Safe use of LLMs in oncology

ELCAP: ESMO Guidance on the Use of Large Language Models in Clinical Practice

The European Society for Medical Oncology (ESMO) has just released 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. ELCAP has been published in ESMO’s peer-reviewed journal Annals of Oncology, coinciding with a session on “ChatGPT and cancer care” at the ESMO Congress 2025 in Berlin.

ELCAP focuses on assistive LLMs that operate under human oversight, supporting clinicians by providing information or drafting content rather than taking independent actions. Jakob N. Kather, Deputy Chair of the ESMO Real World Data & Digital Health Task Force, Professor of Clinical AI and co-author of the study explained:

Challenges in bringing LLMs into clinical practice 

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.”

Stephen Gilbert, Professor of Medical Device Regulatory Science at EKFZ for Digital Health, TUD, explained:

Recommendations for clinical applications of LLMs in oncology

As the use of LLMs accelerates across oncology, ELCAP recognizes 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.

“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.

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.

The European Society for Medical Oncology (ESMO)
Representing more than 45,000 oncology professionals, ESMO is a reference for oncology education and information. Driven by a shared determination to secure the best possible outcomes for patients, ESMO is committed to standing by those who care about cancer through addressing the diverse needs of #ONEoncologycommunity, offering #educationforLIFE, and advocating for #accessiblecancerCARE.
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