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June 6, 2025
Clinical decision-making in oncology is challenging and requires the analysis of various data types – from medical imaging and genetic information to patient records and treatment guidelines. To effectively support medical practice, AI models must be capable of processing multimodal data and have reasoning and problem-solving capabilities that resemble those of humans.
To build an autonomous AI agent for precision medicine, the researchers enhanced the large language model GPT-4 with several digital tools including radiology report generation from MRI and CT scans, medical image analysis, prediction of genetic alterations directly from histopathology slides, and search functions across platforms such as PubMed, Google and OncoKB. To ensure that decisions were grounded in current medical knowledge, the model was given access to around 6,800 documents compiled from official oncology guidelines and clinical resources.
The AI agent was evaluated on 20 real-world, simulated patient cases using a two-step process: First, the system selected appropriate tools, then it retrieved relevant medical information to guide its reasoning. The outputs were reviewed by human medical experts for accuracy, completeness, and correct citation of sources. The AI agent reached correct clinical conclusions in 91% of cases and accurately cited relevant oncology guidelines in over 75% of its responses.
Importantly, the use of specialized tools and medical information retrieval significantly improved the model’s performance. As a result, so-called “hallucinations” seemingly plausible but incorrect statements were significantly reduced. This improvement is particularly important in the sensitive area of healthcare. They could help in daily decision-making processes and support doctors to stay updated on the latest treatment recommendations, contributing to the identification of optimal personalized care for cancer patients.