Papers › MedMobile: A mobile-sized language model with expert-level clinical capabilities
MedMobile: A mobile-sized language model with expert-level clinical capabilities
Krithik Vishwanath, Jaden Stryker, Anton Alaykin, Daniel Alexander Alber, Eric Karl Oermann
Language models (LMs) have demonstrated expert-level reasoning and recall abilities in medicine. However, computational costs and privacy concerns are mounting barriers to wide-scale implementation. We introduce a parsimonious adaptation of phi-3-mini, MedMobile, a 3.8 billion parameter LM capable of running on a mobile device, for medical applications. We demonstrate that MedMobile scores 75.7% on the MedQA (USMLE), surpassing the passing mark for physicians (~60%), and approaching the scores of models 100 times its size. We subsequently perform a careful set of ablations, and demonstrate that chain of thought, ensembling, and fine-tuning lead to the greatest performance gains, while unexpectedly retrieval augmented generation fails to demonstrate significant improvements
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | MedMCQA Dev | MedMobile (3.8B) | Accuarcy | 63.2 | #1 of 1 | Archive leaderboard | report |
| Question Answering | MedQA | MedMobile (3.8B) | Accuracy | 75.7 | #6 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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