ESMO guidance on the use of large language models in clinical practice (ELCAP): special article - Wong, Evelyn Y. T.; Verlingue, L.; Aldea, M.; Franzoi, M. A.; Umeton, R.; Halabi, S.; Harbeck, N.; Indini, A.; Prelaj, A.; Romano, E.; Smyth, E.; Tan, I. B.; et al. (Annals of oncology, 2025)
Background - Large language models (LLMs) are rapidly being integrated into health care, with substantial implications for oncology practice. The European Society for Medical Oncology (ESMO) developed the ESMO guidance on the use of Large Language Models in Clinical Practice (ELCAP) to provide a structured framework and basic guidance for their safe and effective application in oncology. - Patients and methods - Between November 2024 and February 2025, a multidisciplinary group of 20 experts convened under the ESMO Real World Data and Digital Health Task Force. Using literature review and a Delphi consensus process, the panel defined three categories of LLM use in oncology: type 1 (patient-facing applications), type 2 [health care professional (HCP)-facing applications], and type 3 (background institutional systems). Consensus statements were developed for each type to provide basic practical guidance. - Results - ELCAP highlights opportunities such as improved patient education and symptom management, streamlined clinical workflows, and enhanced data processing. At the same time, it addresses challenges including data privacy, algorithmic bias, regulatory compliance, and the risk of unsupervised use. The framework emphasises human oversight, protection of patient privacy, and alignment with clinical and ethical standards. Patient-facing tools should complement, not replace, professional advice and should be embedded in supervised care pathways. HCP-facing and background systems may improve efficiency and decision support but require systematic validation, transparency, and continuous monitoring. - Conclusions - ELCAP provides a three-tier framework and basic practical guidance for LLM use in oncology. ESMO supports efforts to use this framework to improve patient care, but warns against unsupervised or unvalidated use.