From Probabilities to Pathways: Making Agentic AI Safe for Clinical Workflows

Healthcare rarely unfolds as a straight line. Each patient arrives with a unique history, evolving symptoms, and unexpected comorbidities that force care teams to adapt in real time. Generative AI mirrors this reality. It reasons probabilistically, explores multiple paths, and rarely produces the same answer twice. On their own, both healthcare and generative AI are powerful precisely because they are non-deterministic. But when these two forces are combined to automate clinical workflows, their shared uncertainty compounds. The result is a fragile system where reproducibility falters, audits become difficult, and transparency erodes. What healthcare automation has been missing is a steady, deterministic layer that can sit between human judgment and probabilistic intelligence, translating adaptive reasoning into something that can be trusted, traced, and executed.

In this session, we show what that missing layer looks like in practice. We demonstrate an agentic AI system that listens, reasons, and then commits its decisions into explicit business process models grounded in clinical protocols. As new patient conditions or comorbidities emerge, the system deterministically branches and reshapes the care pathway, not by improvisation, but by updating a clear, inspectable process map. The outcome is a living yet predictable representation of the patient journey, one that faithfully reflects clinical reality while remaining suitable for orchestration across people and systems. This approach bridges adaptability with accountability, enabling healthcare organizations to harness generative AI without surrendering control, confidence, or clarity.