ArticleNature health2026
Safety of a large language model-based clinical decision support system in African primary healthcare.
Article in Nature health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial.Nature medicine · 2026Trial
- Large Language Models in Sensor-Driven Control Systems: Architectures, Challenges, and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives.Scientific reports · 2026Article
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Benchmarking large language models and clinicians using locally generated primary healthcare vignettes in Kenya.BMJ digital health & AI · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- From model failure to system harm: operationalizing a sociotechnical pathway for healthcare AI safety.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Here we conducted a retrospective evaluation of an electronic medical record-embedded large language model clinical decision support system deployed across 16 primary care clinics in Kenya, between July and September 2024. A panel of trained physicians reviewed 1,469 records. Hallucinations were uncommon, occurring in 50 encounters (3.4%, 95% confidence interval (CI) 2.5-4.5), and most often involved misexpanded acronyms or drug names. Clinical management guidance aligned with local guidelines in almost all cases (1,455; 99%, 95% CI 98.4-99.5). Despite this, clinicians did not modify documentation in 917 encounters (62%, 95% CI 59.9-64.9). Safety assessments identified actively harmful recommendations from the large language model in 115 encounters (7.8%, 95% CI 6.5-9.3), with 67 such recommendations appearing in the final documentation. Conversely, risk present in the clinician's initial notes was fully mitigated in 118 encounters (8.0%, 95% CI 6.7-9.5 overall; 12.1%, 95% CI 9.5-15.2 of amended cases). Overall, the tool showed strong potential to support quality improvement, but the asymmetric adoption of harmful versus beneficial outputs underscores the need for usability optimization, local guardrails and prospective trials to confirm patient-level benefit.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.