Challenges in incorporating artificial intelligence into daily healthcare practice

Abstract

Artificial intelligence (AI) holds huge potential in improving diagnosis and streamlining workflows in health care. However, several challenges remain, hampering the widespread adoption in clinical settings, such as for assessing data quality, bias, interoperability, and privacy, as well as for use in regulation and clinician training. Potent data channels are vital for assuring the exactness and trustworthiness of diagnostic performance. They boost the transmission of high-quality information, which is essential for expert annotations. Interoperable electronic health record integration and federated or privacy‑enhancing training approaches allow real‑time analytics while guarding patient data. Regulatory indecision and the comprehensive and continuous supervision of the process require transparent, explainable AI and shared accountability among developers, doctors, and institutions. In addition, prospective clinical validation, physician education, and governance are paramount to building trust and guaranteeing safe AI deployment in health care. This review outlines the difficulties faced when integrating these technological advancements into everyday clinical practice.

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