The role of large language models in advancing head and neck cancer research and care: a narrative review

Abstract

Background and Objective: Large language models (LLMs) are transforming the landscape of medicine by providing highly improved data processing forces. In head and neck cancer (HNC), these models can be of great aid in diagnostic practice and potential treatment outcomes through extensive categorizations of data. The objective of this study is to review the HNC LLM literature. Methods: A descriptive review of the literature on the use of LLMs in HNC is provided. Keywords were searched in the databases PubMed, Science Direct, and Web of Science. We reviewed manuscripts published in English, with no time limits. Key Content and Findings: The use of LLMs improved the applications in the fields of education and clinical decision-making. Integration with many sources of medical literature to aid clinicians in real-time can potentially reduce diagnostic errors and such in turn can lead the formulation of an efficient management plan, by the LLMs. Furthermore, LLMs may help in the patient-doctor relationship by providing clear and easily understandable information about the ailments and every possible treatment, tending to the interests of the patients. Conclusions: The benefits of LLMs in the delivery of HNC care are rather apparent, prompting measures to safeguard against the risks and develop an ethical framework for the approach’s proper implementation. Several challenges including data protection, empathetic decision-making, and the inclusion of bias must be solved in order to facilitate the trust and credibility of applications in healthcare. LLMs demonstrate substantial enhancements to the effectiveness of patient management and clinical efficiency, yet their implementation enhances healthcare, the progress toward artificial intelligence should remain monitored to avoid adverse impacts on evolving individualized medicine in HNC care.

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