Implications of integrating large language models into clinical decision making
Open accessAbstract
Christof & Armoundas explore how large language models (LLMs) can augment clinician-level clinical reasoning across the three pillars-framing the encounter, diagnostic reasoning, and treatment/management-highlighting gains in information synthesis and pattern recognition while underscoring limits that require continuous human judgment and oversight. They advocate a bias-aware, privacy-preserving, and rigorously validated “human-in-the-loop” deployment that safeguards patient agency and clinical accountability, while integrating LLMs into real-world workflows via clear clinician imperatives.
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Modern frontier architectures have already internalized the core corpus of medical literature within their parametric knowledge base.
“This suggests that modern frontier architectures have already internalized the core corpus of medical literature within their parametric knowledge base.[12]”
From Multi-Agent Dynamic Refinement Outperforms Static RAG in Clinical Reasoning for Complex Nephrology Cases (2026) · cites this paper as [12]
Table 2 (Introduction; page unavailable) says LLMs align patient information with vast training datasets of medical knowledge, including textbooks and clinical guidelines. The paper does not establish that modern frontier architectures have internalized the core corpus of medical literature in their parametric knowledge base. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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Cites
Educational Strategies to Promote Clinical Diagnostic Reasoning
Bowen, J. L. (2006) · New England Journal of Medicine · doi:10.1056/nejmra054782 · Cited by 931 · see citing works
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347 · see citing works
Self-citationGoh, E., Gallo, R. J., Strong, E., Weng, Y., Kerman, H. M., Freed, J. A., Cool, J. A., Kanjee, Z., Lane, K. P., Parsons, A. S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03456-y · Cited by 179 · see citing works
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Self-citationPerformance of a large language model on the reasoning tasks of a physician
Brodeur, P. G., Buckley, T. A., Kanjee, Z., Goh, E., Ling, E., Jain, P., Cabral, S., Abdulnour, R.-E. E., Haimovich, A. D., Freed, J. A., et al. (2026) · Science · doi:10.1126/science.adz4433 · Cited by 135 · see citing works
The inclusion of augmented intelligence in medicine: A framework for successful implementation
Bazoukis, G., Hall, J. L., Loscalzo, J., Antman, E. M., Fuster, V., & Armoundas, A. A. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100485 · Cited by 104 · see citing works
Self-citationImpact of Social Determinants of Health on Cardiovascular Disease
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Self-citationThe Science of Precision Prevention
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Self-citationProbabilistic medical predictions of large language models
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Self-citationPatient agency and large language models in worldwide encoding of equity
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Armoundas, A. A., & Singh, J. P. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag019 · Cited by 8 · see citing works
Self-citationYano, Y., Kakizaki, H., Nagasu, H., Kishi, S., Koshida, T., Nihei, Y., Hirano, A., Sugawara, Y., Imaizumi, T., Osakabe, Y., et al. (2026) · doi:10.64898/2026.07.15.26358121
- Authors
- Christof, M., & Armoundas, A. A.
- Affiliations
- University of Rochester (US)
- Broad Institute (US)
- Corresponding author
- Armoundas, Antonis A.
- Venue
- Communications Medicine
- Published
- 2025-11-20
- Volume
- 5
- Issue
- 1
- Pages
- 490-490
- ISSN
- 2730-664X (ISSN-L)
- PMC
- PMC12635097
- OpenAlex
- W4416425606
- Cited by
- 16
- References
- 12
- Citations by year
- 2026: 15 · 2025: 1
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Communications Medicine (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Heart Association (3R21EB026164-02S1, R21EB026164-01, 17UNPG33840017)
- National Institutes of Health (17UNPG33840017)
- National Heart, Lung, and Blood Institute (R21HL137870, R01HL135335, R01HL161008)
- National Institute of Biomedical Imaging and Bioengineering (R21EB026164)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- large language models, clinical decision-making, clinical reasoning, human-in-the-loop, diagnostic reasoning, treatment planning, privacy preservation, patient agency, clinical accountability
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