ArticleFrontiers in oncology2025
Clinical decision-making for uveal melanoma radiotherapy: comparative performance of experienced radiation oncologists and leading generative AI models.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Comment on "Artificial Intelligence in melanoma research: a bibliometric analysis".International journal of surgery (London, England) · 2026Article
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2 authors.
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Abstract
Background: Uveal melanoma is the most common primary intraocular malignancy in adults, yet radiotherapy decision-making for this disease often remains complex and variable. Although emerging generative AI models have shown promise in synthesizing vast clinical information, few studies have systematically compared their performance against experienced radiation oncologists in this specialized domain. This study examined the comparative accuracy of three leading generative AI models and experienced radiation oncologists in guideline-based clinical decision-making for uveal melanoma. Methods: A structured, 20-question examination reflecting standard radiotherapy guidelines was developed. Fifty radiation oncologists, each with 10-15 years of experience, completed an open-book exam following a 15-day standardized review. Meanwhile, Grok 3 (Think), Gemini 2.0 Flash Thinking, and Open ai o1 pro were each tested through 10 independent chat sessions. Twelve recognized experts in uveal melanoma, blinded to the source of each submission, scored all answer sets. Kruskal-Wallis tests with Results: Of the 80 total sets (50 from oncologists, 30 from AI), Open ai o1 pro achieved the highest mean total score (98.0 ± 1.9), followed by oncologists (91.5 ± 3.2), Grok 3 (82.3 ± 2.1), and Gemini 2.0 (74.2 ± 3.4). Statistically significant differences emerged across all domains, with human experts particularly excelling in treatment selection but still trailing Open ai o1 pro overall. Completion time was significantly shorter for the AI models compared with oncologists. Conclusion: These findings suggest that advanced generative AI can exceed expert-level performance in certain aspects of radiotherapy decision-making for uveal melanoma. Although AI may expedite clinical workflows and offer highly accurate guidance, human judgment remains indispensable for nuanced patient care.
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