Best Clinical Decision Support AI for Oncology

Oncology is among the most evidence-intensive specialties in medicine, with treatment decisions driven by tumor molecular profiling, precise staging criteria, and a research landscape that produces thousands of new studies annually. The American Cancer Society projected approximately 2 million new cancer cases in the United States for 2025, each requiring individualized treatment plans that consider tumor biology, patient comorbidities, and available clinical trials. The National Comprehensive Cancer Network publishes guidelines spanning more than 60 cancer types, and these guidelines are updated multiple times per year as new trial data emerges. Clinical decision support AI helps oncologists synthesize this volume of evidence by providing rapid access to current treatment protocols, drug interaction data for complex multi-agent regimens, and evidence summaries for emerging therapies.

Clinical trial matching is a particularly valuable CDS application in oncology. According to the National Cancer Institute, only approximately 5% of adult cancer patients enroll in clinical trials, partly because matching eligible patients to appropriate studies is a time-consuming manual process. AI-powered tools that can screen patient characteristics against trial eligibility criteria help close this gap. Additionally, the growing role of precision oncology -- where treatment is guided by genomic biomarkers such as PD-L1 expression, microsatellite instability status, and specific gene mutations -- means oncologists must integrate molecular data with clinical evidence at the point of care. CDS platforms that link treatment recommendations to their underlying evidence base are critical in oncology, where patients and families frequently seek detailed explanations of the rationale behind treatment recommendations.