Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations

Conditions studied

Breast Neoplasms, Lung Neoplasms, Urologic Neoplasms, Prostatic Neoplasms, Urinary Bladder Neoplasms, Kidney Neoplasms, Digestive System Neoplasms, Genital Neoplasms, Artifical Intelligence, Large Language Models, Decision Making, Decision Support Systems, Clinical

About this study

BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark evaluating the treatment recommendations of five frontier large language models (LLMs) against the recommendations of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to five frontier LLMs. Each recommendation - human or model - is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy, work-up and biomarkers) and scored 0/1/2 for concordance against a two-tier reference: the consensus of the two tumour boards, complemented by an a priori locked guideline matrix (ESMO, NCCN). The primary endpoint is domain-level concordance between LLM and RCP consensus, expressed as a linearly weighted Cohen's kappa. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Because expert boards may disagree with one another on identical cases, model performance is always interpreted against the human consensus. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, the locked guideline matrix, scoring algorithms and verbatim prompts are released for full reproducibility.

Interventions

• Other: Multidisciplinary tumour boards — Two independent tumour boards per localisation (10 boards in total) issue a categorical recommendation for every synthetic case. Where both boards agree, their consensus defines the reference standard; where they differ, the case-domain is classified as EQUIPOISE and analysed separately.
• Other: Frontier large language models — Five frontier LLMs (GPT-5.6, Claude Fable 5, Gemini 3.1 Pro, DeepSeek V4 Pro, Llama 4 Maverick) each receive the identical structured input for every case, three times in independent sessions, under locked prompts, versions and settings.

Primary outcomes

• Domain-level performance between LLM recommendations and the locked guidelines. (Assessed once at central scoring, after data collection (~October 2026))

Eligibility information

Study locations

• Hopital Européen Georges Pompidou, Paris, France