ArticleFrontiers in artificial intelligence2026
Evaluating Chain-of-Thought reasoning in large language models for thyroid ultrasound interpretation: a dual-information approach.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To assess whether reasoning-capable large language models (LLMs) can accurately interpret both qualitative and quantitatively encoded ultrasound features of thyroid nodules within the ACR-TIRADS framework and improve diagnostic reliability. Methods: This retrospective study analyzed thyroid nodules with both radiologist-labeled qualitative ultrasound features and quantitatively encoded descriptors generated through standardized numerical modeling. Both formats were converted into structured prompts and input separately into four CoT-enabled LLMs (ChatGPT-O3, Grok-3, DeepSeek-R1, Gemini-2.5 Pro), each performing three reasoning rounds per task. Diagnostic performance was evaluated by accuracy and reproducibility, and two types of inconsistencies-cross-threshold and cross-modal conflicts-were quantified. Reasoning authenticity and conciseness were independently assessed by radiologists of varying experience. Sankey diagrams were used to summarize ACR-TIRADS category transitions. Results: ChatGPT-O3, Gemini-2.5 Pro, and Grok-3 showed strong ACR-TIRADS accuracy (91, 96, 96%), outperforming DeepSeek-R1 (79%). Grok-3 was highest in score-based accuracy (96%); DeepSeek-R1 lowest (52%). Reproducibility for categorization was Grok-3 93%, Gemini-2.5 Pro 90%, ChatGPT-O3 88%, vs. DeepSeek-R1 67%. For scoring reproducibility, Grok-3 (93%), ChatGPT-O3 (90%), and Gemini-2.5 Pro (79%) exceeded DeepSeek-R1 (18%). Physicians rated Grok-3 and Gemini-2.5 Pro highest in reasoning authenticity, while ChatGPT-O3 was most concise (mean 144 words). For quantitative tasks, Gemini-2.5 Pro (78%) and DeepSeek-R1 (74%) were most accurate; Grok-3 lowest (64%). Reproducibility was highest for Gemini-2.5 Pro (84%) and DeepSeek-R1 (86%). Across models, the proportion of nodules exhibiting cross-threshold discrepancies ranged from 3 to 17%, with Grok-3 lowest and DeepSeek-R1 highest. Cross-modal conflicts were more frequent, ranging from 27 to 36% across the four LLMs. Conclusion: Grok-3 excelled in qualitative tasks, while Gemini-2.5 Pro and DeepSeek-R1 showed strengths in quantitative analysis. CoT-enabled LLMs offered interpretable reasoning with promise for clinical decision support.
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