Agentic AI Brings Context to Oncology Decision-Making

As artificial intelligence (AI) continues to enter oncology practice, the field is moving beyond general-purpose tools toward specialized applications designed to address the unique challenges of cancer care. In an interview with Targeted Oncology^TM, Shaalan Beg, MD, MBA, FASCO, of ConcertAI, discusses the premise of agentic AI and how specialized AI agents may help support clinical decision-making across the oncology care continuum.

Beg explains that agentic AI tools are designed to tackle specific problems by bringing together the totality of available evidence, patient-specific information, and relevant data from outside sources. This targeted approach may be particularly important in oncology, where clinical questions often require a nuanced understanding of multiple layers of information.

Beg highlights applications such as interpreting molecular profiles, determining a patient's line of therapy, and applying oncology guidelines within the context of an individual patient's disease and treatment history. Such tasks require tools capable of understanding the clinical context surrounding a specific question. Specialized agents can be developed and trained around distinct use cases, using the data and context needed to solve those problems effectively.