Generative vs. Predictive: The Two Faces of AI Every Clinician Should Understand

The AI conversation in healthcare is loud, but not always clear. Many physicians lump AI into one large, nebulous concept without recognizing the distinct types of models that shape how AI actually performs in clinical practice.

During a recent webinar hosted by Offcall’s co-founder Dr. Graham Walker and Validara Health founder Dr. Sarah Isaksson broke AI down into a simple, digestible framework that every clinician can understand.

The Two Core Types of AI: Predictive vs. Generative

Dr. Walker explained it plainly: "Think of AI in two flavors. Predictive models are deterministic — the same input always yields the same output. Generative models are probabilistic, meaning the same input could result in multiple, slightly varied outputs" [Watch the full video at 00:11:27].

Why It Matters Clinically

Predictive AI shines in areas like risk stratification, diagnostic support, and clinical algorithms. Its strength lies in consistency and auditability. "One plus one always equals two — not 2.01, not a sandwich recipe," Walker quipped [Watch at 00:11:27].

Generative AI, on the other hand, is powerful when tasks involve language: summarizing clinical notes, drafting prior authorizations, or converting discharge summaries into patient-friendly language.

"Many tools today blend both types," noted Dr. Gebauer. "You might have a generative core wrapped in a deterministic layer to add reliability," she said. [Watch at 00:12:39]. This hybrid approach reflects the growing complexity — and opportunity — of AI in clinical tools.

A Quick Clinical Rule of Thumb:

• Use predictive AI for reproducibility and safety (e.g., decision support).
• Use generative AI for flexibility and speed (e.g., documentation).

Want More Clinical Examples? This webinar also featured additional use cases, demos, and a curated resource list of AI tools for clinicians.

→ Download the full AI for Clinicians Resource Guide which is free for clinicians here.