PEERING INTO THE SHADOWS OF AI IN HEALTHCARE: UNMASKING ITS LIMITATIONS AND CHARTING PATHS TOWARD RESOLUTION

Abstract

Technology in AI is now fundamental to healthcare innovation, shaping areas such as diagnostic imaging, predictive analytics, robotic procedures, and the integration of virtual health assistants. The emerging technologies are intended to increase the' efficiency, accuracy, and availability of medical treatments. However, such developments entail significant challenges that threaten AI's safety, equity, and operability and reliability in clinical settings. Data-related difficulties, such as bias, incompleteness, and lack of data dataset diversity, impair model performance and equity for diverse populations. Because of the lack of transparency, the ‘black-box’ character of most AI algorithms undermines clinical trust. It complicates results interpretation, questioning accountability and validation in life-threatening situations in healthcare. Significant assistance of ethical and regulatory constraints further slows the process of AI implementation in healthcare, as regards the issues of patient privacy, consent processes, errors caused by AI, and the problem of algorithmic fairness. Problems associated with human implementation, such as poor training, difficulties in clinical workflows, and a high reliance on AI guidance, exacerbate these problems, representing a considerable discrepancy between AI development and the realities at the frontline of medicine. This article examines the intricate limitations of AI in healthcare and examines fundamental cases that detail the threats to its rapid introduction. Further, it suggests forward-thinking options that focus on the importance of explainable AI, good data stewardship, broad inclusion in design, and responsive regulatory oversight. The article identifies these limitations and argues for pragmatic responses, calling for a transparent, ethical, and contextually informed position on AI, the endgame is to make AI a reliable tool for equitable and high-quality health outcomes.

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