LLM-assisted systematic review of large language models in clinical medicine

5/10 importance · ai assisted · medical research · 2026 · validator confidence 60% · research

The problem

The rapid flood of clinical LLM studies (roughly 3.2 papers per day) makes manual curation and review of the evidence base infeasible.

Why it matters

Clinicians and regulators need a clear evidence base to judge LLM usefulness and safety in medicine.

What the AI did

LLMs were used to assist a systematic review, identifying and characterizing 4,609 peer-reviewed clinical LLM studies from January 2022 to September 2025.

Results

4,609 studies identified; only 1,048 used real-world patient data and only 19 were prospective randomized trials; ChatGPT/OpenAI models were 65.7% of evaluated models.

Impact

Evidence supports LLM-assisted mapping of the clinical LLM evidence landscape at scale.

How important is this problem?

5/10 — real but narrower advance

Abstract quantifies the flood of clinical LLM evidence (4,609 studies, ~3.2 papers/day) and scarcity of rigorous trials; meta-research advance aiding clinicians and regulators, narrower than direct care.

stated — the source itself gives scale or impact evidence for this rating.

Scale stated in source: 4,609 studies identified; ~3.2 papers per day

Ratings use a published rubric and are AI-assisted, not measured or peer-reviewed. Method

Sources & verification

Primary source: https://doi.org/10.1038/s41591-026-04229-5
Model / method: not stated in source
Organization: not stated in source
Validator notes: glm-5.3: ok (confidence 60) | Abstract truncated; descriptive counts only, LLM curation accuracy not stated.
Keywords: large language models, systematic review, clinical medicine, ChatGPT, evidence synthesis

A second-stage review judged this entry against the primary source; status “verified”. “Verified” means the evidence supported the claim — it does not mean the underlying result was independently reproduced.

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