Rejected by the New England Journal of Medicine in November 2016.
Written with Dr. Lester Russell.
See this post for a more expansive version.

In assessing the possible impact of machine learning on clinical medicine, Obermeyer and Emanuel(1) describe the narrowing gap between human vs. computer analysis of images, and declare that “machine learning will displace much of the work of radiologists and anatomical pathologists.”

We hesitate to agree, owing to the Jevons paradox and elastic demand for medical imaging.

In 1865, the economist William Jevons predicted that more efficient coal-burning in manufacturing plants would not lower the nationwide consumption of coal. Instead, the lower cost per unit of energy would increase demand for coal energy and thereby increase consumption(2).

Thus, assuming machine interpretation lowers the cost per imaging study, future human case loads will depend on the quantitative balance between a Jevonsonian increase in imaging (if any(3)) and the fraction of cases where computers completely exclude humans (e.g. only 25% for contemporary computerized Pap smear interpretation(4)).

Clearly, major changes are coming, but, given healthcare’s tangled economics, it is premature to affirm that computerized image interpretation will decimate physician workloads.

(1) Obermeyer Z, Emanuel EJ. Predicting the future -- big data, machine learning, and clinical medicine. N Engl J Med. 2016; 375: 1216-1219.

(2) Jevons WS. The Coal Question. London: Macmillan and Co., 1865. Pages 102-104.

(3) Polimeni JM, Mayumi K, Giampetro M, Alcott B. The Jevons Paradox and the Myth of Resource Efficiency Improvements. New York: Earthscan Routledge, 2008.

(4) Bengtsson E, Malm P. Screening for cervical cancer using automated analysis of Pap-smears. Computational and Mathematical Methods in Medicine. 2014; Article ID 842037.

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Published on January 29, 2018