An Explainable AI-Driven Framework for Integrating Diverse Health Data to Enhance Predictive Accuracy and Clinical Interpretability

Abstract

The potential benefits of artificial intelligence (AI) in healthcare cannot be overstated, and there is a lot of potential to ensure that patients can receive optimal care through early disease detection, personalized treatment options, and more. Nonetheless, the used advanced AI models have remained largely hindered by their black-box nature, which is why they are usually called the black-box problem. Such a lack of transparency undermines trust amongst clinicians and regulatory agencies so as to restrict the effective utilization of such powerful tools practically. Explainable Artificial Intelligence (XAI) appears as a decisive way out in such an essential challenge. Increasing clarity and explanation of complex machine learning models have the direct benefits of fostering more trust, as well as enhancing diagnostic accuracy and the successful overall results of patients. The effectiveness with which XAI can provide transparent and decipherable information toward AI-derived predictions is not only a technical contribution, but a necessary match with demands of ethical and clinical concerns in an area where decisions have significant implications. This means that the XAI capacity will become a rising need in a regulatory and ethical Trias handled and measured in an environment where the outcomes of decisions have great implications on the outcomes of patients. This report is an account of a framework through XAI to incorporate various health data sources such as electronic health records, medical imaging, laboratory reports, and wearable sensor information, which can be integrated in the context of achieving higher predictive performance in disease prediction and treatment stratification, as well as decision-making transparency.