Trial reportCell reports. Medicine2025
The effectiveness of large language models in medical AI research for physicians: A randomized controlled trial.
Trial report in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06015178 (A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers), which is not on this map. Cited by 4 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.
A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers
Who cites it
4 citing papers in PubMed.
- Evaluating Large Language Models for Automated Evidence Synthesis in Neuroimaging AI: A Multi-Model Benchmark.Journal of clinical medicine · 2026Article
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Evaluating large language models for diabetic retinopathy multiple-choice question generation in clinical ophthalmic education.Frontiers in medicine · 2026Article
- Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physicians offer invaluable clinical insights, but their involvement in medical AI research is hindered by limited technical expertise. We conduct a superiority, open-label, randomized controlled trial involving 64 junior ophthalmologists to undertake a 2-week project on "automated cataract identification" under minimal engineering assistance, with (intervention, n = 32) or without (control, n = 32) ChatGPT-3.5. The overall project completion rate is higher in intervention group than controls (87.5% vs. 25.0%; difference 62.5%, p = 9.42e-7), and the unassisted completion rate likewise (68.7% vs. 3.1%; difference 65.6%, p = 5.70e-8). The intervention group demonstrates better project planning and faster completion times (p < 0.01). After a 2-week washout, 41.2% of successful intervention participants complete a new project without the support of large language models (LLMs). A survey shows that 42.6% of participants fear regurgitating information without understanding and 40.4% worry about fostering lazy thinking, indicating potential dependency. Therefore, LLMs can help physicians overcome technical barriers, although long-term risks require further study. Trial registration: This study was registered at ClinicalTrials.gov (NCT06015178).
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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.