Optimizing Healthcare Decisions Using Explainable AI for Enhanced Predictions

Abstract

In the last few years, the use of AI in the healthcare sector has brought about a great change where decision-making processes are concerned, and thus the accuracy of diagnosis, treatment planning, and patient outcomes has improved by leaps and bounds. This article investigates the application of Explainable AI (XAI) in the optimization of health decisions, pointing out the importance of interpretability and transparency in AI models that are used for more accurate prognoses. Usually, traditional AI tools which are commonly known as "black boxes" are the cause of disconnection among medical practitioners because the real decision-making process is not clear. In contrast, the XAI approach provides understandable insights by enabling users to understand the model's actions, therefore, it builds confidence and helps users make informed choices. This paper will cover the different XAI strategies ranging from computer vision-based ones to those expanding on its application in healthcare, besides tackling questions of how they affect prediction accuracy and reliability of health. It will also include case studies on successful XAI implementation. Also, the ethical issues and the development of the future are used to address the issue so as to ensure that healthcare does not just improve performance, but also is in line with the patient-oriented and regulatory standards. Let us through this exploration, show how XAI can be a spark of hope for the advancement of healthcare delivery, as well as an enabler of more transparency, accountability, and effectiveness in the healthcare "ecosystem".

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