Artificial intelligence systems are increasingly used in healthcare to analyse medical images, physiological signals, and other clinical data to support diagnostic decision-making. Common applications include radiology, pathology, dermatology, retinal screening, and analysis of heart and lung sounds.In an Australian insurance context, the use of AI-assisted diagnostic tools may raise additional risk considerations relating to diagnostic accuracy and clinical governance. Healthcare professionals remain responsible for patient care, and AI outputs are typically used to support, rather than replace, human interpretation.From a coverage perspective, relevant considerations may include whether the technology is approved by the Therapeutic Goods Administration (TGA) as a medical device, evidence of validation across appropriate patient populations, clarity around the role of AI versus human interpretation, and the quality assurance processes in place for AI-generated findings.

Artificial intelligence systems are increasingly used in healthcare to analyse medical images, physiological signals, and other clinical data to support diagnostic decision-making. Common applications include radiology, pathology, dermatology, retinal screening, and analysis of heart and lung sounds.In an Australian insurance context, the use of AI-assisted diagnostic tools may raise additional risk considerations relating to diagnostic accuracy and clinical governance. Healthcare professionals remain responsible for patient care, and AI outputs are typically used to support, rather than replace, human interpretation.From a coverage perspective, relevant considerations may include whether the technology is approved by the Therapeutic Goods Administration (TGA) as a medical device, evidence of validation across appropriate patient populations, clarity around the role of AI versus human interpretation, and the quality assurance processes in place for AI-generated findings.