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Cognitive Science in Telemedicine: From Psychology to Artificial Intelligence

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... • The identification of issues in artificial intelligence implementation and/or possible solutions to existing issues, including social science, political science, and philosophy/ethics contributions (Pravettoni et al., 2015;Triberti et al., 2020a,b) (area D); ...
... Among the orders of psychological science, for instance, the investigation of human-machine interfaces fits into this structure. This order includes not just the thought of AI versatile and progressed calculations (AUI-Adaptive User Interface), yet additionally the investigation of individual intellectual instruments, since the proposed arrangements are usable as per the capacities of every person [25]. New innovations have been introduced to improve the checking of patients with incessant conditions, for example, cardiovascular breakdown. ...
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The latest advances in information technologies have fostered more innovative Telemedicine systems that provide high-quality medical screening and diagnostics to patients anytime, anywhere. These new developments have drawn significant attention, especially in underserved communities as well as during crises and emergencies. However, in order to successfully implement these technologies and provide adequate care, various challenges need to be tackled. For instance, networking techniques must be improved to optimize bandwidth, frequency, and data transmission. In addition, these technologies must implement robust security features in order to maintain patients’ privacy. Throughout this chapter, the authors present recent developments in Telemedicine and provide a detailed taxonomy to classify the different components forming Telemedicine. Additionally, we investigate the challenges in designing effective Telemedicine systems for all types of users. Furthermore, we highlight a holistic view that can guide the future development and design of Telemedicine systems, enhancing the user experience of both patients and healthcare providers. This research effort suggests the need for systematic perspectives to enhance the current Telemedicine systems with regards to models, frameworks, guidelines, and management decision-making processes. Nevertheless, several challenges respect to design strategies, communication management, reliability, availability, and maintainability still exist.
... Examples: An effective self-management platform could be based on research on patients' needs and cognitive style, by using multiple design-oriented methods ( Pravettoni et al., 2015;Kondylakis et al., 2017). Regarding psycho-cognitive aspects to be included in implementation, Kondylakis et al. (2014) developed and validated ALGA-C, a web-based tool featuring a questionnaire for cancer patients (analyzing psycho-cognitive aspects ranging from personal needs to cognitive/decision making style) and a profiling mechanism. ...
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In recent years, technology has been developed as an important resource for healthcare management, especially in regard to chronic conditions. In the broad field of eHealth, mobile technology (mHealth) is increasingly used to empower patients not only in disease management but also in the achievement of positive experiences and experiential growth. mHealth tools are considered powerful because, unlike more traditional Internet-based tools, they allow patients to be continuously monitored and followed by their own mobile devices and to have continual access to resources (e.g., mobile apps or functions) supporting healthcare management activities. However, the literature has shown that, in many cases, such technology not accepted and/or adopted in the long term by its users. To address this issue, this article reviews the main factors influencing mHealth technology acceptance/adoption in healthcare. Finally, based on the main aspects emerging from the review, we propose an innovative approach to mHealth design and implementation, namely P5 mHealth. Relying on the P5 approach to medicine and healthcare, this approach provides design suggestions to address mHealth adoption issues already at the initial stages of development of the technologies.
... ANNs turned out to be efficient in making predictions over the past years and they have been largely used in supporting clinical diagnosis (Pravettoni, Folgieri, & Lucchiari, 2015); however, only a few studies applied them in recognizing learning disorders. From 1980 on, some papers have been published about AI applications to the topic: for instance, Geiman and Nolte (1990) proposed an expert system for LDs identification with rather positive results. ...
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The diagnosis of Learning Disabilities (LD) is frequently subject to cognitive biases. In Italy, minimal diagnostic standards have been identified during a national Consensus Conference (2010). However, specialists use different protocols to assess reading and cognitive abilities. Thus, we propose to support LDs diagnosis with Artificial Neural Networks (ANN). Clinical results from 203 reports were input to investigate which ones can predict LD diagnosis. In addition, correlations among LDs were explored. Preliminary results show that ANNs can be useful to support a clinical diagnosis of LDs with an 81.93% average accuracy, and, under certain conditions, with a 99% certainty. Additionally, the 10 most meaningful tests for each LD have been identified and significant correlations between dyscalculia and dyslexia were found.
... Research has underlined the potential for social media, mobile phones, and the Internet in general, to improve mental and physical health, treat addictions, and also to help individuals experiencing homelessness ( Quan, Joseph, Keller & Arch, 2011;McInnes, Fix, Solomon Petrakis, Sawh & Smelson, 2015). Furthermore, Artificial Intelligence is discovering new opportunities to overcome the limitations of the computational approach and, consequently, making possible new advanced healthcare applications, especially in the social-healthcare (Pravettoni, Folgieri & Lucchiari, 2015). ...
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In a relatively short time, information and communication technologies (ICT) have spread worldwide, from defense and space exploration to large industrial applications, and to the worlds of commerce, education, and entertainment. ICT is changing people’s daily lives, the way they work, buy, sell, and learn, and also the way that services are run in the healthcare sector. Consequently, e-health, telehealth, and telemedicine are now terms that are commonly used in this sphere, encompassing three main computer-assisted areas in healthcare, namely: clinical assessment, diagnosis, and therapy. E-health comprises the monitoring of patients’ health, the promotion of good practices, and the prevention and treatment of health conditions by electronic means, as well as the provision of online access to literature and medical knowledge. One of the first reviews of telehealth is that provided by Winters (2002), who identified two major subsets of telehealth: telemedicine (i.e., delivery of clinical services) and telehealthcare (i.e., management of disability and health).
... By the way, our opinion is that to turn from experimentation to actual telerehabilitation services we need to carefully evaluate many contextual factors both technical and organizational. Although the recent technological advances are introducing new opportunities to overcome the limitations of the traditional computer-based approach [10], the transition towards telerehabiliation services doesn't appear so simple. ...
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Research has underlined the potential for Information and Communication Technologies (ICT) to improve healthcare services making them more effective and reducing their costs. In the last decade, thanks to the developments in technology,a wide range of ICT-based solutions have been implemented for the rehabilitation of patients and the concept of telerehabilitation is commonly used to refer to the provision of rehabilitation care at a distance. Telerehabilitation, or e-rehabilitation, is now an autonomous discipline and, lately, the notion of social telerehabilitation has been introduced to distinguish the application of ICT to the social rehabilitation sphere. This article aims at illustrating the major challenges that arise in the implementation and delivery of effective and sustainable telerehabilitation services. In this regard, organizational aspects represent a crucial factor, since the development of effective telerehabilitation services is not only a question of technology. Much depends on appropriate organization models that include additional management procedures as well as new criteria for the acceptance of telerehabilitation treatments.
... Research has underlined the potential for social media, mobile phones, and the Internet in general, to improve mental and physical health, treat addictions, and also to help individuals experiencing homelessness ( Quan, Joseph, Keller & Arch, 2011;McInnes, Fix, Solomon Petrakis, Sawh & Smelson, 2015). Furthermore, Artificial Intelligence is discovering new opportunities to overcome the limitations of the computational approach and, consequently, making possible new advanced healthcare applications, especially in the social-healthcare ( Pravettoni, Folgieri & Lucchiari, 2015). ...
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This paper reports on the Latvian national science program VPP INOSOCTEREHI, a three-year multidisciplinary project on social telerehabilitation, started in 2014 and conducted by four Latvian Universities, which is aimed at the use of mobile technology in the field of social rehabilitation. The activities of the first year of the VPP INOSOCTEREHI project are illustrated here; focusing on the concept of social telerehabilitation, and highlighting the multidisciplinary competences and expertise necessary for developing social telerehabilitation services (social pedagogy, special education, computer science, engineering, physiotherapy). The conceptual framework of the project is outlined, and the methodology that has been adopted to create the common background necessary to ensure effective cooperation between partners is presented.
... The critical task of the system is the automatic profiling of user behaviors. Several systems were introduced in recent years to address elder-care issues, principally fall detection systems [5][6][7][8][9]. However, most of these systems either are too expensive for mass use or are of low quality. ...
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This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder’s daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user’s health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.
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Social telerehabilitation, which focuses on solving limitations and social issues associated with health conditions, represents a further specialization in telerehabilitation. Both telerehabilitation and social telerehabilitation are grounded in the delivery of rehabilitation services through telecommunication networks, especially by means of the internet. Essentially, telerehabilitation comprises methods of delivering rehabilitation services using ICT to minimize the barriers of distance, time, and cost. One can define social telerehabilitation as being the application of ICT to provide equitable access to social rehabilitation services, at a distance, to individuals who are geographically remote, and to those who are physically and economically disadvantaged.
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This chapter explores the most relevant aspects in relation to the outcomes and performance of the different components of a healthcare system with a particular focus on mobile healthcare applications. In detail, we discuss the six quality principles to be satisfied by a generic healthcare system and the main international and European projects, which have supported the dissemination of these systems. This diffusion has been encouraged by the application of wireless and mobile technologies, through the so-called m-Health systems. One of the main fields of application of an m-Health system is telemedicine, for which reason we will address an important challenge encountered during the realization of an m-Health application: the analysis of the functionalities that an m-Health app has to provide. To achieve this latter aim, we will present an overview of a generic m-Health application with its main functionalities and components. Among these, the use of a standardized method for the treatment of a massive amount of patient data is necessary in order to integrate all the collected information resulting from the development of a great number of new m-Health devices and applications. Electronic Health Records (EHR), and international standards, like Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR), aims at addressing this important issue, in addition to guaranteeing the privacy and security of these health data. Moreover, the insights that can be discerned from an examination of this vast repository of data can open up unparalleled opportunities for public and private sector organizations. Indeed, the development of new tools for the analysis of data, which on occasions may be unstructured, noisy, and unreliable, is now considered a vital requirement for all specialists who are involved in the handling and using of information. These new tools may be based on rule, machine or deep learning, or include question answering, with cognitive computing certainly having a key role to play in the development of future m-Health applications.
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Virtual Worlds such as Second Life provide unique opportunities to simulate real life scenarios and immerse the user in an environment that can be tailored to meet specific educational requirements. In these Immersive Learning Environments, students and faculty can interact from anywhere in the real world. From a general education perspective, they allow for virtual classrooms, virtual libraries, interactive role-playing, remote seminars, etc. From a medical education and science perspective, Immersive Learning Environments such as Second Life can be used to model doctor-patient interaction, clinical diagnosis skills, and three dimensional objects ranging from individual molecules and cells to whole organ systems, both healthy and diseased. The principal goal of our project is the development of virtual patient simulations for medical education. In order to simulate real patients with greatest fidelity, the virtual patients are controlled by artificial intelligence. This allows students to engage in a natural language conversation with the patient to obtain relevant patient history, symptoms, etc, and then to develop relevant differential diagnoses and treatments appropriate for the simulated condition of the patients. Virtual world medical simulations enable students to rehearse professional behaviors in a risk-free environment, providing opportunities for skills practice prior to real-world patient encounters.
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Artificial Intelligence chatbot is a technology that makes interaction between man and machine possible by using natural language. In this paper, we proposed an architectural design of a chatbot that will function as virtual diabetes physician/doctor. This chatbot will allow diabetic patients to have a diabetes control/management advice without the need to go to the hospital. A general history of a chatbot, a brief description of each chatbots is discussed. We proposed the design of a new technique that will be implemented in this chatbot as the key component to function as diabetes physician. Using this design, chatbot will remember the conversation path through parameter called Vpath. Vpath will allow chatbot to gives a response that is mostly suitable for the whole conversation as it specifically designed to be a virtual diabetes physician.
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The research project intends to demonstrate how EEG detection through BCI device can improve the analysis and the interpretation of colours-driven cognitive processes through the combined approach of cognitive science and information technology methods. To this end, firstly it was decided to design an experiment based on comparing the results of the traditional (qualitative and quantitative) cognitive analysis approach with the EEG signal analysis of the evoked potentials. In our case, the sensorial stimulus is represented by the colours, while the cognitive task consists in remembering the words appearing on the screen, with different combination of foreground (words) and background colours. In this work we analysed data collected from a sample of students involved in a learning process during which they received visual stimuli based on colour variation. The stimuli concerned both the background of the text to learn and the colour of the characters. The experiment indicated some interesting results concerning the use of primary (RGB) and complementary (CMY) colours.
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The terms 'telemedicine', 'telehealth' and 'e-health' are often used interchangeably. We examined the occurrence of these terms in the Scopus database. A total of 11,644 documents contained one of the three terms in the title or abstract. Telemedicine was the most common term, with 8028 documents referring to it, followed by e-health (n = 2573) and then telehealth (n = 1679). Telemedicine was referred to in documents from 126 countries; the terms telehealth and e-health were found in publications from 55 and 99 countries, respectively. Documents with telemedicine in their title or abstract first appeared in 1972, and continued to appear at a low rate until 1994 when they started to increase rapidly; telehealth showed a similar pattern, but with the growth beginning about five years later. Although articles containing the term e-health appeared later than the other two terms, the rate of increase was higher. Articles (journal papers) were the most common type for the three key terms, followed by conference papers and review articles. Publication rates for telemedicine or telehealth or e-health were compared with two other relatively new fields of study: Minimally Invasive Surgery (MIS) and Highly Active Antiretroviral Therapy (HAART). Publications concerning HAART seem to have reached a peak and are now declining, but those with the three key terms and those concerning MIS are both growing. The variation in the level of adoption for the three terms suggests ambiguity in their definition and a lack of clarity in the concepts they refer to.
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Objectives: To discuss the advantages and disadvantages of rehabilitation applications of virtual reality. Methods: VR can be used as an enhancement to conventional therapy for patients with conditions ranging from musculo-skeletal problems, to stroke-induced paralysis, to cognitive deficits. This approach is called “VR-augmented rehabilitation.” Alternately, VR can replace conventional interventions altogether, in which case the rehabilitation is “VR-based.” If the intervention is done at a distance, then it is called “telerehabilitation.” Simulation exercises for post-stroke patients have been developed using a “teacher object” approach or a video game approach. Simulations for musculo-skeletal patients use virtual replicas of rehabilitation devices (such as rubber ball, power putty, peg board). Phobia-inducing virtual environments are prescribed for patients with cognitive deficits. Results: VR-augmented rehabilitation has been shown effective for stroke patients in the chronic phase of the disease. VR-based rehabilitation has been improving patients with fear of flying, Vietnam syndrome, fear of heights, and chronic stroke patients. Telerehabilitation interventions using VR have improved musculo-skeletal and post-stroke patients, however less data is available at this time. Conclusions: Virtual reality presents significant advantages when applied to rehabilitation of patients with varied conditions. These advantages include patient motivation, adaptability and variability based on patient baseline, transparent data storage, online remote data access, economy of scale, reduced medical costs. Challenges in VR use for rehabilitation relate to lack of computer skills on the part of therapists, lack of support infrastructure, expensive equipment (initially), inadequate communication infrastructure (for telerehabilitation in rural areas), and patient safety concerns.