The now and future of ChatGPT and GPT in psychiatry

ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning‐based language...

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Vydané v:	Psychiatry and Clinical Neurosciences Ročník 77; číslo 11; s. 592 - 596
Hlavní autori:	Cheng, Szu‐Wei, Chang, Chung‐Wen, Chang, Wan‐Jung, Wang, Hao‐Wei, Liang, Chih‐Sung, Kishimoto, Taishiro, Chang, Jane Pei‐Chen, Kuo, John S., Su, Kuan‐Pin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Australia Wiley 01.11.2023 Wiley Subscription Services, Inc John Wiley & Sons Australia, Ltd
Predmet:	Algorithms Chatbots Deep learning Emotions Empathy Health care Humans Language Medical records Patients Psychiatry Psychotherapy Timely Review deep learning GPT ChatGPT artificial intelligence informatics and telecommunications in psychiatry
ISSN:	1323-1316, 1440-1819, 1440-1819
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Abstract	ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning‐based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully‐automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical ‘real world’ inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT‐related tools in all mental healthcare environments, including telemedicine and academic/training settings.
AbstractList	ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning‐based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully‐automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical ‘real world’ inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT‐related tools in all mental healthcare environments, including telemedicine and academic/training settings. ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning-based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully-automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical 'real world' inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT-related tools in all mental healthcare environments, including telemedicine and academic/training settings.ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning-based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully-automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical 'real world' inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT-related tools in all mental healthcare environments, including telemedicine and academic/training settings. ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning‐based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully‐automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical ‘real world’ inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT‐related tools in all mental healthcare environments, including telemedicine and academic/training settings.
Author	Chung‐Wen Chang Hao‐Wei Wang Chih‐Sung Liang Szu‐Wei Cheng Jane Pei‐Chen Chang John S. Kuo Wan‐Jung Chang Taishiro Kishimoto Kuan‐Pin Su
AuthorAffiliation	9 Neuroscience and Brain Disease Center China Medical University Taichung Taiwan 7 Department of Psychiatry Beitou Branch, Tri‐Service General Hospital, National Defense Medical Center Taipei Taiwan 8 Hills Joint Research Laboratory for Future Preventive Medicine and Wellness Keio University School of Medicine Tokyo Japan 5 Internet of Things Laboratory (IOT Lab) Medical and Intelligent Technology Research Center (MIT Center), Southern Taiwan University of Science and Technology Tainan Taiwan 1 College of Medicine China Medical University Taichung Taiwan 4 Department of Electronic Engineering Southern Taiwan University of Science and Technology Tainan Taiwan 11 An‐Nan Hospital, China Medical University Tainan Taiwan 2 Mind‐Body Interface Laboratory (MBI‐Lab) and Department of Psychiatry China Medical University Hospital Taichung Taiwan 6 Department of Family Studies and Child Development Shih Chien University Taipei Taiwan 3 Department of Information Management Chia‐Nan University of Pharmacy &
AuthorAffiliation_xml	– name: 6 Department of Family Studies and Child Development Shih Chien University Taipei Taiwan – name: 4 Department of Electronic Engineering Southern Taiwan University of Science and Technology Tainan Taiwan – name: 9 Neuroscience and Brain Disease Center China Medical University Taichung Taiwan – name: 5 Internet of Things Laboratory (IOT Lab) Medical and Intelligent Technology Research Center (MIT Center), Southern Taiwan University of Science and Technology Tainan Taiwan – name: 8 Hills Joint Research Laboratory for Future Preventive Medicine and Wellness Keio University School of Medicine Tokyo Japan – name: 11 An‐Nan Hospital, China Medical University Tainan Taiwan – name: 10 Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan – name: 7 Department of Psychiatry Beitou Branch, Tri‐Service General Hospital, National Defense Medical Center Taipei Taiwan – name: 3 Department of Information Management Chia‐Nan University of Pharmacy & Science Tainan Taiwan – name: 1 College of Medicine China Medical University Taichung Taiwan – name: 2 Mind‐Body Interface Laboratory (MBI‐Lab) and Department of Psychiatry China Medical University Hospital Taichung Taiwan
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Cites_doi	10.1016/j.jad.2022.04.005 10.1038/s41746-022-00589-7 10.1016/j.nicl.2021.102584 10.1016/j.biopsycho.2021.108117 10.3389/fdgth.2021.689736 10.1016/j.fmre.2021.11.011 10.1038/s41746-021-00464-x 10.1162/153244303322533223 10.3390/ijerph20021514 10.1016/S2589-7500(23)00019-5 10.1016/j.eij.2021.05.004 10.3389/fpsyt.2022.1025517 10.2196/17204 10.1037/0022-3514.56.3.339 10.1016/S2589-7500(23)00023-7 10.1145/365153.365168 10.1016/0004-3702(71)90002-6 10.31219/osf.io/m6gcn 10.1111/j.1467-6494.2008.00534.x 10.1007/s11023-020-09548-1 10.1126/science.adh2586 10.1001/jamapsychiatry.2022.4076 10.1016/S2589-7500(23)00048-1 10.1016/S2589-7500(23)00021-3 10.1037/pst0000175 10.1176/appi.ps.202100692
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Copyright	2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology. 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.
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References	e_1_2_10_23_1 Aboraya A (e_1_2_10_35_1) 2006; 3 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_22_1 e_1_2_10_43_1 Radford A (e_1_2_10_16_1) 2019; 1 e_1_2_10_42_1 Ouyang L (e_1_2_10_19_1) 2022; 35 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_40_1 (e_1_2_10_6_1) 1949 Amin MM (e_1_2_10_38_1) 2023; 38 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_39_1 e_1_2_10_5_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_7_1 e_1_2_10_15_1 Brown T (e_1_2_10_17_1) 2020; 33 e_1_2_10_36_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_33_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 Kosinski M (e_1_2_10_37_1) 2023 Kumar S (e_1_2_10_28_1) 2007; 6 Sutskever I (e_1_2_10_10_1) 2014; 27 Vaswani A (e_1_2_10_12_1) 2017; 30 e_1_2_10_29_1 Chintagunta B (e_1_2_10_30_1) 2021; 2021 e_1_2_10_27_1 e_1_2_10_25_1 e_1_2_10_26_1
References_xml	– ident: e_1_2_10_27_1 doi: 10.1016/j.jad.2022.04.005 – ident: e_1_2_10_33_1 – ident: e_1_2_10_34_1 doi: 10.1038/s41746-022-00589-7 – ident: e_1_2_10_13_1 – ident: e_1_2_10_21_1 doi: 10.1016/j.nicl.2021.102584 – volume: 2021 start-page: 66 year: 2021 ident: e_1_2_10_30_1 article-title: Medically aware GPT‐3 as a data generator for medical dialogue summarization publication-title: NLPMC – ident: e_1_2_10_22_1 doi: 10.1016/j.biopsycho.2021.108117 – ident: e_1_2_10_31_1 doi: 10.3389/fdgth.2021.689736 – ident: e_1_2_10_24_1 doi: 10.1016/j.fmre.2021.11.011 – ident: e_1_2_10_32_1 doi: 10.1038/s41746-021-00464-x – volume: 35 start-page: 27730 year: 2022 ident: e_1_2_10_19_1 article-title: Training language models to follow instructions with human feedback publication-title: Adv. Neural Inf. Process. Syst – ident: e_1_2_10_9_1 doi: 10.1162/153244303322533223 – ident: e_1_2_10_42_1 doi: 10.3390/ijerph20021514 – ident: e_1_2_10_2_1 doi: 10.1016/S2589-7500(23)00019-5 – ident: e_1_2_10_40_1 doi: 10.1016/j.eij.2021.05.004 – volume: 38 start-page: 23 year: 2023 ident: e_1_2_10_38_1 article-title: Will affective computing emerge from foundation models and general AI? A first evaluation on ChatGPT publication-title: ArXiv – ident: e_1_2_10_44_1 doi: 10.3389/fpsyt.2022.1025517 – start-page: 15 volume-title: Machine Translation of Languages year: 1949 ident: e_1_2_10_6_1 – ident: e_1_2_10_45_1 doi: 10.2196/17204 – volume: 33 start-page: 1877 year: 2020 ident: e_1_2_10_17_1 article-title: Language models are few‐shot learners publication-title: Adv. Neural Inf. Process. Syst – ident: e_1_2_10_43_1 doi: 10.1037/0022-3514.56.3.339 – ident: e_1_2_10_5_1 doi: 10.1016/S2589-7500(23)00023-7 – ident: e_1_2_10_7_1 doi: 10.1145/365153.365168 – ident: e_1_2_10_8_1 doi: 10.1016/0004-3702(71)90002-6 – ident: e_1_2_10_25_1 doi: 10.31219/osf.io/m6gcn – ident: e_1_2_10_26_1 – ident: e_1_2_10_20_1 – ident: e_1_2_10_39_1 doi: 10.1111/j.1467-6494.2008.00534.x – ident: e_1_2_10_18_1 doi: 10.1007/s11023-020-09548-1 – year: 2023 ident: e_1_2_10_37_1 article-title: Theory of mind may have spontaneously emerged in large language models publication-title: ArXiv – ident: e_1_2_10_29_1 doi: 10.1126/science.adh2586 – ident: e_1_2_10_23_1 doi: 10.1001/jamapsychiatry.2022.4076 – volume: 6 start-page: 186 year: 2007 ident: e_1_2_10_28_1 article-title: Burnout in psychiatrists publication-title: World Psychiatry – ident: e_1_2_10_3_1 doi: 10.1016/S2589-7500(23)00048-1 – ident: e_1_2_10_15_1 – volume: 1 start-page: 9 year: 2019 ident: e_1_2_10_16_1 article-title: Language models are unsupervised multitask learners publication-title: OpenAI Blog – volume: 30 start-page: 6000 year: 2017 ident: e_1_2_10_12_1 article-title: Attention is all you need publication-title: Adv. Neural. Inf. Process Sys – ident: e_1_2_10_14_1 – ident: e_1_2_10_4_1 doi: 10.1016/S2589-7500(23)00021-3 – volume: 27 start-page: 3104 year: 2014 ident: e_1_2_10_10_1 article-title: Sequence to sequence learning with neural networks publication-title: Adv. Neural. Inf. Process Sys – volume: 3 start-page: 41 year: 2006 ident: e_1_2_10_35_1 article-title: The reliability of psychiatric diagnosis revisited: The Clinician's guide to improve the reliability of psychiatric diagnosis publication-title: Psychiatry (Edgmont) – ident: e_1_2_10_36_1 doi: 10.1037/pst0000175 – ident: e_1_2_10_41_1 doi: 10.1176/appi.ps.202100692 – ident: e_1_2_10_11_1
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Snippet	ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of...
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SubjectTerms	Algorithms Chatbots Deep learning Emotions Empathy Health care Humans Language Medical records Patients Psychiatry Psychotherapy Timely Review
Title	The now and future of ChatGPT and GPT in psychiatry
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