Translating Artificial Intelligence Algorithms into Clinical Practice: Addressing Implementation Barriers in Healthcare
Keywords:
Artificial intelligence, clinical practice, healthcare implementation, algorithm adoptionAbstract
The integration of artificial intelligence (AI) algorithms into clinical practice holds immense potential to improve diagnostic accuracy, treatment planning, and operational efficiency in healthcare settings. However, despite significant technological advancements, translating AI innovations into routine care faces numerous implementation barriers. This paper systematically examines the key challenges impeding adoption, including data privacy concerns, lack of standardized protocols, integration with legacy health information systems, clinician trust and acceptance, regulatory compliance, and the need for robust validation in real-world environments. It also explores strategies to overcome these obstacles, such as establishing interdisciplinary collaboration, developing transparent and interpretable models, implementing rigorous evaluation frameworks, and designing scalable deployment pipelines. By addressing these barriers comprehensively, this work aims to facilitate the effective translation of AI algorithms from research prototypes to impactful tools in everyday clinical workflows.
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Copyright (c) 2025 Dr. Chen Zhao, Dr. Beatriz Santos López
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