Machine Learning (Healthcare)Machine learning and decision tree algorithms are used in healthcare to analyse clinical data, identify patterns, and generate predictions or recommendations to support clinical decision-making. Machine learning models are typically trained and updated using large datasets, while decision trees use structured branching logic to guide decisions.In an insurance context, the use of machine learning tools in clinical practice may raise questions about how professional judgement is exercised when algorithm-generated recommendations differ from traditional approaches. In Australia, healthcare professionals remain responsible for patient care, and the use of AI tools is assessed in light of accepted clinical practice and governance arrangements.From a risk and coverage perspective, insurers may consider factors such as the regulatory status of the technology, transparency and explainability of algorithm outputs, validation on relevant patient populations, ongoing performance monitoring, and clear protocols for when clinicians should override algorithmic recommendations. Coverage is subject to the terms, conditions, and exclusions of the relevant policy.

Machine Learning (Healthcare)Machine learning and decision tree algorithms are used in healthcare to analyse clinical data, identify patterns, and generate predictions or recommendations to support clinical decision-making. Machine learning models are typically trained and updated using large datasets, while decision trees use structured branching logic to guide decisions.In an insurance context, the use of machine learning tools in clinical practice may raise questions about how professional judgement is exercised when algorithm-generated recommendations differ from traditional approaches. In Australia, healthcare professionals remain responsible for patient care, and the use of AI tools is assessed in light of accepted clinical practice and governance arrangements.From a risk and coverage perspective, insurers may consider factors such as the regulatory status of the technology, transparency and explainability of algorithm outputs, validation on relevant patient populations, ongoing performance monitoring, and clear protocols for when clinicians should override algorithmic recommendations. Coverage is subject to the terms, conditions, and exclusions of the relevant policy.