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High-performance medicine: the convergence of human and artificial intelligence


The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.

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Funding was provided by the Clinical and Translational Science Award (CTSA) from the National Institute of Health (NIH) grant number UL1TR002550.

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Competing interests

E.T. is on the scientific advisory board of Verily, Tempus Labs, Myokardia and Voxel Cloud and the board of directors of Dexcoman and is an advisor to Guardant Health, Blue Cross Blue Shield Association, and Walgreens.

Correspondence to Eric J. Topol.

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Fig. 1

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Fig. 2: Examples of AI applications across the human lifespan.

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Fig. 3: The virtual medical coach model with multi-modal data inputs and algorithms to provide individualized guidance.

Debbie Maizels/Springer Nature

Fig. 4: Call for due process of AI studies in medicine.

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Fig. 5: The analogy between self-driving cars and medicine.

Debbie Maizels/Springer Nature