The interaction of structured data using openEHR and large Language models for clinical decision support in prostate cancer.

backgroundMultidisciplinary teams (MDTs) are essential for cancer care but are resource-intensive. Decision-making processes within MDTs, while critical, contribute to increased healthcare costs due to the need for specialist time and coordination. The recent emergence of large language models (LLMs) offers the potential to improve the efficiency and accuracy of clinical decision-making processes, potentially reducing costs associated with traditional MDT models.

methodsWe conducted a retrospective study of 171 consecutively treated patients with newly diagnosed prostate cancer. Relevant structured clinical data and the European Association of Urology (EAU) pocket guidelines were provided to two LLMs (chatGPT-4, Claude-3-Opus). LLM treatment recommendations were compared to actual treatment recommendations of the MDT meeting (MDM).

resultsBoth LLMs demonstrated an overall adherence of 93% with the MDT treatment recommendations. Discrepancies between LLM and MDT recommendations were observed in 15 cases (9%), primarily due to lack of clinical information that could be provided to the LLMs. In 5 cases (3%), the LLM recommendations were not in line with EAU guidelines despite having access to all relevant information.

conclusionsOur findings provide evidence that LLMs can provide accurate treatment recommendations for newly diagnosed prostate cancer patients. LLMs have the potential to streamline MDT workflows, enabling specialists to focus on complex cases and patient-centered discussions. In this study, we explored the potential of artificial intelligence models called large language models (LLMs) to assist in treatment decision-making for prostate cancer patients. We found that LLMs, when provided with patient information and clinical guidelines, can recommend treatments that closely match those made by a team of cancer specialists, suggesting that LLMs could help streamline the decision-making process and potentially reduce healthcare costs.