Checklist to Support the Development and Implementation of AI in Clinical Settings

Abstract

Abstract The integration of Artificial Intelligence (AI) in healthcare settings demands a nuanced approach that considers both technical performance and sociotechnical factors. Recognizing this, our study introduces the Clinical AI Sociotechnical Framework (CASoF), developed through literature synthesis, and refined via a Modified Delphi study involving global healthcare professionals. Our research identifies a critical gap in existing frameworks, which largely focus on either technical specifications or trial outcomes, neglecting the comprehensive sociotechnical dynamics essential for successful AI deployment in clinical environments. CASoF addresses this gap by providing a structured checklist that guides the planning, design, development, and implementation stages of AI systems in healthcare. The checklist emphasizes the importance of considering the value proposition, data integrity, human-AI interaction, technical architecture, organizational culture, and ongoing support and monitoring, ensuring that AI tools are not only technologically sound but also practically viable and socially adaptable within clinical settings. Our findings suggest that the successful integration of AI in healthcare depends on a balanced focus on both technological advancements and the socio-technical environment of clinical settings. CASoF represents a step forward in bridging this divide, offering a holistic approach to AI deployment that is mindful of the complexities of healthcare systems. The checklist aims to facilitate the development of AI tools that are effective, userfriendly, and seamlessly integrated into clinical workflows, ultimately enhancing patient care and healthcare outcomes.

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