TL;DR
ChatGPT achieved the highest overall concordance, although all models generated clinically acceptable recommendations in most cases, and all LLMs demonstrated high concordance with consultant-led thyroid cancer MDT decisions.
While LLMs demonstrate promising concordance in standardized thyroid cancer management, they are best positioned as supportive decision aids—such as in MDT preparation and workflow streamlining—rather than replacements for expert multidisciplinary evaluation, particularly in complex clinical scenarios.
Both models demonstrated high agreement with expert GIST MTB recommendations, with no significant performance difference between them, and support a potential assistive role for LLMs in GIST MTB workflows, while underscores the continued necessity of expert oversight.
Leading LLMs show variable capacity to align with ATC clinical guidelines, while top-performing models hold promise as supportive tools, their inconsistencies across domains and complexity levels preclude autonomous clinical use.
A multi-dimensional evaluation framework integrating clinical quality and operational efficiency provides more actionable insights than single metric assessments, enabling pragmatic model selection for oncology practice.
Publicly available multimodal LLMs showed measurable but heterogeneous performance in grayscale ultrasound-based thyroid nodule classification, but none of the models matched senior radiologist-level performance.
The appropriate role of AI-assisted decision support as an adjunct to, rather than a substitute for, multidisciplinary review in rectal cancer management is defined.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
