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Abstract

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone psychotherapy chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., (i) they enable early diagnosis and intervention through digital phenotyping; (ii) they defy the stigma of mental illness diagnosis and treatment; (iii) they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises.

Details

Title
Is Big Data the New Stethoscope? Perils of Digital Phenotyping to Address Mental Illness
Author
Tekin, Şerife 1   VIAFID ORCID Logo 

 University of Texas at San Antonio, Department of Philosophy and Classics, San Antonio, USA (GRID:grid.215352.2) (ISNI:0000000121845633) 
Pages
447-461
Publication year
2021
Publication date
Sep 2021
Publisher
Springer Nature B.V.
ISSN
22105433
e-ISSN
22105441
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2568106575
Copyright
© Springer Nature B.V. 2020.