# LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation

arXiv: https://arxiv.org/abs/2506.04078 · HTML: https://mlsanity.com/paper/2506.04078/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2106.08087/) (2021)
- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [MedCalc-Bench: Evaluating Large Language Models for Medical Calculations](https://mlsanity.com/paper/2406.12036/) (2024)
- [PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain](https://mlsanity.com/paper/2310.14151/) (2023)
- [MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain](https://mlsanity.com/paper/2106.01491/) (2021)
- [WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation](https://mlsanity.com/paper/2410.12722/) (2024)
- [MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine](https://mlsanity.com/paper/2305.07340/) (2023)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)

## Built on by (most cited first, 30 of 34)

- [Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning](https://mlsanity.com/paper/2508.16949/) (2025)
- [HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats](https://mlsanity.com/paper/2604.27470/) (2026)
- [EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation](https://mlsanity.com/paper/2605.29847/) (2026)
- [Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL](https://mlsanity.com/paper/2608.11669/) (2026)
- [CriPO: Enhancing Rubric-based RL via Self-Distillation](https://mlsanity.com/paper/2607.18082/) (2026)
- [From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes](https://mlsanity.com/paper/2507.17717/) (2025)
- [Beyond Classification Accuracy: Neural-MedBench and the Need for Deeper Reasoning Benchmarks](https://mlsanity.com/paper/2509.22258/) (2025)
- [Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs](https://mlsanity.com/paper/2603.20046/) (2026)
- [EH-Benchmark Ophthalmic Hallucination Benchmark and Agent-Driven Top-Down Traceable Reasoning Workflow](https://mlsanity.com/paper/2507.22929/) (2025)
- [LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models](https://mlsanity.com/paper/2508.05452/) (2025)
- [Quark Medical Alignment: A Holistic Multi-Dimensional Alignment and Collaborative Optimization Paradigm](https://mlsanity.com/paper/2602.11661/) (2026)
- [EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs](https://mlsanity.com/paper/2605.30637/) (2026)
- [Evaluating Large Language Model Raters for German Open-Response Clinical Questions: A Physician-Annotated Benchmark Study of Agreement, Evaluator Bias, and Abstention](https://mlsanity.com/paper/2607.01103/) (2026)
- [DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space](https://mlsanity.com/paper/2607.25675/) (2026)
- [SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning](https://mlsanity.com/paper/2607.26873/) (2026)
- [RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning](https://mlsanity.com/paper/2608.09123/) (2026)
- [Can LLMs Threaten Human Survival? Benchmarking Potential Existential Threats from LLMs via Prefix Completion](https://mlsanity.com/paper/2511.19171/) (2025)
- [MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters](https://mlsanity.com/paper/2607.08257/) (2026)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs](https://mlsanity.com/paper/2603.02097/) (2026)
- [Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarking](https://mlsanity.com/paper/2603.23506/) (2026)
- [DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models](https://mlsanity.com/paper/2603.23514/) (2026)
- [AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment](https://mlsanity.com/paper/2605.11398/) (2026)
- [TreeProbe : A Tibetan Medicine Benchmark for Cultural Bias in LLMs](https://mlsanity.com/paper/2608.00640/) (2026)
- [RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Care](https://mlsanity.com/paper/2608.04514/) (2026)
- [A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports](https://mlsanity.com/paper/2608.22713/) (2026)
- [SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence](https://mlsanity.com/paper/2608.29582/) (2026)
- [PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning](https://mlsanity.com/paper/2609.04598/) (2026)
- [DischargeBench: Pre-Deployment Evaluation of LLM Discharge Educators through Persona-Grounded Patient Simulation](https://mlsanity.com/paper/2609.20827/) (2026)

## Abstract (arXiv)

Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three main types: medical exam-based, comprehensive medical, and specialized assessments. However, these benchmarks have limitations in question design (mostly multiple-choice), data sources (often not derived from real clinical scenarios), and evaluation methods (poor assessment of complex reasoning). To address these issues, we present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We also design an automated evaluation pipeline, incorporating expert-developed checklists into our LLM-as-Judge framework. Furthermore, our methodology validates machine scoring through human-machine agreement analysis, dynamically refining checklists and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective deployment of LLMs in medical domains. The dataset is released in https://github.com/llmeval/LLMEval-Med.