Expectations for Artificial Intelligence (AI) in Psychiatry

被引：24

作者：

Monteith, Scott ^{[1
]}

Glenn, Tasha ^{[2
]}

Geddes, John ^{[3
]}

Whybrow, Peter C. ^{[4
]}

Achtyes, Eric ^{[5
,6
]}

Bauer, Michael ^{[7
]}

机构：

[1] Michigan State Univ, Coll Human Med, Traverse City Campus, Traverse City, MI 49684 USA

[2] ChronoRecord Assoc, Fullerton, CA USA

[3] Univ Oxford, Warneford Hosp, Dept Psychiat, Oxford, England

[4] Univ Calif Los Angeles, Semel Inst Neurosci & Human Behav, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90024 USA

[5] Michigan State Univ, Coll Human Med, Grand Rapids, MI 49684 USA

[6] Network180, Grand Rapids, MI USA

[7] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Med Fac, Dept Psychiat & Psychotherapy, Dresden, Germany

来源：

CURRENT PSYCHIATRY REPORTS | 2022年 / 24卷 / 11期

关键词：

Artificial intelligence; Machine learning; Psychiatry; Technology maturity; BEHAVIORAL HEALTH; DECISION-SUPPORT; MEDICAL DEVICES; MACHINE; TECHNOLOGY; SPEECH; TRENDS; VALLEY; CARE; CLASSIFICATION;

D O I：

10.1007/s11920-022-01378-5

中图分类号：

R749 [精神病学];

学科分类号：

100205 ;

摘要：

Purpose of Review Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. Recent Findings For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

引用

页码：709 / 721

页数：13

共 198 条

[1]

ACR (American College of Radiology) Data Science Institute AI Central, 2022, FDA CLEAR AI ALG

[2] Machine Learning and Health Care Disparities in Dermatology [J].

Adamson, Adewole S. ;

Smith, Avery .

JAMA DERMATOLOGY, 2018, 154 (11) :1247-1248

[3] Biases in electronic health record data due to processes within the healthcare system: retrospective observational study [J].

Agniel, Denis ;

Kohane, Isaac S. ;

Weber, Griffin M. .

BMJ-BRITISH MEDICAL JOURNAL, 2018, 361

[4] Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice: Try It, Buy It, Check It [J].

Allen, Bibb ;

Dreyer, Keith ;

Stibolt, Robert ;

Agarwal, Sheela ;

Coombs, Laura ;

Treml, Chris ;

Elkholy, Mona ;

Brink, Laura ;

Wald, Christoph .

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2021, 18 (11) :1489-1496

[5] 2020 ACR Data Science Institute Artificial Intelligence Survey [J].

Allen, Bibb ;

Agarwal, Sheela ;

Coombs, Laura ;

Wald, Christoph ;

Dreyer, Keith .

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2021, 18 (08) :1153-1159

[6] Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care [J].

Alrassi, James ;

Katsufrakis, Peter J. ;

Chandran, Latha .

ACADEMIC MEDICINE, 2021, 96 (01) :37-43

[7] Monitoring Suicidal Patients in Primary Care Using Electronic Health Records [J].

Anderson, Heather D. ;

Pace, Wilson D. ;

Brandt, Elias ;

Nielsen, Rodney D. ;

Allen, Richard R. ;

Libby, Anne M. ;

West, David R. ;

Valuck, Robert J. .

JOURNAL OF THE AMERICAN BOARD OF FAMILY MEDICINE, 2015, 28 (01) :65-71

[8]

[Anonymous], 2019, 2019 VAL AI C NOV 5

[9] On instabilities of deep learning in image reconstruction and the potential costs of AI [J].

Antun, Vegard ;

Renna, Francesco ;

Poon, Clarice ;

Adcock, Ben ;

Hansen, Anders C. .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (48) :30088-30095

[10] Deep learning predicts hip fracture using confounding patient and healthcare variables [J].

Badgeley, Marcus A. ;

Zech, John R. ;

Oakden-Rayner, Luke ;

Glicksberg, Benjamin S. ;

Liu, Manway ;

Gale, William ;

McConnell, Michael, V ;

Percha, Bethany ;

Snyder, Thomas M. ;

Dudley, Joel T. .

NPJ DIGITAL MEDICINE, 2019, 2 (1)

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