Challenge: Current medical benchmarks have limitations in question design, data sources and evaluation methods.
Approach: They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records .
Outcome: The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.

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Benchmarking LLMs on Authentic Cases from Medical Journals (2026.findings-acl)

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Challenge: Existing medical benchmarks suffer from performance saturation due to medical exam questions.
Approach: They evaluate the performance of over 20 open-source and proprietary large language models and benchmark them against human medical experts.
Outcome: The new benchmark is based on authentic clinical cases sourced from medical journals and implements rigorous human review process to ensure the quality and reliability of the benchmark.
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)

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Challenge: Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments.
Approach: They propose a new step-by-step evaluation pipeline that assesses formula selection, entity extraction, and arithmetic computation.
Outcome: The proposed method improves the accuracy of large language models on medical benchmarks from 16.35% to 53.19%.
Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models (2026.acl-long)

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Challenge: Large language models (LLMs) are proving significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities.
Approach: They propose a framework that deconstructs benchmark development into five stages from design to governance and provides a checklist of 46 medically-tailored criteria.
Outcome: The framework deconstructs benchmark development into five stages from design to governance and provides a comprehensive checklist of 46 medically-tailored criteria.
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios (2024.emnlp-main)

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Challenge: Chinese medical large language models (LLMs) are underperforming on this benchmark, especially where medical reasoning and factual consistency are vital.
Approach: They propose a benchmark with 14 expert-guided clinical scenarios to assess the medical ability of large language models across 7 pivot dimensions.
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MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings (2026.eacl-industry)

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Challenge: Existing risk evaluations focused on general safety benchmarks, resulting in role-dependent vulnerabilities in real-world medical and clinical deployments.
Approach: They propose a patient-oriented dataset called PatientSafetyBench that evaluates a variety of open- and closed-source LLMs.
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EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions (2025.findings-acl)

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Challenge: Recent advances in Large Language Models (LLMs) show their potential in accurately answering biomedical questions, yet current healthcare benchmarks primarily assess knowledge mastered by medical doctors, neglecting other essential professions.
Approach: They evaluated 17 LLMs including proprietary and open-source models and found they struggled with specialized fields and alternative medicine.
Outcome: The examinations for medical PErsonnel in Chinese (EMPEC) features 157,803 exam questions across 124 subjects and 20 healthcare professions.
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes (2025.findings-acl)

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Challenge: Several studies have shown that large language models can answer medical questions correctly, outperforming the average human score in some medical exams.
Approach: They introduce MEDEC, the first publicly available benchmark for medical error detection and correction in clinical notes.
Outcome: The proposed model outperforms medical doctors in errors detection and correction tasks.
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

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Challenge: Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach: They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome: The proposed model outperforms human experts in multiple medical tasks.
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)

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Challenge: Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time.
Approach: They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time.
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Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning (2026.findings-eacl)

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Challenge: Large language models (LLMs) have shown remarkable progress in reasoning across multiple domains, but it remains unclear whether their abilities reflect genuine reasoning or sophisticated pattern matching.
Approach: They conduct one of the largest evaluations to date, assessing 77 LLMs . they select three medical question answering (QA) benchmarks targeting reasoning processes .
Outcome: The results highlight the need to improve specific reasoning strategies to better reflect medical decision-making.

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