Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations

Abstract

Background – Patients with brain tumors often struggle to understand their condition because of complex imaging findings, multidisciplinary care pathways, and frequent cognitive and emotional vulnerability. Effective patient education is, therefore, essential but difficult to deliver within routine clinical encounters. Objective – This narrative review evaluates the role of large language models (LLMs) in supporting patient education for individuals with brain tumors. Content – We synthesize evidence from neuro-oncology, radiology, and digital health literature on the use of LLMs to explain imaging results, diagnoses, and treatment options in patient-centered language. Potential benefits include improved health literacy, accessibility, and continuity of education. Key limitations are also discussed, including hallucinations, output variability, overtrust, data privacy concerns, and ethical challenges. A clinician-guided framework for responsible integration is proposed. Conclusion – When used under clinician supervision as educational support tools, LLMs may enhance patient understanding and engagement in brain tumor care. Safe implementation will require structured governance, oversight, and alignment with ethical standards.