Work

Clinical decision-making for uveal melanoma radiotherapy: comparative performance of experienced radiation oncologists and leading generative AI models

Year: 2025

Type: article

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 post hoc comparisons were conducted to evaluate group-level differences in total and domain-specific performance. 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.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1605916

Authors: Xing Wang, Peng Wang

Institution: The Affiliated Yongchuan Hospital of Chongqing Medical University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801