Artificial Intelligence in Medicine: Barriers, Solutions, and Strategies

Abstract

Description The integration of artificial intelligence (AI) and machine learning (ML) into health care holds the potential to revolutionize patient care by enhancing clinical decision-making, improving diagnostic accuracy, and reducing costs. Despite this promise, adoption remains limited due to a range of technical, regulatory, educational, and cultural barriers. This paper examines these challenges and proposes strategies to support safe and effective implementation of AI in clinical practice. Key barriers include the lack of model interpretability, often referred to as the "black box" problem, which undermines clinician trust and accountability in clinical settings, evolving regulatory frameworks and unresolved questions surrounding liability, and persistent concerns regarding data quality, access, and privacy. In addition, bias in AI models remains a critical issue with implications for health equity. Beyond these established challenges, emerging barriers include a disconnect between academically developed models and real-world clinical development, as well as the need for substantial transformation in medical education to address AI assisted clinical workflows. The increasing use of AI in core clinical tasks, such as documentation and diagnostic support, also raises concerns regarding the potential erosion of fundamental clinical skills. Without deliberate training strategies, reliance on AI may compromise the development and maintenance of independent clinical reasoning. To address these challenges, this paper emphasizes the importance of interdisciplinary collaboration, transparent and interpretable model development, modernization of medical education, and the establishment of adaptive regulatory and ethical frameworks. Ultimately, the successful integration of AI in health care will depend on aligning technological innovation with clinical trust, education, and patient centered care.

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