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The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) Curriculum: A Machine Learning and
Problem:
Despite the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in health care, a significant knowledge gap remains among clinicians in their ability to evaluate and use AI and ML tools.
Approach:
The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) curriculum was developed to teach fundamental AI and ML concepts to clinical trainees. The curriculum contains 4 modules: (1) Introduction to AI/ML in Healthcare, (2) Epidemiology and Biostatistics in AI/ML, (3) Use of AI/ML to Augment Diagnostic Decisions, and (4) Ethical and Legal Considerations of AI/ML in Healthcare. The curriculum was piloted in May and June 2023 among University of Michigan internal medicine residents and delivered to 2 cohorts of 11 and 12 residents. All learners completed presession and postsession assessments on AI and ML knowledge before and after the curriculum and a retrospective pre-post survey to evaluate familiarity with AI and ML concepts, comfort with AI and ML literature appraisal, and attitudes toward AI and ML use.
Outcomes:
Twenty of 23 learners (87%) completed the presession knowledge assessment before participating in the DATA-MD sessions, and all 23 learners completed the postsession knowledge assessment and retrospective pre-post survey. Median knowledge scores significantly improved for modules 1 to 3 (module 1: 2.5 to 3.0, P = .008; module 2: 1.0 to 2.0, P = .049; module 3: 2.0 to 3.0, P < .001) but not module 4 (2.0 before and after testing; P = .80). Learners reported increased confidence in their abilities to appraise the AI and ML literature and use AI and ML tools in future practice.
Next Steps:
The DATA-MD curriculum pilot demonstrates that a standardized AI and ML curriculum can improve trainees' knowledge and attitudes about clinical AI and ML. Next steps include expansion to learners from different medical specialties, health professions, and academic institutions.
