Source: Financial Times (paywall)
Clinicians are drawing a hard line between AI applications they trust—image analysis where performance metrics are clear and historical data abundant—and broader clinical deployments where algorithms influence treatment decisions without comparable evidence. The distinction reflects a real stakes difference: misdiagnosing a chest X-ray affects one patient; a flawed AI system recommending drug dosages or patient triage affects workflows and liability across entire hospital systems. Venture-backed medical AI companies will face a credibility wall without prospective clinical trial data that regulators and providers increasingly demand before deployment.