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The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) Curriculum: A Machine Learning and
Summary
A new curriculum, Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD), improved clinical trainees' knowledge of artificial intelligence (AI) and machine learning (ML) in healthcare. The pilot showed increased confidence in using and evaluating AI/ML tools.
Area of Science:
- Medical Education
- Health Informatics
- Artificial Intelligence in Medicine
Background:
- Clinicians possess a significant knowledge gap regarding artificial intelligence (AI) and machine learning (ML) tools in healthcare.
- The expanding role of AI/ML necessitates improved clinician understanding and evaluation skills.
Purpose of the Study:
- To introduce fundamental AI/ML concepts to clinical trainees.
- To develop and pilot the Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) curriculum.
Main Methods:
- The DATA-MD curriculum consists of 4 modules covering AI/ML fundamentals, epidemiology, diagnostic augmentation, and ethical considerations.
- Piloted with internal medicine residents, the curriculum included pre/post knowledge assessments and surveys.
- Evaluated familiarity, literature appraisal comfort, and attitudes toward AI/ML.
Main Results:
- Significant improvements in knowledge scores were observed for the first three modules (P < .05).
- Module 4 (Ethical and Legal Considerations) showed no significant knowledge improvement (P = .80).
- Learners reported increased confidence in appraising AI/ML literature and using AI/ML tools.
Conclusions:
- The DATA-MD curriculum pilot demonstrates effectiveness in enhancing trainees' AI/ML knowledge and attitudes.
- Future steps include expanding the curriculum to diverse learners and institutions.
