ArticleBMC medical education2025
DeepSeek-R1 for automated scoring in radiology residency examinations: an agreement and test-retest reliability study.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Preliminary evaluation of DeepSeek-R1 and GPT-5.3 in selected PET/CT clinical scenarios: patient preparation, report interpretation, and diagnostic reasoning.Frontiers in medicine · 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
5 authors.
Funding
Abstract
objectiveThis study evaluates the feasibility of employing DeepSeek-R1 for automated scoring in examinations for radiology residents, comparing its performance with that of radiologists.
methodsA cross-sectional study was undertaken to assess 504 diagnostic radiology reports produced by eighteen third-year radiology residents. The evaluations were independently conducted by Radiologist A, Radiologist B, and DeepSeek-R1 (as of June 15, 2025), utilizing standardized scoring rubrics and predefined evaluation criteria. One month after the initial evaluation, a re-assessment was performed by DeepSeek-R1 and Radiologist A. The inter-rater reliability among Radiologist A, Radiologist B, and DeepSeek-R1, in addition to the test-retest reliability, was analyzed using intraclass correlation coefficients (ICC).
resultsThe ICC values between DeepSeek-R1 and Radiologist A, DeepSeek-R1 and Radiologist B, and Radiologist A and Radiologist B were found to be 0.879, 0.820, and 0.862, respectively. The test-retest ICC for DeepSeek-R1 was determined to be 0.922, whereas for Radiologist A, it was 0.952. The ICC between DeepSeek-R1 (re-test) and Radiologist A (re-test) was 0.885.
conclusionThe performance of DeepSeek-R1 was comparable to that of radiologists in the evaluation of radiology residents' reports. The integration of DeepSeek-R1 into medical education could effectively assist in assessment tasks, potentially alleviating faculty workload while preserving the quality of evaluations.
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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.