ArticleBrain & spine2026
AI at the Sella Turcica: Multi-Model Large Language Model Evaluation in Pituitary Adenomas.
Article in Brain & spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Safety-Filter Fallback on Consumer Health Questions During the Initial Claude Fable 5 Deployment: Observational Study.JMIR AI · 2026Article
- Can large language models accurately compute descriptive statistics from structured datasets? A comparative evaluation of ChatGPT and Claude.American heart journal plus : cardiology research and practice · 2026Article
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Optimizing a Multimodal Large Language Model for Ultrasound-Based Thyroid Nodule Malignancy Classification: A Comparative Study of Few-Shot Learning, Prompt Engineering, and Fine-Tuning.Diagnostics (Basel, Switzerland) · 2026Article
- "MELMA" in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
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Authors and funding
6 authors.
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Abstract
Introduction: Large language models (LLMs) are explored as clinical decision-support tools in complex medical fields. However, their reliability and clinical usefulness in multidisciplinary management of pituitary adenomas remain insufficiently evaluated using validated, clinician-based frameworks. Research question: Do LLMs differ in informational quality, clinical reasoning, and expert satisfaction when applied to pituitary adenoma-related clinical scenarios? Materials and methods: A prospective comparative study evaluated three LLMs: ChatGPT-5.0, Claude Opus 4.1, and Gemini 2.5 Flash. A standardized prompt set covering general knowledge, surgical decision-making, endocrine evaluation, patient education, and MRI-based scenarios was submitted to each model identically. Outputs were anonymized and independently assessed by 10 board-certified doctors using three validated instruments: the Quality Assessment of Medical Artificial Intelligence (QAMAI), the Artificial Intelligence Performance Instrument (AIPI), and the Artificial Intelligence Satisfaction and Performance Evaluation Questionnaire (AISPE-Q). Results: Claude Opus 4.1 achieved the highest performance across all major domains. Aggregate QAMAI scores were highest for Claude Opus 4.1 (4.39 ± 0.66), compared with ChatGPT-5.0 (4.12 ± 0.74) and Gemini 2.5 Flash (4.07 ± 0.76; p = 0.018). Clinical reasoning assessed by AIPI was superior for Claude Opus 4.1 versus Gemini 2.5 Flash and ChatGPT-5.0. Strong correlations were observed between informational quality, reasoning performance, and satisfaction. Discussion and conclusion: LLMs exhibit significant variability in performance when managing pituitary adenomas. Claude Opus 4.1 demonstrated the highest levels of informational quality, reasoning depth, and expert trust. While LLMs may serve as supportive adjuncts in multidisciplinary pituitary care, structured evaluation and expert oversight remain essential before clinical integration. Level of evidence: 2 - Prospective comparative diagnostic accuracy study.
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