Trial reportBMC medical education2026
Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06748170 (Al to Improve the Diagnosis of Rare Rheumatic Diseases), which is not on this map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Al to Improve the Diagnosis of Rare Rheumatic Diseases
Who cites it
4 citing papers in PubMed.
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Effects of Artificial Intelligence-Supported Education on Critical Thinking, Problem-Solving, Clinical Reasoning, and Decision-Making in Health Science Education: A Systematic Review of Interventional Studies.Advances in medical education and practice · 2026Review
- Patient experiences, attitudes, and profiles regarding artificial intelligence in rheumatology: a German national cross-sectional survey study.Rheumatology international · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnostic errors and delays are common in rheumatology, driven by overlapping symptoms and the rarity of many diseases. While traditional diagnostic decision support systems (DDSS) have seen limited adoption because of high input burden and low perceived value, large language models (LLMs) now offer genuine dialogue and reduced effort, with rapidly improving diagnostic performance, yet empirical evidence on their real-world effectiveness and educational impact is still scarce.
objectiveThe aim of this study was to investigate the impact of an LLM on medical students’ diagnostic performance in rheumatology compared with traditional resources.
methodsIn this randomized controlled trial, medical students solved three rheumatology vignettes. For each case, they provided a main diagnosis with confidence and up to four differential diagnoses. Participants were randomized to use ChatGPT-4o plus traditional resources or traditional resources alone. The primary outcome was the proportion of correct top diagnoses. Secondary outcomes were correctness within the top 5 diagnoses, a cumulative diagnostic score, diagnostic confidence, and completion time.
resultsSixty-eight students (mean [SD] age 24.8 [2.6] years) were randomized. The LLM group identified the correct top diagnosis more often than controls (77.5% vs. 32.4%), yielding an adjusted odds ratio of 7.0 (95% CI 3.8–14.4; P<.001), and also exceeded LLM-only performance (77.5% vs. 71.6%). Cumulative diagnostic scores were higher with LLM support (mean [SD] 12.3 [2.3] vs. 6.7 [3.2]; P<.001), as was confidence (7.0 [1.3] vs. 6.1 [1.2]; P<.001). Completion time increased in the LLM group (505 [131] s vs. 287 [106] s; P<.001).
conclusionsMedical students using an LLM achieved significantly higher diagnostic accuracy than those using conventional resources. Students assisted by the LLM also outperformed the model alone, highlighting the potential of human-AI collaboration. These findings suggest that LLMs may help improve clinical reasoning in complex fields such as rheumatology. However, these findings should be interpreted cautiously, as larger and more diverse studies are needed to confirm their generalisability.
trial registrationClinicalTrials.gov, NCT06748170 registered 27 December 2024.
Indexed as
Identifiers
What OpenQuestion holds
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.