Currently submitted to: JMIR Medical Education
Date Submitted: Jul 3, 2026
Open Peer Review Period: Jul 6, 2026 - Aug 31, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Improving AI Readiness Among Medical Trainees Through an Interdisciplinary Health Datathon: Mixed Methods Evaluation
ABSTRACT
Background:
Artificial intelligence (AI) is transforming healthcare, yet medical trainees often lack formal AI education. Further, clinician resistance is a major barrier to AI integration into practice. Experiential learning approaches such as datathons may offer a scalable pathway to improve AI literacy and acceptance among medical trainees.
Objective:
To evaluate whether participation in a virtual, team-based health AI datathon improves medical AI readiness, technology acceptance, and digital health literacy among trainees.
Methods:
We organized a 3-week virtual datathon themed “Empowering Patients Through AI” through a national trainee-led nonprofit with both academic and industry partners. Teams of trainees analyzed one of four provided public healthcare datasets to develop patient-centered AI solutions. Project submissions were analyzed using reflexive thematic analysis. Pre- and post-event surveys assessed AI readiness, technology acceptance, and digital health literacy using adapted Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and Technology Acceptance Model (TAM) scales. Within-participant differences in composite scores were evaluated using Wilcoxon signed-rank tests. Free-text reflections were analyzed using data-driven consensus clustering of reviewer-extracted codes.
Results:
The program attracted registrants (n=292) from 75 institutions, with 31 teams (n=149 participants) submitting final projects across 10 clinical domains. Six patient empowerment themes were identified among submitted projects, with symptom forecasting (39%), self-management (32%), and health literacy (26%) being most common. Among paired respondents (n=69), medical AI readiness (mean 3.61 to 4.24), AI technology acceptance (3.93 to 4.36), and digital health literacy (3.67 to 4.23) all increased significantly (all P<.001). The largest gains were in identifying AI-appropriate problems, explaining core AI concepts, and using digital tools for self-management. Qualitative analysis of free-text reflections identified seven themes, with teamwork, interdisciplinary collaboration, and motivation appearing most frequently, all associated with strongly positive sentiment expressed by participants.
Conclusions:
Participation in a health AI datathon improved AI readiness and acceptance while generating diverse, patient-centered solutions. Datathons represent a scalable experiential learning model to prepare trainees for effective engagement with AI in healthcare, while creating opportunities for trainees to explore and develop novel health technology solutions.
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