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Med42-v2: A Suite of Clinical LLMs

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arxiv 2408.06142 v1 pith:L37KPPPF submitted 2024-08-12 cs.CL cs.AI

Clément Christophe , Praveen K Kanithi , Tathagata Raha , Shadab Khan , Marco AF Pimentel This is my paper · ORCID
classification cs.CLcs.AI
keywords clinicalmodelsmed42-v2llmsgenerichttpshuggingfacellama3
verification ladder T0 review T1 audit T2 compute T3 formal
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Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3 architecture and fine-tuned using specialized clinical data. They underwent multi-stage preference alignment to effectively respond to natural prompts. While generic models are often preference-aligned to avoid answering clinical queries as a precaution, Med42-v2 is specifically trained to overcome this limitation, enabling its use in clinical settings. Med42-v2 models demonstrate superior performance compared to the original Llama3 models in both 8B and 70B parameter configurations and GPT-4 across various medical benchmarks. These LLMs are developed to understand clinical queries, perform reasoning tasks, and provide valuable assistance in clinical environments. The models are now publicly available at \href{https://huggingface.co/m42-health}{https://huggingface.co/m42-health}.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

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    MedJudgeRAG fine-tunes a medical MCQA model to emit per-option evidence verdicts and choose grounded, elimination, or parametric reasoning, improving over vanilla RAG by up to 17 accuracy points.

  2. Auditing Evidence Use in Medical LLM Diagnosis

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Behavioral auditing of five medical LLMs shows most mined evidence interactions are clinically plausible, while adjudicated shortcut-like failures concentrate in negated or absent findings and clinically local evidence.

  3. Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry

    cs.CL 2026-05 conditional novelty 6.0 of 10

    A new benchmark of 1,048 psychiatric notes shows LLMs frequently diagnose too early under incomplete evidence, and safety prompting only trades premature diagnoses for excessive abstention.

  4. HIVMedQA: Benchmarking large language models for HIV medical decision support

    cs.CL 2025-07 conditional novelty 6.0 of 10

    The HIVMedQA benchmark finds that Gemini 2.5 Pro leads on most clinical reasoning dimensions, medical fine-tuning does not guarantee gains, and LLM judges are more informative than lexical overlap.

  5. Diagnosing our datasets: How does my language model learn clinical information?

    cs.CL 2025-05 conditional novelty 6.0 of 10

    The frequency of clinical jargon in pretraining corpora predicts how well open-source LLMs interpret that jargon, but hospital notes use abbreviations that appear only rarely online.

  6. C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning

    cs.AI 2026-05 unverdicted novelty 5.0 of 10

    C-MIG uses multi-view information gain from retrieved documents and refinements to supervise RAG-RL for clinical diagnosis, claiming top performance on four medical benchmarks.

  7. Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation

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    Quantizing LLMs to 4 or 8 bits cuts GPU memory by up to 75% with generally small performance changes across eight biomedical NLP benchmarks.

  8. Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A new three-level medical benchmark shows LLM accuracy falls sharply from factual recall (up to 78%) to full clinical diagnosis (max 19%), with larger models and inference-time scaling helping most at intermediate levels.

  9. Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models

    cs.CL 2025-05 conditional novelty 5.0 of 10

    LLMs infer drug use from alcohol or smoking mentions in clinical notes, producing gender-skewed false positives that prompting only partially corrects.

  10. Medicine on the Edge: Comparative Performance Analysis of On-Device LLMs for Clinical Reasoning

    cs.CL 2025-02 conditional novelty 5.0 of 10

    On-device LLMs reach about half the AMEGA clinical-reasoning score of large cloud models, with Med42 and Aloe highest (about 490/1000) and Phi-3 Mini the best accuracy-per-memory trade-off.

  11. Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning

    cs.CV 2025-06 conditional novelty 4.0 of 10

    Sampling multiple VLM-generated visual descriptions and letting a text-only LLM vote on the diagnosis improves zero-shot medical image classification on three MedMNIST datasets.

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