mlsanityData to 14 Oct 2026

MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics

arXiv 2503.02374 ↗First posted 4 Mar 2025cs.CLcited by 8

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Abstract (arXiv)

Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models' grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs' ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 5 of its 31 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

5 of 31 references.

References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  3. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  4. 2023Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Datasetcited by 91
  5. 2018Medical Exam Question Answering with Large-scale Reading Comprehensioncited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2026From RAG to Agentic RAG for Faithful Islamic Question Answeringcited by 9
  2. 2025EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AIcited by 1
  3. 2025Beyond MedQA: Towards Real-world Clinical Decision Making in the Era of LLMscited by 1
  4. 2026JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Modelscited by 1
  5. 2026D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarioscited by 1
  6. 2025PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignmentcited by 0
  7. 2026A Human-Centric Pipeline for Aligning Large Language Models with Chinese Medical Ethicscited by 0
  8. 2026Rare Diseases, Common Dilemmas: LLMs Prioritize Equal Resource Distribution over Patient Benefit in Decision-Makingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies
  2. 2026Ethical Risks in Deploying Large Language Models: An Evaluation of Medical Ethics Jailbreaking
  3. 2025MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agents
  4. 2025MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
  5. 2023MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models
  6. 2025MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine
  7. 2025Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
  8. 2025Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models