Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness
Open accessAbstract
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.
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LLMs struggle to generate accurate and up-to-date responses on current topics due to outdated knowledge, lack of domain-specific expertise, or hallucination.
“LLMs struggle to generate accurate and up-to-date responses on current topics, due to outdated knowledge, lack of domain-specific expertise, or hallucination13–17.”
From Leveraging long context in retrieval augmented language models for medical question answering (2025) · cites this paper as [17]
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The doctor will polygraph you now
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Xiao, Y., & li, X. (2026) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-026-09809-z · Cited by 4 · see citing works
Lokker, C., McKibbon, K. A., Afzal, M., Navarro, T., Linkins, L., Haynes, R. B., & Iorio, A. (2024) · Journal of Medical Internet Research · doi:10.2196/58764 · Cited by 4 · see citing works
Waqar, M., Khan, Z. A., Baig, M. H. A., Hsu, C.-C., Haq, I. U., Khan, S., Chang, C.-Y., Chaudhary, N. I., & Raja, M. A. Z. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3974 · Cited by 3 · see citing works
Fairness and inclusion methods for biomedical informatics research
Visweswaran, S., Luo, Y., & Mor Peleg, B. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104713 · Cited by 3 · see citing works
Fang, Y., Zhang, G., Tejada, S. S., Chen, F., Shortliffe, E. H., Patel, V., Mor Peleg, B., & Weng, C. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105013 · Cited by 2 · see citing works
Self-citationEducating health professionals in evidence-based practice in 2026
Innocenti, T., Guyatt, G. H., Cartabellotta, N., Glasziou, P., Ilić, D., & Nunan, D. (2026) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2025-114184 · Cited by 2 · see citing works
Dadashi, A., Gavgani, V. Z., Hajebrahimi, S., & Oskouei, M. M. (2025) · Medical Reference Services Quarterly · doi:10.1080/02763869.2025.2471886 · Cited by 2 · see citing works
Comparative Performance of DeepSeek and ChatGPT-4o in the Chinese Nursing Licensing Exam
Liu, X., Wang, H., Guo, X., Tian, S., & Cao, H. (2026) · Journal of Nursing Education · doi:10.3928/01484834-20250926-03 · Cited by 1 · see citing works
A neural-symbolic AI agent system for biomedical concept mapping
Zhang, G., Fang, Y., Chen, F., Ta, C., Hripcsak, G. M., Ryan, P., Peng, Y., & Weng, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02594-6 · Cited by 1 · see citing works
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The doctor will polygraph you now: ethical concerns with AI for fact-checking patientsPreprint
Anibal, J., Gunkel, J., Awan, S. N., Huth, H. B., Nguyen, H. T. T., Le, T., Bélisle‐Pipon, J., Boyer, M., Hazen, L., Consortium, B. V., et al. (2024) · PubMed · doi:10.48550/arxiv.2408.07896
Online pre-appraised evidence resources for speech-language pathology: a scoping review
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Counterfactual Red Teaming and Dialogical Ethics in Distributed Human–Computer Interaction
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A PRISMA-Aligned Agentic Framework for Medical Systematic Reviews and Evidence SynthesisPreprint
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- Authors
- Zhang, G., Jin, Q., Jered McInerney, D., Chen, Y., Wang, F., Cole, C. L., Yang, Q., Wang, Y., Malin, B. A., Peleg, M., Wallace, B. C., Lu, Z., Weng, C., & Peng, Y.
- Affiliations
- Columbia University (US)
- National Institutes of Health (US)
- University of Pennsylvania (US)
- Weill Cornell Medicine (US)
- Cornell University (US)
- University of Pittsburgh (US)
- Vanderbilt University (US)
- Northeastern University (US)
- Corresponding author
- Weng, Chunhua
- Peng, Yifan
- ORCID
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 153
- Pages
- 104640
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC12478423
- OpenAlex
- W4394688596
- Cited by
- 40
- References
- 49
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 11
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine (R01LM009886, R01LM014344, R01LM014306)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI, large language models, clinical evidence synthesis, trustworthiness, text summarization, medical evidence, evidence-based medicine, model accountability
- MeSH terms
- Artificial Intelligence, Humans, Natural Language Processing, Evidence-Based Medicine, Trust
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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