Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge
Open accessAbstract
Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize relevant details to their practice. To reduce cognitive load on providers, generative AI with Large Language Models have emerged to automatically summarize patient records into clear, actionable insights and offload the cognitive burden for providers. However, LLM summaries need to be precise and free from errors, making evaluations on the quality of the summaries necessary. While human experts are the gold standard for evaluations, their involvement is time-consuming and costly. Therefore, we introduce and validate an automated method for evaluating real-world EHR multi-document summaries using an LLM as the evaluator, referred to as LLM-as-a-Judge. Benchmarking against the validated Provider Documentation Summarization Quality Instrument (PDSQI)-9 for human evaluation, our LLM-as-a-Judge framework demonstrated strong inter-rater reliability with human evaluators. GPT-o3-mini achieved the highest intraclass correlation coefficient of 0.818 (95% CI 0.772, 0.854), with a median score difference of 0 from human evaluators, and completes evaluations in just 22 seconds. Overall, the reasoning models excelled in inter-rater reliability, particularly in evaluations that require advanced reasoning and domain expertise, outperforming non-reasoning models, those trained on the task, and multi-agent workflows. Cross-task validation on the Problem Summarization task similarly confirmed high reliability. By automating high-quality evaluations, medical LLM-as-a-Judge offers a scalable, efficient solution to rapidly identify accurate and safe AI-generated summaries in healthcare settings.
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Only a few studies report high consistency between LLM-as-a-judge and expert outputs that overall met the researchers' needs.
“However, only a few studies report high consistency between LLM-as-a-judge and expert outputs that overall met the researchers’ needs [22,23].”
From Evaluating Medical Text Summaries Using Automatic Evaluation Metrics and LLM-as-a-Judge Approach: A Pilot Study (2025) · cites this paper as [23]
The Results (Single LLM Framework; page unavailable) report high agreement between one LLM-as-a-Judge configuration and human evaluators (ICC 0.818), and the Discussion describes strong inter-rater reliability for clinical summaries. The Introduction says performance data are limited in the medical domain, but the paper does not establish that only a few studies overall achieved high consistency or that their results met their researchers’ needs. (LLM, conf=0.84)
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Cites
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · see citing works
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 23,433 · see citing works
Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618 · see citing works
Computing inter‐rater reliability and its variance in the presence of high agreement
Gwet, K. L. (2008) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711006x126600 · Cited by 2,167 · see citing works
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888 · see citing works
Statistical Methods for Research Workers
Fisher, R. A. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_6 · Cited by 1,464 · see citing works
Poissant, L. (2005) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1700 · Cited by 901 · see citing works
Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review
Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., Fries, J. A., Wornow, M., Swaminathan, A., Lehmann, L. S., et al. (2025) · JAMA · doi:10.1001/jama.2024.21700 · Cited by 662 · see citing works
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.12948 · Cited by 538 · see citing works
Self-citationQLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14314 · Cited by 507 · see citing works
Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424 · see citing works
Ambient artificial intelligence scribes to alleviate the burden of clinical documentation.
Tierney, A. A., Gayre, G., Hoberman, B., Mattern, B., Ballesca, M., Kipnis, P., Liu, V., & Lee, K. (2024) · NEJM Catalyst · doi:10.1056/cat.23.0404 · Cited by 400 · see citing works
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2023) · arXiv · doi:10.48550/arxiv.2305.19118 · Cited by 320 · see citing works
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.992 · Cited by 274 · see citing works
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Li, D., Jiang, B., Huang, L., Beigi, A., Zhao, C., Tan, Z., Bhattacharjee, A., Jiang, Y., Chen, C., Wu, T., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.16594 · Cited by 271 · see citing works
Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages
Garcia, P., Ma, S. P., Shah, S. J., Smith, M., Jeong, Y., Devon-Sand, A., Tai-Seale, M., Takazawa, K., Clutter, D., Vogt, K., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.3201 · Cited by 257 · see citing works
A Survey of Evaluation Metrics Used for NLG Systems
Sai, A. B., Mohankumar, A. K., & Khapra, M. M. (2023) · ACM Computing Surveys · doi:10.1145/3485766 · Cited by 235 · see citing works
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D. (2022) · arXiv · doi:10.48550/arxiv.2210.17323 · Cited by 197 · see citing works
Assessing electronic note quality using the physician documentation quality instrument (PDQI-9)
Bakken, S., Wrenn, J. O., Siegler, E. L., & Stetson, P. D. (2012) · Applied Clinical Informatics · doi:10.4338/aci-2011-11-ra-0070 · Cited by 182 · see citing works
AI-Generated Draft Replies Integrated Into Health Records and Physicians’ Electronic Communication
Tai-Seale, M., Baxter, S. L., Vaida, F., Walker, A., Sitapati, A. M., Osborne, C., Diaz, J., Desai, N., Webb, S., Polston, G., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.6565 · Cited by 158 · see citing works
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2023) · arXiv · doi:10.48550/arxiv.2307.15343 · Cited by 152 · see citing works
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023) · arXiv · doi:10.48550/arxiv.2305.14325 · Cited by 96 · see citing works
Embi, P. J., Weir, C., Efthimiadis, E. N., Thielke, S. M., Hedeen, A. N., & Hammond, K. W. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000946 · Cited by 74 · see citing works
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69 · see citing works
Improving diagnostic accuracy using EHR in emergency departments: A simulation-based study
Ben‐Assuli, O., Sagi, D., Leshno, M., Ironi, A., & Ziv, A. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.03.004 · Cited by 61 · see citing works
Croxford, E., Gao, Y., Pellegrino, N., Wong, K., Wills, G. J., First, E., Schnier, M., Burton, K. A., Ebby, C. G., Gorski, J. K., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf068 · Cited by 40 · see citing works
Self-citationLLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
Li, H., Dong, Q., Chen, J., Su, H., Zhou, Y., Ai, Q., Ye, Z., & Liu, Y. (2024) · arXiv · doi:10.48550/arxiv.2412.05579 · Cited by 40 · see citing works
Long-context LLMs Struggle with Long In-context Learning
Li, T., Zhang, G., Do, Q. D., Yue, X., & Chen, W. (2024) · arXiv · doi:10.48550/arxiv.2404.02060 · Cited by 31 · see citing works
Impact of a problem-oriented view on clinical data retrieval
Semanik, M. G., Kleinschmidt, P. C., Wright, A. T., Willett, D. L., Dean, S. M., Saleh, S. N., Co, Z., Sampene, E., & Buchanan, J. R. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa332 · Cited by 30 · see citing works
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2023) · arXiv · doi:10.48550/arxiv.2310.05694 · Cited by 27 · see citing works
Mixture-of-Agents Enhances Large Language Model Capabilities
Wang, J., Wang, J., Athiwaratkun, B., Zhang, C., & Zou, J. (2024) · arXiv · doi:10.48550/arxiv.2406.04692 · Cited by 25 · see citing works
Gao, Y., Dligach, D., Miller, T. A., & Afshar, M. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.43 · Cited by 17 · see citing works
Self-citationMagentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Fourney, A., Bansal, G., Mozannar, H., Tan, C., Salinas, E., Erkang, Zhu, Niedtner, F., Proebsting, G., Bassman, G., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.04468 · Cited by 17 · see citing works
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Dubois, Y., Galambosi, B., Liang, P., & Hashimoto, T. B. (2024) · arXiv · doi:10.48550/arxiv.2404.04475 · Cited by 17 · see citing works
Afshar, M., Gao, Y., Wills, G. J., Wang, J., Churpek, M. M., Westenberger, C. J., Kunstman, D., Gordon, J. E., Goswami, C., Liao, F., et al. (2024) · PubMed Central · doi:10.1093/jamiaopen/ooae080 · Cited by 12 · see citing works
Self-citationPatterson, B. W., Hekman, D. J., Liao, F., Hamedani, A. G., Shah, M. N., & Afshar, M. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae039 · Cited by 11 · see citing works
Self-citationAn Investigation of Evaluation Metrics for Automated Medical Note Generation
Abacha, A. B., Yim, W.-W., Michalopoulos, G., & Lin, T. (2023) · arXiv · doi:10.48550/arxiv.2305.17364 · Cited by 11 · see citing works
Croxford, E., Gao, Y., Patterson, B. W., To, D., Tesch, S., Dligach, D., Mayampurath, A. M., Churpek, M. M., & Afshar, M. (2024) · medRxiv · doi:10.1101/2024.03.20.24304620 · Cited by 8 · see citing works
Self-citationCroxford, E., Gao, Y., Pellegrino, N., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.18170
Self-citationGu, J., Jiang, X., Shi, Z., Tan, H., Zhai, X., Xu, C., Li, W., Shen, Y., Ma, S., Liu, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.15594
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Flagged first, then by citations
Research works printed but not linked: 2
- [15]Xiong W. Effective Long-Context Scaling of Foundation Models en. in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (Association for Computational Linguistics, Mexico City, Mexico, 2024), 4643– 4663. https://aclanthology.org/2024.naacl-long.260.
- [41]Akiba T., Sano S., Yanase T., Ohta T. & Koyama M. Optuna: A Next-Generation Hyperparameter Optimization Framework in The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019), 2623– 2631.
Cited by
Sorted by citations
Generative artificial intelligence in medicine
Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03983-2 · Cited by 119 · see citing works
Holistic evaluation of large language models for medical tasks with MedHELM
Bedi, S., Cui, H., Fuentes, M., Unell, A., Wornow, M., Banda, J. M., Kotecha, N., Keyes, T., Mai, Y., Oez, M., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04151-2 · Cited by 59 · see citing works
Medical reasoning in LLMs: an in-depth analysis of DeepSeek R1
Moëll, B., Aronsson, F. S., & Akbar, S. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1616145 · Cited by 42 · see citing works
Wang, S., Tang, Z., Yang, H., Gong, Q., Gu, T., Ma, H., Wang, Y., Sun, W., Lian, Z., Mao, K., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02277-8 · Cited by 33 · see citing works
Self-citationWhy and How to Monitor Deployed AI Systems in Health Care
Keyes, T., Callahan, A., Pandya, A. S., Ambers, N., Banda, J. M., Fuentes, M., Lugtu, C., Masariya, P., Nallan, S., O’Brien, C., et al. (2026) · NEJM Catalyst · doi:10.1056/cat.25.0372 · Cited by 10 · see citing works
Artificial Authority: The Promise and Perils of LLM Judges in Healthcare
Genovese, A., Hegstrom, L., Prabha, S., Gomez-Cabello, C. A., Haider, S. A., Collaço, B. G., Wood, N. G., & Forte, A. J. (2026) · Bioengineering · doi:10.3390/bioengineering13010108 · Cited by 9 · see citing works
Vasilev, Y., Raznitsyna, I., Pamova, A., Burtsev, T., Bobrovskaya, T., Kosov, P., Vladzymyrskyy, A., Omelyanskaya, O., & Arzamasov, K. (2025) · Diagnostics · doi:10.3390/diagnostics16010003 · Cited by 5 · see citing works
“However, only a few studies report high consistency between LLM-as-a-judge and expert outputs that overall met the researchers’ needs [22,23].”
Automating evaluation of LLM-generated responses to patient questions about rare diseases
Zhao, M., Oh, I. Y., Gupta, A., Cohen‐Cutler, S., Harmoney, K. M., Lai, A. M., & Sisk, B. A. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag054 · Cited by 4 · see citing works
Khoruzhaya, A. N., Varyukhina, M. D., Арсеньевич, Е. Р., Blokhin, I. A., Reshetnikov, R. V., Kodenko, M. R., Pamova, A. P., Burtsev, T., Arzamasov, K. M., Omelyanskaya, O. V., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1761601 · Cited by 2 · see citing works
Peters, S. G., Boyum, J. P., Legler, S. R., Heise, K. J., Griffin, A., & Heaton, H. A. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag076 · Cited by 2 · see citing works
Byun, H., Lee, D., Jung, M., & Jang, B. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02440-y · Cited by 3 · see citing works
A Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Framework
Li, C., Akhtar, Z., Kwak, M., Ji, Y., Zhang, H., Obi, T., Ren, Y., Wu, X., Sivarajkumar, S., Lehmann, H. P., et al. (2026) · arXiv · doi:10.48550/arxiv.2604.25933 · Cited by 1 · see citing works
Gazzo, A. M., Berardelli, S., Biancospino, M., Cuollo, L., Zotti, F. D., Ferraro, E., Marra, A., Tartarotti, E., & Magni, P. (2026) · Genome biology · doi:10.1186/s13059-026-04237-1 · Cited by 1 · see citing works
Yao, Z., Chafekar, T., Wang, J., Han, S., Ouyang, F., Qian, J., Li, L., & Yu, H. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i46.41305
Synthetic Validation of Pediatric Trust Instruments Using Persona-Driven Large Language Models
Boone, E., Loban, K., Guadagno, E., & Poenaru, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30813-9_9
Борисов, А. А., Burtsev, T., Kosov, P., Bobrovskaya, T. V., Vasilev, Y. A., Vladzymyrskyy, A. V., Omelyanskaya, O. V., Pamova, A. P., & Arzamasov, K. M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1768005
Stupnicki, A., Mendes, B., Reinstein, M., Ong, A. Y., Malem, A. H., Keane, P. A., & Thirunavukarasu, A. J. (2026) · Research Square · doi:10.21203/rs.3.rs-9383173/v1
Retrieval-augmented generation for medication safety: A case study using drug package inserts
Sun, Y., Wang, W., Cheng, W., Li, R., Liu, X., Pei, Q., Tang, J., & Yang, G. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106599
Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?
Rouillard, A., Mundia, S., Camara, L., Dangor, Z., Gramanie, M. C., Kalla, I., Madhi, S. A., Morar, K., Ncube, M. T., Saloojee, H., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30710-1_44
FHIRBench: Benchmarking FHIR Clinical Data Serialization Strategies for Large Language ModelsPreprint
Chong, J. (2026) · medRxiv · doi:10.64898/2026.07.14.26358020
Butler, J. M., Jones, M., Balbin, C. A., Taber, P. A., Fiol, G. D., Dalrymple, R., & Kawamoto, K. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag091
Agha, A., Jalil, M. N., Anwar, E., & Bakoush, O. (2026) · JMIR Research Protocols · doi:10.2196/103220
Boone, E., Loban, K., Daskalo, C., Rajasegaran, S., Guadagno, E., Haggerty, J. L., Lambert, S., & Poenaru, D. (2026) · JMIR Formative Research · doi:10.2196/96901
Szell, A., Yu, Y., Oeding, J. F., Oettl, F. C., Piasecki, A., Dalla, K., Siöland, T., af Segerstad, M. H., Olsen, F., Larsson, P., et al. (2026) · Journal of Experimental Orthopaedics · doi:10.1002/jeo2.70922
Cornelisse, E., Kousa, D., Menz, B. D., Murphy-Reuter, B., Shahnam, A., Chin-Yee, B. H., Calabro, R., Li, L. X., Ramsey, I., Abuhelwa, A. Y., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/100531 · Cited by 1 · see citing works
Jo, E., Lee, Y., Cho, E., Kim, S., Park, E., Ryu, H., Joo, H. J., & Song, S. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02483-1
- Authors
- Croxford, E., Gao, Y., First, E., Pellegrino, N., Schnier, M., Caskey, J., Oguss, M., Wills, G., Chen, G., Dligach, D., Churpek, M. M., Mayampurath, A., Liao, F., Goswami, C., Wong, K. K., Patterson, B. W., & Afshar, M.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Colorado Anschutz (US)
- Epic Systems (United States) (US)
- University of Wisconsin Health (US)
- Corresponding author
- Afshar, Majid
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2025-04-22
- PMC
- PMC12045442
- OpenAlex
- W4409709519
- Cited by
- 25
- References
- 22
- Citations by year
- 2026: 21 · 2025: 4
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- U.S. National Library of Medicine (T15LM007359, R01LM012973, R00LM014308)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- LLM-as-a-judge, clinical summarization, electronic health records, multi-document summarization, inter-rater reliability, large language models, GPT-3.5
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