Lugano/Berlin, 20 October 2025 – The European Society for Medical Oncology (ESMO) released today the ESMO Guidance on the Use of Large Language Models in Clinical Practice (ELCAP), the first structured set of recommendations to bring AI language models into oncology safely and effectively. The publication of ELCAP in ESMO's peer-reviewed journal Annals of Oncology coincides with a session on Chat GPT and cancer care at the ESMO Congress 2025 in Berlin, underscoring the growing role of AI in oncology.
"ESMO's priority is to ensure that innovation translates into measurable benefit for patients and workable solutions for clinicians. With ELCAP, ESMO provides a pragmatic, oncology-specific framework that embraces AI while upholding clinical responsibility, transparency and robust data protection," said Fabrice André, ESMO President.
As the use of Large Language Models (LLMs) accelerates across oncology, ELCAP recognises that opportunities and risks vary depending on the user –whether patients, clinicians or institutions, and therefore anchors the recommendations in a three-type structure, translating high-level principles into 23 consensus statements for day-to-day practice:
- The first category (Type 1) addresses patient-facing applications such as chatbots for education and symptom support, which should complement clinical care and operate within supervised pathways with explicit escalation and robust data protection.
- The second category (Type 2) covers healthcare professional-facing tools such as decision support, documentation and translation, which require formal validation, transparent limitations, and explicit human accountability for clinical decisions.
- The third category (Type 3) concerns background institutional systems integrated with electronic health records for tasks like data extraction, automated summaries and clinical-trial matching; these systems require pre-deployment testing, continuous monitoring for bias and performance change, institutional governance and re-validation when processes or data sources change. Clinicians should also be aware when such systems are operating within their environment, as their impact depends on interoperability and privacy-by-design measures.
Across settings, the reliability of outputs depends on the completeness and correctness of input data: gaps in clinical documentation or partial patient queries can lead to inaccurate or misleading responses, underscoring the need for supervision and clear escalation routes.
"ELCAP recognises that the value of language models depends on who is using them," said Miriam Koopman, Chair of the ESMO Real World Data & Digital Health Task Force, co-author of the paper. "By distinguishing patient-facing, clinician-facing and background institutional systems, we set expectations for each context: supervised pathways for patients, validated and transparent tools for clinicians, and continuously monitored, well-governed systems embedded in the electronic health records."
ELCAP focuses on assistive LLMs that operate under human oversight, supporting clinicians by providing information or drafting content rather than taking independent actions. "These systems are designed to enhance – and not replace – clinical workflows and decision-making," added Jakob N. Kather, Deputy Chair of the ESMO Real World Data & Digital Health Task Force and co-author of the study. "At the same time, the guidance acknowledges the rapid emergence of autonomous, or 'agentic', AI models capable of initiating actions without direct prompts, which raises distinct safety, regulatory and ethical challenges and will require dedicated future guidance."
Looking ahead, the ESMO President emphasised that shared standards are as critical as algorithms to ensure trust in AI-driven cancer care. "Responsible use of AI in oncology requires shared standards as much as it requires algorithms; ELCAP sets out how to deploy language models in ways that improve the quality, equity and efficiency of cancer care, without compromising trust in clinical judgement", André concluded.
Development and methods
ELCAP was developed between November 2024 and February 2025 by a 20-member international panel spanning oncology, AI, biostatistics, digital health, ethics and the patient perspective, convened under the ESMO Real World Data & Digital Health Task Force.
Reference
ESMO guidance on the use of Large Language Models in Clinical Practice (ELCAP) , by E.Y.T. Wong et al. Annals of Oncology. doi: 10.1016/j.annonc.2025.09.001
