Is DeepSeek Capable of Passing the UK Radiology Fellowship Examinations?
Bibliographic record
Abstract
Objective: This study evaluates the capability of DeepSeek, a large language model-based Artificial intelligence (AI) system, in passing the UK Fellowship of the Royal College of Radiologists (FRCR) examination by assessing its performance on text-based components. Methods: DeepSeek R1, a publicly available AI chatbot, was tested using standardised prompts on 200 Part 1 physics questions and two sets of 120 single-best-answer questions from Part 2A of the FRCR examination. The AI’s performance was compared against the 2024 FRCR pass marks (57%-75% for Part 1 and 55%-60% for Part 2A). Due to its inability to analyse images, DeepSeek was not assessed on the anatomy or Part 2B components. Results: DeepSeek achieved an accuracy of 82% on the Part 1 physics section and 81.67% and 80% on the two Part 2A papers, surpassing the required pass thresholds for all tested sections. Discussion: These findings demonstrate that DeepSeek possesses substantial knowledge relevant to the FRCR examination and suggest potential applications in radiology education. However, its current inability to process image-based questions limits its applicability in practical radiological assessments. Future advancements integrating image analysis capabilities may enhance its role in radiology training and clinical practice. Conclusion: DeepSeek demonstrates high accuracy in answering text-based FRCR questions, highlighting its potential as an AI-driven educational tool. However, further development is required to enable comprehensive AI integration into radiology training and diagnostic workflows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".