Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach

Our research shows that combining explainable techniques with AI models greatly enhances the accuracy of case detection and boosts the trust of medical professionals. These models result in directly involving the reader and health care professional in the decision-making process, making informed decisions, and efficiently allocating resources by providing insight into the decision-making process. In addition, this study underscores the potential of AI in public health surveillance, particularly in enhancing responses to emerging infectious diseases such as monkeypox.