The practical implementation of artificial intelligence technologies in medicine

Abstract

The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.

Fig. 1 |. Potential roles of AI-based technologies in healthcare.

In the healthcare space, AI is poised to play major roles across a spectrum of application domains, including diagnostics, therapeutics, population health management, administration, and regulation. NIPT, noninvasive prenatal test. Credit: Debbie Maizels/Springer Nature

Fig. 2 |. Integration of patient health information at multiple interfaces.

The vast quantity and accessibility of electronic health information will influence decision-making on multiple fronts, including for patients, physicians, healthcare systems, healthcare providers, and regulatory bodies. Standardization of information storage and retrieval will be critical for facilitating information exchange across these multiple interfaces. Credit: Debbie Maizels/Springer Nature

Fig. 3 |. Conceptual diagram of the FDA precertification for SaMD.

This approach represents an organization-centric evaluation to facilitate streamlined review and faster adoption of technology. Credit: Debbie Maizels/Springer Nature