Background The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

BackgroundThe increase in life expectancy and recent advancements in technology and medical science have changed the way we deliver health services to the aging societies. Evidence suggests that home telemonitoring can significantly decrease the number of readmissions, and continuous monitoring of older adults’ daily activities and health-related issues might prevent medical emergencies.ObjectiveThe primary objective of this review was to identify advances in assistive technology devices for seniors and aging-in-place technology and to determine the level of evidence for research on remote patient monitoring, smart homes, telecare, and artificially intelligent monitoring systems.MethodsA literature review was conducted using Cumulative Index to Nursing and Allied Health Literature Plus, MEDLINE, EMBASE, Institute of Electrical and Electronics Engineers Xplore, ProQuest Central, Scopus, and Science Direct. Publications related to older people’s care, independent living, and novel assistive technologies were included in the study.ResultsA total of 91 publications met the inclusion criteria. In total, four themes emerged from the data: technology acceptance and readiness, novel patient monitoring and smart home technologies, intelligent algorithm and software engineering, and robotics technologies. The results revealed that most studies had poor reference standards without an explicit critical appraisal.ConclusionsThe use of ubiquitous in-home monitoring and smart technologies for aged people’s care will increase their independence and the health care services available to them as well as improve frail elderly people’s health care outcomes. This review identified four different themes that require different conceptual approaches to solution development. Although the engineering teams were focused on prototype and algorithm development, the medical science teams were concentrated on outcome research. We also identified the need to develop custom technology solutions for different aging societies. The convergence of medicine and informatics could lead to the development of new interdisciplinary research models and new assistive products for the care of older adults.

Introduction Disruptive medical technologies, wearable devices and new diagnostic solutions have been shaping the future of healthcare, and the health informatics skills gap has become a major problem for technology-centric healthcare applications. This study evaluated the relationships between a specific practical skills training method and students' confidence in using wireless monitoring devices along with the attitude towards technology adoption. Methods Six practical exercises were developed to provide health informatics technical skills to transfer medical information and display multi-channel biological signals. Two hundred and six undergraduate nursing students received a telemedicine and homecare training course. Their familiarity with various data formats and likelihood to recommend telemedicine and remote monitoring applications were measured. Results The skills training session changed students' attitudes towards remote patient monitoring, and the majority of students provided positive feedback about their confidence in using wireless monitoring devices after the training session. Students stated their plans to use the technology when they start practising and to educate their patients to promote the use of telemedicine. Conclusion We propose a skills training framework that covers (a) telemedicine, (b) m-Health and connected health,

Background Existing health informatics curriculum requirements mostly use a competency-based approach rather than a skill-based one. Objective The main objective of this study was to assess the current skills training requirements in graduate health informatics curricula to evaluate graduate students’ confidence in specific health informatics skills. Methods A quantitative cross-sectional observational study was developed to evaluate published health informatics curriculum requirements and to determine the comprehensive health informatics skill sets required in a research university in New York, United States. In addition, a questionnaire to assess students’ confidence about specific health informatics skills was developed and sent to all enrolled and graduated Master of Science students in a health informatics program. Results The evaluation was performed in a graduate health informatics program, and analysis of the students’ self-assessments questionnaire showed that 79.4% (81/102) of participants were not confident (not at all confident or slightly confident) about developing an artificial intelligence app, 58.8% (60/102) were not confident about designing and developing databases, and 54.9% (56/102) were not confident about evaluating privacy and security infrastructure. Less than one-third of students (24/105, 23.5%) were confident (extremely confident and very confident) that they could evaluate the use of data capture technologies and develop mobile health informatics apps (10/102, 9.8%). Conclusions Health informatics programs should consider specialized tracks that include specific skills to meet the complex health care delivery and market demand, and specific training components should be defined for different specialties. There is a need to determine new competencies and skill sets that promote inductive and deductive reasoning from diverse and various data platforms and to develop a comprehensive curriculum framework for health informatics skills training.

This study demonstrated that the multidisciplinary smart home healthcare and health informatics training laboratories and the hands-on exercises improved students' technology adoption rates and their self-confidence in using wireless patient monitoring devices.