Artificial intelligence in psychiatry research, diagnosis, and therapy

被引:42
作者
Sun, Jie [1 ,2 ]
Dong, Qun-Xi [3 ]
Wang, San-Wang [2 ,4 ]
Zheng, Yong-Bo [2 ,5 ,6 ]
Liu, Xiao-Xing [2 ]
Lu, Tang-Sheng [7 ,8 ]
Yuan, Kai [2 ]
Shi, Jie [7 ,8 ]
Hu, Bin [3 ]
Lu, Lin [2 ,5 ,6 ,9 ]
Han, Ying [7 ,8 ]
机构
[1] Peking Univ Third Hosp, Pain Med Ctr, Beijing 100191, Peoples R China
[2] Peking Univ, Peking Univ Sixth Hosp, Inst Mental Hlth, NHC Key Lab Mental Hlth,Natl Clin Res Ctr Mental D, Beijing 100191, Peoples R China
[3] Beijing Inst Technol, Sch Med Technol, 5 Zhongguancun South, Beijing 100081, Peoples R China
[4] Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan 430060, Peoples R China
[5] Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing 100871, Peoples R China
[6] Peking Univ, PKU IDG McGovern Inst Brain Res, Beijing 100871, Peoples R China
[7] Peking Univ, Natl Inst Drug Dependence, Beijing 100191, Peoples R China
[8] Peking Univ, Beijing Key Lab Drug Dependence Res, Beijing 100191, Peoples R China
[9] Peking Univ Sixth Hosp, Inst Mental Hlth, 51 Huayuanbei Rd, Beijing 100191, Peoples R China
关键词
Psychiatric disorders; Artificial intelligence; Diagnosis; Prognosis; Treatment; MENTAL-HEALTH; ALZHEIMERS-DISEASE; NEURAL-NETWORKS; SLEEP-APNEA; SCHIZOPHRENIA; PREDICTION; CLASSIFICATION; PSYCHOLOGY; FRAMEWORK; CHATBOTS;
D O I
10.1016/j.ajp.2023.103705
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
引用
收藏
页数:12
相关论文
共 134 条
  • [1] Machine learning approach for early detection of autism by combining questionnaire and home video screening
    Abbas, Halim
    Garberson, Ford
    Glover, Eric
    Wall, Dennis P.
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2018, 25 (08) : 1000 - 1007
  • [2] Use of a machine learning framework to predict substance use disorder treatment success
    Acion, Laura
    Kelmansky, Diana
    van der Laan, Mark
    Sahker, Ethan
    Jones, DeShauna
    Arndt, Stephan
    [J]. PLOS ONE, 2017, 12 (04):
  • [3] Afshar M, 2022, LANCET DIGIT HEALTH, V4, pE426, DOI 10.1016/S2589-7500(22)00041-3
  • [4] Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression
    Allesoe, Rosa Lundbye
    Nudel, Ron
    Thompson, Wesley K.
    Wang, Yunpeng
    Nordentoft, Merete
    Borglum, Anders D.
    Hougaard, David M.
    Werge, Thomas
    Rasmussen, Simon
    Benros, Michael Eriksen
    [J]. SCIENCE ADVANCES, 2022, 8 (26):
  • [5] Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning
    Alshaer, Hisham
    Hummel, Richard
    Mendelson, Monique
    Marshal, Travis
    Bradley, T. Douglas
    [J]. JOURNAL OF CLINICAL SLEEP MEDICINE, 2019, 15 (03): : 463 - 470
  • [6] Asgari Meysam, 2017, Alzheimers Dement (N Y), V3, P219, DOI 10.1016/j.trci.2017.01.006
  • [7] Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism
    Bahado-Singh, Ray O.
    Vishweswaraiah, Sangeetha
    Aydas, Buket
    Mishra, Nitish K.
    Yilmaz, Ali
    Guda, Chittibabu
    Radhakrishna, Uppala
    [J]. BRAIN RESEARCH, 2019, 1724
  • [8] Relapse prediction in schizophrenia through digital phenotyping: a pilot study
    Barnett, Ian
    Torous, John
    Staples, Patrick
    Sandoval, Luis
    Keshavan, Matcheri
    Onnela, Jukka-Pekka
    [J]. NEUROPSYCHOPHARMACOLOGY, 2018, 43 (08) : 1660 - 1666
  • [9] MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
    Bashyam, Vishnu M.
    Erus, Guray
    Doshi, Jimit
    Habes, Mohamad
    Nasralah, Ilya
    Truelove-Hill, Monica
    Srinivasan, Dhivya
    Mamourian, Liz
    Pomponio, Raymond
    Fan, Yong
    Launer, Lenore J.
    Masters, Colin L.
    Maruff, Paul
    Zhuo, Chuanjun
    Volzke, Henry
    Johnson, Sterling C.
    Fripp, Jurgen
    Koutsouleris, Nikolaos
    Satterthwaite, Theodore D.
    Wolf, Daniel
    Gur, Raquel E.
    Gur, Ruben C.
    Morris, John
    Albert, Marilyn S.
    Grabe, Hans J.
    Resnick, Susan
    Bryan, R. Nick
    Wolk, David A.
    Shou, Haochang
    Davatzikos, Christos
    [J]. BRAIN, 2020, 143 : 2312 - 2324
  • [10] CrossCheck: Integrating Self- Report, Behavioral Sensing, and Smartphone Use to Identify Digital Indicators of Psychotic Relapse
    Ben-Zeev, Dror
    Brian, Rachel
    Wang, Rui
    Wang, Weichen
    Campbell, Andrew T.
    Aung, Min S. H.
    Merrill, Michael
    Tseng, Vincent W. S.
    Choudhury, Tanzeem
    Hauser, Marta
    Kane, John M.
    Scherer, Emily A.
    [J]. PSYCHIATRIC REHABILITATION JOURNAL, 2017, 40 (03) : 266 - 275