Artificial Intelligence in Health Professions Education
Open accessAbstract
Artificial intelligence (AI) is widely used in medicine. AI may provide low-cost solutions to health problems and is especially important for developing countries. Health-care professionals will play an important role in providing data for educating AI systems and validating these through clinical trials. AI may necessitate changes in the different roles of a physician and possibly other professionals. Intelligent tutoring systems can support student learning by providing individualized feedback and creating personalized learning pathways. Role-plays with an intelligent active agent can enhance students' interaction with computers and activate their sense of responsibility. AI can support personalized learning by intelligent agents, autonomous scoring, and chatbots. AI has an important role to play in supporting simulations, serious games, and the gamification of learning. Learning analytics and educational data mining are two other important applications. Personalized prediction is also an important benefit. AI will supplement the work of educators and can reduce curricular overload by migrating some knowledge to AI algorithms. Routine tasks and responses to routine queries of learners can be provided by AI. AI can support continuing professional education by incorporating longitudinal and innovative formative assessment methods that can help identify knowledge and skill gaps and support learning. The use of AI in curriculum review and assessment has been limited. Data integrity and privacy are important issues to consider. Unconscious bias in the data used to educate AI systems is also possible. Most of the literature is from developed countries and among medical students and residents.
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There is limited data available on the accuracy and consistency of AI in assessment.
“The application of AI in assessment should also be approached with caution because of the limited data available on its accuracy and consistency.”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [60]
These limitations carry significant implications for evaluating students and, consequently, for ensuring their ability to make clinical decisions.
“These limitations carry significant implications for evaluating students and, consequently, for ensuring their ability to make clinical decisions [60].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [60]
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“The approach develops learning through several means of offering support and additional learning opportunities [7–10].”
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- Authors
- Shankar, P. R.
- Affiliations
- IMU University (MY)
- Corresponding author
- Shankar, P. Ravi
- ORCID
- Shankar, P. Ravi
- Venue
- Archives of Medicine and Health Sciences
- Published
- 2022-07
- Volume
- 10
- Issue
- 2
- Pages
- 256-261
- ISSN
- 2321-4848 (ISSN-L)
- OpenAlex
- W4312790428
- Semantic Scholar
- 1d8ab1ddcb377c3c8bb48c6764f26abeea4685a9
- Cited by
- 40
- References
- 40
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 10 · 2023: 5
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Archives of Medicine and Health Sciences (publishedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence in education, health professions education, intelligent tutoring systems, chatbots, serious games, gamification, learning analytics, educational data mining, personalized learning, continuing professional development, formative assessment, data privacy, algorithmic bias, medical students, residents (medical training), developing countries
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