Towards accurate differential diagnosis with large language models
Open accessAbstract
A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process 1 . Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: assistance from search engines and standard medical resources; or assistance from AMIE in addition to these tools. All clinicians provided a baseline, unassisted differential diagnosis prior to using the respective assistive tools. AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% versus 33.6%, P = 0.04). Comparing the two assisted study arms, the differential diagnosis quality score was higher for clinicians assisted by AMIE (top-10 accuracy 51.7%) compared with clinicians without its assistance (36.1%; McNemar’s test: 45.7, P < 0.01) and clinicians with search (44.4%; McNemar’s test: 4.75, P = 0.03). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without assistance from AMIE. Our study suggests that AMIE has potential to improve clinicians’ diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients’ access to specialist-level expertise.
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Previous health-related benchmarks graded short responses, extracted fixed entities from responses, or used weakly correlated natural language generation metrics.
“Previous health-related benchmarks graded short responses, extracted fixed entities from responses, or used weakly correlated natural language generation metrics (Jain et al., 2021; Fleming et al., 2024; McDuff et al., 2025).”
From HealthBench: Evaluating Large Language Models Towards Improved Human Health (2025) · cites this paper as McDuff et al., 2025
The Main section describes earlier standardized, multiple-choice medical benchmarks, supporting the claim that some prior evaluations graded constrained answers. The Methods section describes extracting diagnoses from this paper’s own responses, not a practice it attributes to previous benchmarks. The copy does not say that previous benchmarks used natural-language generation metrics that correlated weakly with quality; no page numbers are provided in the copy. (LLM, conf=0.82)
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- Authors
- McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., Hou, L., Cheng, Y., Liu, Y., Mahdavi, S. S., Prakash, S., Pathak, A., Semturs, C., Patel, S., Webster, D. R., Dominowska, E., Gottweis, J., Barral, J., Chou, K., Corrado, G. S., Matias, Y., Sunshine, J., Karthikesalingam, A., & Natarajan, V.
- Affiliations
- Google (United States) (US)
- Google (Switzerland) (CH)
- Google (United Kingdom) (GB)
- Corresponding author
- McDuff, Daniel
- Schaekermann, Mike
- Karthikesalingam, Alan
- Natarajan, Vivek
- ORCID
- McDuff, Daniel
- Schaekermann, Mike
- Tu, Tao
- Palepu, Anil
- Wang, Amy
- Singhal, Karan
- Azizi, Shekoofeh
- Cheng, Yong
- Liu, Yun
- Mahdavi, S. Sara
- Prakash, Sushant
- Pathak, Anupam
- Semturs, Christopher
- Patel, Shwetak
- Webster, Dale R.
- Dominowska, Ewa
- Gottweis, Juraj
- Barral, Joelle
- Chou, Katherine
- Corrado, Greg S.
- Matias, Yossi
- Sunshine, Jake
- Karthikesalingam, Alan
- Natarajan, Vivek
- Venue
- Nature
- Published
- 2025-04-09
- Volume
- 642
- Issue
- 8067
- Pages
- 451-457
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC12158753
- OpenAlex
- W4409283616
- Cited by
- 311
- References
- 34
- Citations by year
- 2026: 172 · 2025: 108 · 2024: 31
- FWCI
- 67.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Massachusetts Medical Society
- DeepMind
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- differential diagnosis, large language models, diagnostic reasoning, clinical decision support, clinician performance, AI-assisted diagnosis, medical knowledge retrieval
- MeSH terms
- Case Reports as Topic, Large Language Models, Artificial Intelligence, Diagnosis, Differential, Humans, Language
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.
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