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Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment

Published online by Cambridge University Press:  12 May 2021

Daniel S. Barron Open the ORCID record for Daniel S. Barron [Opens in a new window]
Daniel S. Barron*
Affiliation:
Department of Psychiatry, Yale University, New Haven, CT, USA Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA Department of Psychiatry, Brigham & Women's Hospital, Harvard University, Boston, MA, USA Department of Anesthesiology & Pain Medicine, Brigham & Women's Hospital, Harvard University, Boston, MA, USA
*
Author for correspondence: Daniel S. Barron, Email: daniel.s.barron@yale.edu
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Extract

The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that ‘vitals’ are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. There are as many ways to approach a clinical interview as there are psychiatrists; and trainees can learn as many ways of performing and formulating the clinical interview as there are instructors (Nestler, 1990). Even in the same clinical setting, two clinicians might interview the same patient and conduct very different examinations and reach different treatment recommendations. From the perspective of data science, this mismatch is not one of personal style or idiosyncrasy but rather one of uncertain salience: neither the clinical interview nor the data thereby generated is operationalized and, therefore, neither can be rigorously evaluated, tested, or optimized.

Keywords

Schizophreniadiagnosismachine learning
Type
Invited Commentary
Information
Psychological Medicine , Volume 51 , Issue 15 , November 2021 , pp. 2522 - 2524
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press.

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