Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)
Copied to clipboard
| Challenge: | Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale. |
| Approach: | They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. |
| Outcome: | The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. |
Similar Papers
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
Copied to clipboard
Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Benchmarking LLMs on Authentic Cases from Medical Journals (2026.findings-acl)
Copied to clipboard
| 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. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
Copied to clipboard
Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| 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. |
LlamaCare: An Instruction Fine-Tuned Large Language Model for Clinical NLP (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models have shown remarkable abilities in generating natural texts . applying LLMs to clinical domain still poses significant challenges . |
| Approach: | They propose a method of instruction fine-tuning for adapting large language models to clinical domains . they generate instructions, inputs, and outputs covering a wide spectrum of clinical services . |
| Outcome: | The proposed method outperforms baseline LLMs on clinical tasks . it requires domain adaptation, task-specific learning, and reliability . |
DischargeSim: A Simulation Benchmark for Educational Doctor–Patient Communication at Discharge (2025.emnlp-main)
Copied to clipboard
| Challenge: | Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. |
| Approach: | They propose a benchmark that evaluates large language models’ ability to act as personalized discharge educators. |
| Outcome: | Experiments with 18 LLMs show that model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. |
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
Copied to clipboard
Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| 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. |
Can AI Relate: Testing Large Language Model Response for Mental Health Support (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are already being piloted for clinical use in hospitals . recent failures of the Tessa chatbot have led to doubts about their reliability in high-stakes settings. |
| Approach: | They propose safety guidelines for the potential deployment of large language models for mental health response. |
| Outcome: | The proposed framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. |
To Err Is Human, How about Medical Large Language Models? Comparing Pre-trained Language Models for Medical Assessment Errors and Reliability (2024.lrec-main)
Copied to clipboard
| Challenge: | a 1999 report found that at least forty thousand deaths are a result of preventable medical errors. |
| Approach: | They test pre-trained language models to characterize their error generation and reliability in medical assessment ability. |
| Outcome: | The results show that pre-trained models can generate errors and perform better than human models. |
A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks. |
| Approach: | They analyze system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement. |
| Outcome: | The findings highlight the future directions in medical reasoning, physical system integration, and training simulations. |