On 2017 Nov 02, Federico Cabitza commented:

The Limits of Mind: Extended by Computers, or just distanced from sight?

In their Perspective [1] (Obermeyer Z, 2017), Obermeyer and Lee claim that computers, ”far from being the problem [of the increasing complexity of contemporary medicine], are the solution” and suggest that, as the inadequacy of “our inborn sensorium” spurred the development of “stethoscopes, electrocardiograms, and radiographs”, likewise the inadequacy of our “inborn cognition” motivates an analogous augmentation by computers.

However, the mentioned sensorial augmentation amplifies subtle clinical signs offering them at the physicians’ interpretation, while computers would augment cognition in terms of mere textual categories and numerical data, thus often shortcutting intuition, dispelling uncertainty [2] (Simpkin AL, 2016) from clinical reasoning and worse yet potentially biasing interpretation [3] (Goddard K, 2012).

Understating the irreducible gap between the discreteness of data and the continuous (and partly ineffable) experience of illness in physicians regards the “demise of context” we highlighted [4] when physicians overrely on computer outputs (Cabitza F, 2017).

While the computers’ potential for pattern recognition in diagnostic imaging is indisputable, the complexity of more clinical applications has so far been irksome to master [5].

This suggests prudence before entrusting the “future of medicine” to a wider digitization, which can entail unintended bottlenecks.

Federico Cabitza, PhD; Camilla Alderighi, MD; Raffaele Rasoini, MD;

1. Obermeyer Z, Lee TH. Lost in Thought — The Limits of the Human Mind and the Future of Medicine. NEJM 2017; 377:1209-1211

2. Simpkin AL, Schwartzstein RM. Tolerating uncertainty—the next medical revolution? NEJM 2016; 375(18): 1713-1715.

3. Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. JAMIA. 2011;19(1):121-7.

4. Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA 2017; 318(6): 517–518

5. Ross C, Swetlitz I. IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It's Nowhere Close. Scientific American, 6 september 2017.

On 2017 Nov 02, Federico Cabitza commented:

The Limits of Mind: Extended by Computers, or just distanced from sight?

In their Perspective [1] (Obermeyer Z, 2017), Obermeyer and Lee claim that computers, ”far from being the problem [of the increasing complexity of contemporary medicine], are the solution” and suggest that, as the inadequacy of “our inborn sensorium” spurred the development of “stethoscopes, electrocardiograms, and radiographs”, likewise the inadequacy of our “inborn cognition” motivates an analogous augmentation by computers.

However, the mentioned sensorial augmentation amplifies subtle clinical signs offering them at the physicians’ interpretation, while computers would augment cognition in terms of mere textual categories and numerical data, thus often shortcutting intuition, dispelling uncertainty [2] (Simpkin AL, 2016) from clinical reasoning and worse yet potentially biasing interpretation [3] (Goddard K, 2012).

Understating the irreducible gap between the discreteness of data and the continuous (and partly ineffable) experience of illness in physicians regards the “demise of context” we highlighted [4] when physicians overrely on computer outputs (Cabitza F, 2017).

While the computers’ potential for pattern recognition in diagnostic imaging is indisputable, the complexity of more clinical applications has so far been irksome to master [5].

This suggests prudence before entrusting the “future of medicine” to a wider digitization, which can entail unintended bottlenecks.

Federico Cabitza, PhD; Camilla Alderighi, MD; Raffaele Rasoini, MD;

1. Obermeyer Z, Lee TH. Lost in Thought — The Limits of the Human Mind and the Future of Medicine. NEJM 2017; 377:1209-1211

2. Simpkin AL, Schwartzstein RM. Tolerating uncertainty—the next medical revolution? NEJM 2016; 375(18): 1713-1715.

3. Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. JAMIA. 2011;19(1):121-7.

4. Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA 2017; 318(6): 517–518

5. Ross C, Swetlitz I. IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It's Nowhere Close. Scientific American, 6 september 2017.