Presenter: Sigve Nakken, OUS
Title
Matching cancer patients to clinical trials with AI.
Abstract
Patient recruitment remains a critical bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered proof-of-concept recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility, and maintains a lightweight deployment footprint suitable for clinical environments. The pipeline delivers explainable outputs with traceable decision rationales. Evaluation across synthetic and real clinical datasets confirm state-of-the-art performance, with expert assessment validating over 90% accuracy in criterion-level eligibility classification - particularly excelling in biomarker-driven matches. Designed for modularity and privacy, TrialMatchAI provides a scalable solution for AI-driven clinical trial matching in precision medicine.
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