Explainable AI in Healthcare: Enhancing Transparency and Trust in Predictive Models

Explainable AI in healthcare brings transparency and trust to predictive models, empowering doctors and patients to understand decision-making processes. Validating models against clinical expertise improves accuracy and addresses ethical concerns. Real-life examples include detecting lung cancer nodules and predicting sepsis. The future holds enhanced diagnosis and treatment strategies, improved patient engagement, and deeper insights into algorithmic decision-making.