to 79 years without prior CVD from 6 U.S. cohorts.
Measurements:
Nonfatal myocardial infarction, death from coronary heart disease, or fatal or nonfatal stroke.
Results:
The 2013 PCEs overestimated 10-year risk for atherosclerotic CVD by an average of 20% across risk groups. Misestimation of risk was particularly prominent among black adults, of whom 3.9 million (33% of eligible black persons) had extreme risk estimates (<70% or >250% those of white adults with otherwise-identical risk factor values). Updating these equations improved accuracy among all race and sex subgroups. Approximately 11.8 million U.S. adults previously labeled high-risk (10-year risk ≥7.5%) by the 2013 PCEs would be relabeled lower-risk by the updated equations.
Limitations:
Updating the 2013 PCEs with data from modern cohorts reduced the number of persons considered to be at high risk. Clinicians and patients should consider the potential benefits and harms of reducing the number of persons recommended aspirin, blood pressure, or statin therapy. Our findings also indicate that risk equations will generally become outdated over time and require routine updating.
Conclusion:
Revised PCEs can improve the accuracy of CVD risk estimates.
Primary funding source:
National Institutes of Health.
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