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Harnessing the Power of Artificial Intelligence in Health Care: Balancing Promise with Caution
The goal of this chapter is to offer an outline of how humans may be integrated into AI frameworks for
Earth tracking. It’s going to take a look at the benefits of human-contact integration and speak about
how this technique can be adapted to a diffusion of tracking situations. Furthermore, the human-touch
integration additionally gives a human-in-the-loop management device that could evaluate and approve
any
outputs
produced
by
using
the
AI
models.
The
human
contact
can
also
assist
with
AI
version
calibration
and validation that might, in any other case, be extra tough or not possible with the AI
algorithms on my own. For instance, floor fact information from skilled professionals may be used to
cross-validate and calibrate the AI fashions to ensure that they may be producing correct outcomes.
Taking those measures can assist us in balancing the advantages of human-contact integration with the
capability risks that include it, allowing us to use AI frameworks to monitor and respond to worldwide
environmental adjustments.
The purpose of this review is to provide a comprehensive review of publications related to artificial intelligence (AI) applications in healthcare for the year 2022. With an exponentially increasing number of publications related to AI in healthcare,there is a need to have a curated,timely and data driven review.
This year's review provides a comprehensive analysis of more than 9000 publications related to AI in healthcare for the year 2022. We provide a (1) quantitative analysis, (2) deep learning based automated qualitative analysis and (3) speciality expert based evaluation of AI in healthcare publications.
"Cite as: Mishra, S., R. Awasthi, J. Cywinski, A. Khanna, K. Maheshwari, A. Naylor, N. Abdallah, C. Weight, A. Bhattacharyya, A. Khare, G. Singh, S. Reddy, J. Cha, A. Anand, H. Nguyen, A. Tandon, J. Lee, N. Farrokhian, S. Sharma, A. Khosla, A. Ozair, R. Gullapalli, A. Guha, A. Bur, S. Chawla, T. Vachon, S. Segal, A. Tiwari, M. Ahluwalia, F. Papay and P. Mathur (2023). Artificial Intelligence in Healthcare: 2022 Year in Review.DOI: 10.13140/RG.2.2.19573.86241"
Background
Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.
Methods
Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI.
Results
Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health.
Conclusions
To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.
The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less-common responses to treatment or unique health care needs. AI leverages sophisticated computation and inference to generate insights, enable the system to reason and learn, and empower clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which non-genomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
Artificial intelligence (AI) is a disruptive technology that involves the use of computerised algorithms to dissect complicated data. Among the most promising clinical applications of AI is diagnostic imaging, and mounting attention is being directed at establishing and fine-tuning its performance to facilitate detection and quantification of a wide array of clinical conditions. Investigations leveraging computer-aided diagnostics have shown excellent accuracy, sensitivity, and specificity for the detection of small radiographic abnormalities, with the potential to improve public health. However, outcome assessment in AI imaging studies is commonly defined by lesion detection while ignoring the type and biological aggressiveness of a lesion, which might create a skewed representation of AI's performance. Moreover, the use of non-patient-focused radiographic and pathological endpoints might enhance the estimated sensitivity at the expense of increasing false positives and possible overdiagnosis as a result of identifying minor changes that might reflect subclinical or indolent disease. We argue for refinement of AI imaging studies via consistent selection of clinically meaningful endpoints such as survival, symptoms, and need for treatment.
Purpose
The aim of the current narrative review was to summarize the available evidence in the literature on artificial intelligence (AI) methods that have been applied during robotic surgery.
Methods
A narrative review of the literature was performed on MEDLINE/Pubmed and Scopus database on the topics of artificial intelligence, autonomous surgery, machine learning, robotic surgery, and surgical navigation, focusing on articles published between January 2015 and June 2019. All available evidences were analyzed and summarized herein after an interactive peer-review process of the panel.
Literature review
The preliminary results of the implementation of AI in clinical setting are encouraging. By providing a readout of the full telemetry and a sophisticated viewing console, robot-assisted surgery can be used to study and refine the application of AI in surgical practice. Machine learning approaches strengthen the feedback regarding surgical skills acquisition, efficiency of the surgical process, surgical guidance and prediction of postoperative outcomes. Tension-sensors on the robotic arms and the integration of augmented reality methods can help enhance the surgical experience and monitor organ movements.
Conclusions
The use of AI in robotic surgery is expected to have a significant impact on future surgical training as well as enhance the surgical experience during a procedure. Both aim to realize precision surgery and thus to increase the quality of the surgical care. Implementation of AI in master–slave robotic surgery may allow for the careful, step-by-step consideration of autonomous robotic surgery.
Telehealth and remote patient monitoring have expanded the reach of traditional clinical practice by removing geographical barriers as well as clinical limitations. Will this lead to increased value in healthcare? Are clinical outcomes going to improve alongside the delivery of better patient experiences? Will technology make the delivery of healthcare more sustainable? Despite most of us feeling like we could not work, live, or play without our mobile phones, they have only been in existence (as we know them today) for the last 20 years. However, the continuous advancement of technology and capability has affected healthcare and brought medicine, first into our homes and increasingly into our pockets. The future of telehealth fits into a consumer world that expects high quality, instant access, and personalized health propositions. As the demand for personalized medicine rises, more devices are released to capture vital signs, well-being metrics, and background data. In the clinical setting, this could allow real-time monitoring and preemptive doctor/patient interactions to prevent adverse incidents. At the very least, these developments offer the chance for richer and better-informed clinical decisions to be made based on longitudinal metrics. None of this is happening in isolation, and in fact some of the biggest companies in the world (e.g., Apple) have made their intent clear with devices such as watches which have engaged people to perform remote monitoring even when they are not “patients”. This combined with the increasing number of health apps (325,000 in 2017) are changing the way people engage with their own health and interact with clinicians. It is expected that telehealth and wearable technology—allowing remote monitoring—will continue to play an increasingly significant role in almost all future healthcare delivery models from prevention to recovery and everything in-between.
Background:
Healthcare provider burnout is considered a factor in quality of care, yet little is known about the consistency and magnitude of this relationship. This meta-analysis examined relationships between provider burnout (emotional exhaustion, depersonalization, and reduced personal accomplishment) and the quality (perceived quality, patient satisfaction) and safety of healthcare.
Methods:
Publications were identified through targeted literature searches in Ovid MEDLINE, PsycINFO, Web of Science, CINAHL, and ProQuest Dissertations & Theses through March of 2015. Two coders extracted data to calculate effect sizes and potential moderators. We calculated Pearson's r for all independent relationships between burnout and quality measures, using a random effects model. Data were assessed for potential impact of study rigor, outliers, and publication bias.
Results:
Eighty-two studies including 210,669 healthcare providers were included. Statistically significant negative relationships emerged between burnout and quality (r = -0.26, 95 % CI [-0.29, -0.23]) and safety (r = -0.23, 95 % CI [-0.28, -0.17]). In both cases, the negative relationship implied that greater burnout among healthcare providers was associated with poorer-quality healthcare and reduced safety for patients. Moderators for the quality relationship included dimension of burnout, unit of analysis, and quality data source. Moderators for the relationship between burnout and safety were safety indicator type, population, and country. Rigor of the study was not a significant moderator.
Discussion:
This is the first study to systematically, quantitatively analyze the links between healthcare provider burnout and healthcare quality and safety across disciplines. Provider burnout shows consistent negative relationships with perceived quality (including patient satisfaction), quality indicators, and perceptions of safety. Though the effects are small to medium, the findings highlight the importance of effective burnout interventions for healthcare providers. Moderator analyses suggest contextual factors to consider for future study.
Medical education is at a crossroads. Although unique features exist at the undergraduate, graduate, and continuing education levels, shared aspects of all three levels are especially revealing, and form the basis for informed decision-making about the future of medical education.This paper describes some of the internal and external challenges confronting undergraduate medical education. Key internal challenges include the focus on disease to the relative exclusion of behavior, inpatient versus outpatient education, and implications of a faculty whose research is highly focused at the molecular or submolecular level. External factors include the exponential growth in knowledge, associated technologic ("disruptive") innovations, and societal changes. Addressing these challenges requires decisive institutional leadership with an eye to 2020 and beyond--the period in which current matriculants will begin their careers. This paper presents a spiral-model format for a curriculum of medical education, based on disease mechanisms, that addresses many of these challenges and incorporates sound educational principles.
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