Thinking Through AI

Clinical AI risk management for patient safety: hazards like missed findings, drift and automation bias, the controls for each and how to monitor after launch.

How to optimize clinical workflows, find common sources of waste and use AI where it helps.

A short history of Google’s original Med-PaLM model, its results, limits and successors.

Explore how AI applications in medical diagnosis and imaging are revolutionizing radiology and pathology to enhance accuracy, speed, and patient care.

How AI is used across the medical field by specialty, with sourced examples in gastroenterology, dermatology, sepsis care, cardiology and oncology.

Medical artificial intelligence explained for healthtech teams: main categories, FDA regulation, data needs, benefits, limits and how to evaluate products.

A department-by-department map of how hospitals use AI, who runs it and how it is governed.

How medical AI software is regulated in the US and EU, from FDA routes to the EU AI Act.

How hospitals can use AI for better patient care: early warning, imaging triage, safety, discharges and documentation, with evidence and safe rollout steps.