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The practical implementation of artificial intelligence technologies in medicine


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.

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

Debbie Maizels/Springer Nature

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

Debbie Maizels/Springer Nature

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

Debbie Maizels/Springer Nature


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This study was funded in part by the Innovative team (B185004102) and Backbone talent (B185004075) training program for high-level universities of Guangzhou Medical University, Guangzhou Regenerative Medicine and Health Guangdong Laboratory.

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Correspondence to Jianxing He or Kang Zhang.

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He, J., Baxter, S.L., Xu, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med 25, 30–36 (2019).

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