Integrating AI into healthcare systems: A multivocal literature review

Abstract

The integration of artificial intelligence (AI) into healthcare systems promises to improve patient care, enhance operational efficiency, and facilitate personalized medicine. The goal of this paper is to provide a comprehensive review of the current challenges that hinder the seamless adoption of AI in healthcare. Additionally, the paper aims to delineate the best practices for achieving optimal integration of AI within the medical domain. To achieve these objectives, we employ a Multivocal Literature Review (MLR), a systematic literature review methodology that incorporates both peer-reviewed publications and non-peer-reviewed sources, including technical blog posts and white papers. Substantial evidence in the literature points to challenges related to data quality, model bias, interoperability, patient privacy, and the susceptibility of AI systems to adversarial attacks. Additionally, there is growing awareness of challenges such as the distributional shift between training and production data, as well as the critical need for continuous monitoring and retraining of AI models within dynamic clinical settings. Based on our review, we advocate for the adoption of best practices aimed at mitigating the identified challenges, including rigorous model evaluation, standardization of data practices, and promotion of interdisciplinary collaboration. Furthermore, we emphasize the need for responsible AI that aligns with principles of fairness, transparency, security, and reliability, underscoring the importance of multi-stakeholder engagement.

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