Towards evaluating and building versatile large language models for medicine.

In this study, we present MedS-Bench, a comprehensive benchmark to evaluate large language models (LLMs) in clinical contexts, MedS-Bench, spanning 11 high-level clinical tasks. We evaluate nine leading LLMs, e.g., MEDITRON, Llama 3, Mistral, GPT-4, Claude-3.5, etc. and found that most models struggle with these complex tasks. To address these limitations, we developed MedS-Ins, a large-scale instruction-tuning dataset for medicine. MedS-Ins comprises 58 medically oriented language corpora, totaling 5M instances with 19K instructions, across 122 tasks. To demonstrate the dataset's utility, we conducted a proof-of-concept experiment by performing instruction tuning on a lightweight, open-source medical language model. The resulting model, MMedIns-Llama 3, significantly outperformed existing models on various clinical tasks. To promote further advancements, we have made MedS-Ins fully accessible and invite the research community to contribute to its expansion. Additionally, we have launched a dynamic leaderboard for MedS-Bench, to track the development progress of medical LLMs.