Explainable artificial intelligence for point-of-care cancer diagnosis (Conference Presentation)

Explainable artificial intelligence algorithms for point-of-care cancer diagnosis are designed to make the decision-making processes of AI models transparent and interpretable, that address a critical need in clinical practice. These algorithms integrate machine learning models with interpretable features, allowing clinicians to understand how decisions are made by AI algorithms. By providing clear explanations, they bridge the gap between AI-driven predictions and clinical insights, enhancing trust and enabling informed patient management. This approach not only improves diagnostic accuracy and efficiency but also empowers healthcare professionals to make confident, evidence-based decisions, thereby advancing personalized medicine in oncology.