Explainable AI in Healthcare Applications

Abstract

The entry of artificial intelligence into health care systems brings unprecedented advances in diagnosing, personalized treatment, and predictive analytics. Many of these AI models, especially the deep-learning algorithms, have been referred to as "black boxes" and raise gigantic questions about trust, transparency, and reliability in clinical settings. Therefore, explainable AI answers the challenges by drawing to the fore methodologies that make AI models more interpretable, thereby making them more accepted and usable in the fraternity of health. It engages with XAI in healthcare by scrutinizing various aspects of feature importance analysis, architectures of the interpretable model, and visual explication of decisions driven by AI. The case studies regarding applications of XAI in the fields of radiology, disease prediction, and personalized medicine illustrate how this technology has its own importance even in terms of improving the precision of diagnosis and clinician-patient communication. We are answerable to ethics, such as how to explain respect for the trust of patients, legalities in deploying XAI in healthcare, and further directions to be taken so that XAI does not lag behind the timeline of AI innovation. Our results repeat again that XAI indeed is the much-needed solution to be technically suitable and necessary for ensuring the responsible adoption of AI within healthcare systems to empower clinicians using not only accurate but also transparent, understandable, and aligned AI systems in clinical best practice. In conclusion, this paper concludes by highlighting explainable AI as one of the key enablers toward safe, effective, and widely accepted AI applications in health care.

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