AI Model Achieves 89% Accuracy in Screening Patients for Clinical Trials

Researchers used large language models to automate patient screening for clinical trial enrollment, a major bottleneck causing trial failures. MedGemma with retrieval-augmented generation achieved 89.05% accuracy on benchmark tests, with particular strength in complex reasoning across long medical records. Adoption requires balancing AI capability with computing costs for different screening criteria.