Integrating AI Biomarkers Into Shared Decision-Making

In this segment, Amar U. Kishan, MD, of UCLA describes how he introduces the concept of an AI-based predictive biomarker to patients with intermediate-risk prostate cancer who may be encountering the idea for the first time. He frames the shared decision-making conversation around the patient's own values and goals, particularly the trade-off between accepting the adverse effect burden of hormone therapy and the potential reduction in metastasis risk.

Recent cost-effectiveness data of an AI-based biomarker test adds a dimension to this conversation that extends beyond individual patients to the health care system as a whole. Kishan notes that when a biomarker-negative result spares a patient from unnecessary androgen deprivation therapy (ADT), it simultaneously reduces system-level costs.

In closing, Kishan situates the ArteraAI test within the larger trajectory of precision medicine in prostate cancer, describing the current moment as one in which AI tools are transitioning from novelty to genuine clinical utility and in which predictive biomarkers are emerging as viable instruments for treatment individualization. He expresses enthusiasm for forthcoming validation data that may further strengthen the evidence base for the test. His single takeaway message for community urologists encapsulates the program's central argument: ADT carries a quality-of-life cost for patients and an economic cost for the system, and a test that can identify who bears that cost unnecessarily represents the kind of precision medicine advance the field should be moving toward.