Assessment of decision-making with locally run and web-based large language models versus human board recommendations in otorhinolaryngology, head and neck surgery

Tumor boards are a cornerstone of modern cancer treatment. Given their advanced capabilities, the role of Large Language Models (LLMs) in generating tumor board decisions for otorhinolaryngology (ORL) head and neck surgery is gaining increasing attention. However, concerns over data protection and the use of confidential patient information in web-based LLMs have restricted their widespread adoption and hindered the exploration of their full potential. In this first study of its kind we compared standard human multidisciplinary tumor board recommendations (MDT) against a web-based LLM (ChatGPT-4o) and a locally run LLM (Llama 3) addressing data protection concerns.