Academic Medicine · 2023 · 34 citations · 27 references
Artificial IntelligenceMedical Ai CompetenciesClinical SpecialtiesAllied Health ProfessionsMultidisciplinary AiBiomedical Artificial IntelligenceMedical Expert SystemDigital HealthClinical InformaticsAi HealthcarePublic HealthDelphi SurveyEssential CompetenciesHealth InformaticsHealth PolicyOutcomes ResearchAi EducationHealth Data ScienceMedical EthicsAi CompetenciesPatient SafetyHealth Profession TrainingMedical Student/educator QuestionnaireMedicineClinical Decision Support SystemArtificial Intelligence Ethics
Abstract Purpose Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. Method An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competencies from the optional ones. Results The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. Conclusions This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.
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