Clinical AI must prove its value beyond a controlled demo. In this sponsored episode of Podnosis, host Irena Luo speaks with Chad Dodd, vice president of product management at Athenahealth, about how healthcare leaders can evaluate AI governance, safety and performance. Dodd explains why organizations should examine how a solution handles real-world data, edge cases and workflow variation, not only its ideal output. He discusses governance across the product development lifecycle, including testing for bias, accuracy and data validity, as well as validation across specialties, datasets and patient samples.

The conversation also covers practical clinician oversight through clear labeling of AI-generated information and links to source content. After deployment, Dodd says organizations should monitor model outputs, drift, telemetry, feature adoption and user feedback. For practices comparing AI partners, the discussion highlights four priorities: demonstrated workflow value, data privacy and security, transparent failure points, and evidence that the technology delivers its intended result safely and compliantly. Listen to the full podcast to learn more.