For Work / Against Work
Debates on the centrality of work

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

by Topol, Eric J (2019)


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.

Key Passage

Besides the productivity and workflow gains that can be derived from AI-assisted image interpretation and clinician support, there is potential to reduce the workforce for many types of back-office, administrative jobs such as coding and billing, scheduling of operating rooms and clinic appointments, and staffing. At Geisinger Health in Pennsylvania, over 100,000 patients have undergone exome sequencing; the results are provided via an AI chatbot (Clear Genetics), which is well-received by most patients and reduces the need for genetic counselors. This demonstrates how a health system can leverage AI tools to provide complex information without having to rely on expansion of highly trained personnel (p.49)


Artificial Intelligence, Machine Learning, Medicine, Physicians, Medical Technology


AI and Medicine

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