title: MedMobile: A mobile-sized language model with expert-level clinical
    capabilities
summary: 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
paper_authors._id: 6711ce7841ad206aabb63260
paper_authors.name: Krithik Vishwanath
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-18T02:56:57.499Z
paper_authors._id: 66a02bb71bb371390c4ae519
paper_authors.avatarUrl: /avatars/2c4791516aae0b20a9c9ce0542dc966e.svg
paper_authors.fullname: Krithik Vishwanath
paper_authors.type: user
paper_authors.user: KrithikV
paper_authors._id: 6711ce7841ad206aabb63261
paper_authors.name: Jaden Stryker
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:57:32.723Z
paper_authors._id: 6490f001f049c475bdb5178f
paper_authors.avatarUrl: /avatars/97523fda9a37a16282e427e8862b0d84.svg
paper_authors.fullname: Jaden Stryker
paper_authors.type: user
paper_authors.user: JadenStryker
paper_authors._id: 6711ce7841ad206aabb63262
paper_authors.name: Anton Alaykin
paper_authors._id: 6711ce7841ad206aabb63263
paper_authors.name: Daniel Alexander Alber
paper_authors._id: 6711ce7841ad206aabb63264
paper_authors.name: Eric Karl Oermann
paper_title: MedMobile: A mobile-sized language model with expert-level clinical
    capabilities
paper_summary: 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
paper_ai_summary: MedMobile, a 3.8 billion parameter LM optimized for mobile devices, outperforms larger models on MedQA with enhancements from chain of thought, ensembling, and fine-tuning.
paper_ai_keywords: language models phi-3-mini MedMobile MedQA USMLE chain of thought ensembling fine-tuning retrieval augmented generation