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Impact of an Ambient AI Scribe on Medical Student Objective Structured Clinical Examination Notes: Nonrandomized
Jaideep S Talwalkar1, Donald S Wright2, Lee H Schwamm3
1Departments of Internal Medicine and Pediatrics, School of Medicine, Yale University, New Haven, CT, United States.
JMIR Medical Education
|May 13, 2026
Summary
Artificial intelligence (AI) scribes had minimal impact on medical student note writing quality. While AI notes were concise, they sometimes omitted details, with students discerningly balancing AI
Area of Science:
- Medical education technology
- Clinical documentation improvement
- Artificial intelligence in healthcare
Background:
- Ambient artificial intelligence (AI) scribes are increasingly used in clinical settings.
- The educational impact of AI scribes on medical student note writing remains underexplored.
Purpose of the Study:
- To evaluate the effect of an AI scribe on pre-clerkship medical student clinical note composition.
Main Methods:
- Prospective, non-randomized pre/post design study involving first-year medical students (n=104).
- Students submitted "human-only" notes, followed by AI-generated notes from recorded audio.
- A subgroup (n=47) created "hybrid" notes incorporating AI suggestions, then completed a survey; all notes were rated by blinded senior medical students using QNOTE criteria.
Main Results:
- Human-only notes generally received high scores (81.3-100).
- Hybrid notes showed a slight decline in Chief Complaint scores (P=.05), with AI notes omitting symptom duration in 17% of cases.
- Students found AI notes more concise but noted omissions; 21% worried about reduced learning of note-writing skills.
Conclusions:
- AI scribe interaction had little impact on the overall quality of hybrid notes for students already writing acceptable human-only notes.
- Chief Complaint score decline was linked to AI conciseness and omissions; students demonstrated discernment in using AI-generated content.
- Future research should examine longitudinal AI scribe use in standard clinical workflows, considering potential benefits for lower-performing students with necessary educational safeguards.
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