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Cited by 94Mental Health via WritingArtificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

The Applications of Large Language Models in Mental Health: Scoping Review

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Jin, Yu, Liu, Jiayi, Li, Pan, Wang, Baosen, Yan, Yangxinyu, Zhang, Huilin, Ni, Chenhao, Wang, Jing +3 more·2025·Journal of Medical Internet Research
DOI 10.2196/69284PMC PMC12089884

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Abstract

Background: Mental health is emerging as an increasingly prevalent public issue globally. There is an urgent need in mental health for efficient detection methods, effective treatments, affordable privacy-focused health care solutions, and increased access to specialized psychiatrists. The emergence and rapid development of large language models (LLMs) have shown the potential to address these mental health demands. However, a comprehensive review summarizing the application areas, processes, and performance comparisons of LLMs in mental health has been lacking until now. Objective: This review aimed to summarize the applications of LLMs in mental health, including trends, application areas, performance comparisons, challenges, and prospective future directions. Methods: A scoping review was conducted to map the landscape of LLMs’ applications in mental health, including trends, application areas, comparative performance, and future trajectories. We searched 7 electronic databases, including Web of Science, PubMed, Cochrane Library, IEEE Xplore, Weipu, CNKI, and Wanfang, from January 1, 2019, to August 31, 2024. Studies eligible for inclusion were peer-reviewed articles focused on LLMs’ applications in mental health. Studies were excluded if they (1) were not peer-reviewed or did not focus on mental health or mental disorders or (2) did not use LLMs; studies that used only natural language processing or long short-term memory models were also excluded. Relevant information on application details and performance metrics was extracted during the data charting of eligible articles. Results: A total of 95 articles were drawn from 4859 studies using LLMs for mental health tasks. The applications were categorized into 3 key areas: screening or detection of mental disorders (67/95, 71%), supporting clinical treatments and interventions (31/95, 33%), and assisting in mental health counseling and education (11/95, 12%). Most studies used LLMs for depression detection and classification (33/95, 35%), clinical treatment support and intervention (14/95, 15%), and suicide risk prediction (12/95, 13%). Compared with nontransformer models and humans, LLMs demonstrate higher capabilities in information acquisition and analysis and efficiently generating natural language responses. Various series of LLMs also have different advantages and disadvantages in addressing mental health tasks. Conclusions: This scoping review synthesizes the applications, processes, performance, and challenges of LLMs in the mental health field. These findings highlight the substantial potential of LLMs to augment mental health research, diagnostics, and intervention strategies, underscoring the imperative for ongoing development and ethical deliberation in clinical settings.

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  • Existing reviews and meta-analyses of LLM-CBs for mental health focus on applications of LLM-CBs as clinical intervention tools, often under research-protocol conditions.

    “Although reviews and meta-analyses of LLM-CBs for mental health exist13,14,29,30, as well as scoping reviews on the implementation of LLM-CBs in mental health31,32, their focus is narrowed to applications of LLM-CBs as clinical intervention tools.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [32]

    The Results section (Categorization of Studies Based on Mental Health Applications; page unavailable) says this scoping review covers screening and detection, clinical treatment and intervention, and counseling and education. Screening or detection appears in 67 of 95 studies, versus 31 of 95 for treatment and intervention, contrary to the claim that the review's focus is narrowed to clinical intervention tools. The cited passages do not establish the additional characterization about research-protocol conditions. (LLM, conf=0.89)

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  1. [1]Carswell K Cuijpers P Gray B Kestel D Malik A Weissbecker I van Ommeren M WHO recommendations on psychological interventions for mental disorders Lancet Psychiatry 2024 09 11 9 678 9 10.1016/S2215-0366(24)00220-7 39067470 S2215-0366(24)00220-7 39067470 · DOI
  2. [2]Sacco R Camilleri N Eberhardt J Umla-Runge K Newbury-Birch D A systematic review and meta-analysis on the prevalence of mental disorders among children and adolescents in Europe Eur Child Adolesc Psychiatry 2024 09 33 9 2877 94 10.1007/s00787-022-02131-2 36581685 10.1007/s00787-022-02131-2 36581685 PMC9800241 · DOI
  3. [3]Brohan E Chowdhary N Dua T Barbui C Thornicroft G Kestel D The WHO Mental Health Gap Action Programme for mental, neurological, and substance use conditions: the new and updated guideline recommendations Lancet Psychiatry 2024 02 11 2 155 8 10.1016/S2215-0366(23)00370-X 37980915 S2215-0366(23)00370-X 37980915 · DOI
  4. [4]Flett GL Hewitt PL The need to focus on perfectionism in suicide assessment, treatment and prevention World Psychiatry 2024 02 12 23 1 152 4 10.1002/wps.21157 38214627 38214627 PMC10785971 · DOI
  5. [6]Brown TB Mann B Ryder N Subbiah M Kaplan J Dhariwal P Neelakantan A Shyam P Sastry G Askell A Agarwal S Herbert-Voss A Krueger G Henighan T Child R Ramesh A Ziegler DM Wu J Winter C Hesse C Chen M Sigler E Litwin M Gray S Chess B Clark J Berner C McCandlish S Radford A Sutskever I Amodei D Language models are few-shot learners arXiv Preprint posted online on May 28, 2020
  6. [8]Touvron H Lavril T Izacard G Martinet X Lachaux MA Lacroix T Rozière B Goyal N Hambro E Azhar F Rodriguez A Joulin A Grave E Lample G LLaMA: open and efficient foundation language models arXiv Preprint posted online on February 27, 2023
  7. [9]Zhao WX Zhou K Li J Tang T Wang X Hou Y Min Y Zhang B Zhang J Dong Z Du Y Yang C Chen Y Chen Z Jiang J Ren R Li Y Tang X Liu Z Liu P Nie JY Wen JRC A survey of large language models arXiv Preprint posted online on March 31, 2023
  8. [11]Gao J Lin CY Introduction to the special issue on statistical language modeling ACM Trans Asian Lang Inf Process 2004 06 3 2 87 93 10.1145/1034780.1034781 · DOI
  9. [12]Kombrink S Mikolov T Karafiát M Burget L Recurrent neural network based language modeling in meeting recognition Proceedings of the Interspeech 2011 2011 Interspeech 2011 August 27-31, 2011 Florence, Italy 10.21437/interspeech.2011-720 · DOI
  10. [14]Beredo JL Ong EC A hybrid response generation model for an empathetic conversational agent Proceedings of the 2022 International Conference on Asian Language Processing 2022 IALP 2022 October 27-28, 2022 Singapore, Singapore 10.1109/ialp57159.2022.9961311 · DOI
  11. [15]Wu Y Chen J Mao K Zhang Y Automatic post-traumatic stress disorder diagnosis via clinical transcripts: a novel text augmentation with large language models Proceedings of the 2023 IEEE Biomedical Circuits and Systems Conference 2023 BioCAS 2023 October 19-21, 2023 Toronto, ON 10.1109/biocas58349.2023.10388714 · DOI · Paper page
  12. [17]Lee C Mohebbi M O'Callaghan E Winsberg M Large language models versus expert clinicians in crisis prediction among telemental health patients: comparative study JMIR Ment Health 2024 08 02 11 e58129 10.2196/58129 38876484 v11i1e58129 38876484 PMC11329850 · DOI
  13. [18]Jeon E Yoon N Sohn SY Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa Technol Forecast Soc Change 2023 01 186 122130 10.1016/j.techfore.2022.122130 · DOI
  14. [19]Mármol-Romero AM García-Vega M García-Cumbreras MÁ Montejo-Ráez A An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers Int J Hum Comput Interact 2024 05 08 41 7 1 17 10.1080/10447318.2024.2344355 · DOI
  15. [20]Elyoseph Z Levkovich I Shinan-Altman S Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public Fam Med Community Health 2024 01 09 12 Suppl 1 e002583 10.1136/fmch-2023-002583 38199604 fmch-2023-002583 38199604 PMC10806564 · DOI
  16. [21]Perlis RH Goldberg JF Ostacher MJ Schneck CD Clinical decision support for bipolar depression using large language models Neuropsychopharmacology 2024 08 13 49 9 1412 6 10.1038/s41386-024-01841-2 38480911 10.1038/s41386-024-01841-2 38480911 PMC11251032 · DOI
  17. [22]He W Zhang W Jin Y Zhou Q Zhang H Xia Q Physician versus large language model chatbot responses to web-based questions from autistic patients in Chinese: cross-sectional comparative analysis J Med Internet Res 2024 04 30 26 e54706 10.2196/54706 38687566 v26i1e54706 38687566 PMC11094593 · DOI
  18. [23]Metzler H Baginski H Niederkrotenthaler T Garcia D Detecting potentially harmful and protective suicide-related content on Twitter: machine learning approach J Med Internet Res 2022 08 17 24 8 e34705 10.2196/34705 35976193 v24i8e34705 35976193 PMC9434391 · DOI
  19. [24]Xu X Yao B Dong Y Gabriel S Yu H Hendler J Ghassemi M Dey AK Wang D Mental-LLM: leveraging large language models for mental health prediction via online text data Proc ACM Interact Mob Wearable Ubiquitous Technol 2024 03 06 8 1 1 32 10.1145/3643540 39925940 31 PMC11806945 39925940 · DOI · Paper page
  20. [25]Hua Y Liu F Yang K Li Z Na H Sheu YH Zhou P Moran LV Ananiadou S Beam A Torous J Large language models in mental health care: a scoping review arXiv Preprint posted online on January 1, 2024
  21. [27]The applications of large language models in mental health: a scoping review OSF Registries 2024 2025-04-22 https://osf.io/yg9xj 40324177
  22. [30]Yang K Zhang T Kuang Z Xie Q Huang J Ananiadou S MentaLLaMA: interpretable mental health analysis on social media with large language models Proceedings of the ACM Web Conference 2024 2024 WWW '24 May 13-17, 2024 Singapore, Singapore 10.1145/3589334.3648137 · DOI
  23. [31]Vajre V Naylor M Kamath U Shehu A PsychBERT: a mental health language model for social media mental health behavioral analysis Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine 2021 BIBM 2021 December 09-12, 2021 Houston, TX 10.1109/bibm52615.2021.9669469 · DOI
  24. [32]Zhang Y Lyu H Liu Y Zhang X Wang Y Luo J Monitoring depression trends on Twitter during the COVID-19 pandemic: observational study JMIR Infodemiology 2021 1 1 e26769 10.2196/26769 34458682 v1i1e26769 34458682 PMC8330892 · DOI
  25. [33]Suri M Semwal N Chaudhary D Gorton I Kumar B I don’t feel so good! Detecting depressive tendencies using transformer-based multimodal frameworks Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing 2022 MLNLP '22 December 23-25, 2022 Sanya, China 10.1145/3578741.3578817 · DOI
  26. [34]Guo Y Liu J Wang L Qin W Hao S Hong R A prompt-based topic-modeling method for depression detection on low-resource data IEEE Trans Comput Soc Syst 2024 2 11 1 1430 9 10.1109/tcss.2023.3260080 · DOI
  27. [35]Abilkaiyrkyzy A Laamarti F Hamdi M Saddik AE Dialogue system for early mental illness detection: toward a digital twin solution IEEE Access 2024 12 2007 24 10.1109/access.2023.3348783 · DOI
  28. [36]Abdullah M Negied N Detection and prediction of future mental disorder from social media data using machine learning, ensemble learning, and large language models IEEE Access 2024 12 120553 69 10.1109/access.2024.3406469 · DOI
  29. [37]Tao Y Yang M Shen H Yang Z Weng Z Hu B Classifying anxiety and depression through LLMs virtual interactions: a case study with ChatGPT Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine 2023 BIBM 2023 December 5-8, 2023 Istanbul, Turkiye 10.1109/bibm58861.2023.10385305 · DOI
  30. [38]Sood P Yang X Wang P Enhancing depression detection from narrative interviews using language models Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine 2023 BIBM 2023 December 5-8, 2023 Istanbul, Turkiye 10.1109/bibm58861.2023.10385480 · DOI
  31. [39]Lu KC Thamrin SA Chen AL Depression detection via conversation turn classification Multimed Tools Appl 2023 04 01 82 25 39393 413 10.1007/s11042-023-15103-8 · DOI
  32. [40]Bokolo BG Liu Q Deep learning-based depression detection from social media: comparative evaluation of ML and transformer techniques Electronics 2023 10 24 12 21 4396 10.3390/electronics12214396 · DOI
  33. [41]Hayati MF Ali MA Rosli AN Depression detection on Malay dialects using GPT-3 Proceedings of the 2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences 2022 IECBES 2022 December 7-9, 2022 Kuala Lumpur, Malaysia 10.1109/iecbes54088.2022.10079554 · DOI
  34. [42]Danner M Hadzic B Gerhardt S Ludwig S Uslu I Shao P Advancing mental health diagnostics: GPT-based method for depression detection Proceedings of the 62nd Annual Conference of the Society of Instrument and Control Engineers 2023 SICE 2023 September 06-09, 2023 Tsu, Japan 10.23919/sice59929.2023.10354236 · DOI
  35. [43]Wang X Chen S Li T Li W Zhou Y Zheng J Chen Q Yan J Tang B Depression risk prediction for Chinese microblogs via deep-learning methods: content analysis JMIR Med Inform 2020 07 29 8 7 e17958 10.2196/17958 32723719 v8i7e17958 32723719 PMC7424493 · DOI
  36. [44]Owen D Antypas D Hassoulas A Pardiñas AF Espinosa-Anke L Collados JC Enabling early health care intervention by detecting depression in users of web-based forums using language models: longitudinal analysis and evaluation JMIR AI 2023 03 24 2 e41205 10.2196/41205 37525646 37525646 PMC7614849 · DOI
  37. [45]Senn S Tlachac ML Flores R Rundensteiner E Ensembles of BERT for depression classification Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society 2022 EMBC 2022 July 11-15, 2022 Glasgow, UK 10.1109/embc48229.2022.9871120 36085764 · DOI
  38. [47]Sadeghi M Egger B Agahi R Richer R Capito K Rupp LH Exploring the capabilities of a language model-only approach for depression detection in text data Proceedings of the IEEE EMBS International Conference on Biomedical and Health Informatics 2023 BHI 2023 October 15-18, 2023 Pittsburgh, PA 10.1109/bhi58575.2023.10313367 · DOI
  39. [48]Farruque N Goebel R Sivapalan S Zaïane OR Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach Lang Resour Eval 2024 04 04 58 3 1013 41 10.1007/s10579-024-09720-4 · DOI
  40. [49]Tey W Goh H Lim AH Phang C Pre- and post-depressive detection using deep learning and textual-based features Int J Technol 2023 10 31 14 6 1334 43 10.14716/ijtech.v14i6.6648 · DOI
  41. [50]de Hond A van Buchem M Fanconi C Roy M Blayney D Kant I Steyerberg E Hernandez-Boussard T Predicting depression risk in patients with cancer using multimodal data: algorithm development study JMIR Med Inform 2024 01 18 12 e51925 10.2196/51925 38236635 v12i1e51925 38236635 PMC10835583 · DOI
  42. [51]Ilias L Mouzakitis S Askounis D Calibration of transformer-based models for identifying stress and depression in social media IEEE Trans Comput Soc Syst 2024 4 11 2 1979 90 10.1109/tcss.2023.3283009 · DOI
  43. [52]Levkovich I Elyoseph Z Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians Fam Med Community Health 2023 09 16 11 4 e002391 10.1136/fmch-2023-002391 37844967 fmch-2023-002391 37844967 PMC10582915 · DOI
  44. [53]Toto E Tlachac ML Rundensteiner EA AudiBERT: a deep transfer learning multimodal classification framework for depression screening Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 CIKM '21 November 1-5, 2021 Virtual Event 10.1145/3459637.3481895 · DOI
  45. [54]Lau C Zhu X Chan WY Automatic depression severity assessment with deep learning using parameter-efficient tuning Front Psychiatry 2023 6 15 14 1160291 10.3389/fpsyt.2023.1160291 37398577 37398577 PMC10308283 · DOI
  46. [55]Verma S Vishal Joshi RC Dutta MK Jezek S Burget R AI-enhanced mental health diagnosis: leveraging transformers for early detection of depression tendency in textual data Proceedings of the 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops 2023 ICUMT 2023 October 30-November 1, 2023 Ghent, Belgium 10.1109/icumt61075.2023.10333301 · DOI
  47. [56]Pourkeyvan A Safa R Sorourkhah A Harnessing the power of hugging face transformers for predicting mental health disorders in social networks IEEE Access 2024 12 28025 35 10.1109/access.2024.3366653 · DOI
  48. [57]Englhardt Z Ma C Morris ME Chang CC Xu X Qin L McDuff D Liu X Patel S Iyer V From classification to clinical insights: towards analyzing and reasoning about mobile and behavioral health data with large language models Proc ACM Interact Mob Wearable Ubiquitous Technol 2024 05 15 8 2 1 25 10.1145/3659604 · DOI
  49. [58]Firoz N Berestneva O Aksyonov SV Dual layer Cogni - insight deep-mood encoder: a two- tiered approach for depression detection Proceedings of the International Russian Smart Industry Conference 2024 SmartIndustryCon 2024 March 25-29, 2024 Sochi, Russian Federation 10.1109/smartindustrycon61328.2024.10516113 · DOI
  50. [59]Huang L A study on the design of a depression diagnostic framework based on DepGPT and neural networks Proceedings of the 2nd International Conference on Mechatronics, IoT and Industrial Informatics 2024 ICMIII 2024 June 12-14, 2024 Melbourne, Australia 10.1109/icmiii62623.2024.00137 · DOI
  51. [60]Shin D Kim H Lee S Cho Y Jung W Using large language models to detect depression from user-generated diary text data as a novel approach in digital mental health screening: instrument validation study J Med Internet Res 2024 09 18 26 e54617 10.2196/54617 39292502 v26i1e54617 39292502 PMC11447422 · DOI
  52. [61]Qorich M El Ouazzani R Advanced deep learning and large language models for suicide ideation detection on social media Prog Artif Intell 2024 06 21 13 2 135 47 10.1007/s13748-024-00326-z · DOI
  53. [62]Howard D Maslej MM Lee J Ritchie J Woollard G French L Transfer learning for risk classification of social media posts: model evaluation study J Med Internet Res 2020 05 13 22 5 e15371 10.2196/15371 32401222 v22i5e15371 32401222 PMC7254287 · DOI
  54. [63]Levkovich I Elyoseph Z Suicide risk assessments through the eyes of ChatGPT-3.5 versus ChatGPT-4: vignette study JMIR Ment Health 2023 09 20 10 e51232 10.2196/51232 37728984 v10i1e51232 37728984 PMC10551796 · DOI
  55. [64]Ghanadian H Nejadgholi I Osman HA Socially aware synthetic data generation for suicidal ideation detection using large language models IEEE Access 2024 12 14350 63 10.1109/access.2024.3358206 · DOI
  56. [65]Diniz EJ Fontenele JE de Oliveira AC Bastos VH Teixeira S Rabêlo RL Calçada DB Dos Santos RM de Oliveira AK Teles AS Boamente: a natural language processing-based digital phenotyping tool for smart monitoring of suicidal ideation Healthcare (Basel) 2022 04 08 10 4 698 10.3390/healthcare10040698 35455874 healthcare10040698 35455874 PMC9029735 · DOI
  57. [66]Wang R Yang BX Ma Y Wang P Yu Q Zong X Huang Z Ma S Hu L Hwang K Liu Z Medical-level suicide risk analysis: a novel standard and evaluation model IEEE Internet Things J 2021 12 1 8 23 16825 34 10.1109/jiot.2021.3052363 · DOI
  58. [67]Bauer B Norel R Leow A Rached ZA Wen B Cecchi G Using large language models to understand suicidality in a social media-based taxonomy of mental health disorders: linguistic analysis of Reddit posts JMIR Ment Health 2024 05 16 11 e57234 10.2196/57234 38771256 v11i1e57234 38771256 PMC11112053 · DOI
  59. [68]Kumar A Trueman TE Cambria E Stress identification in online social networks Proceedings of the IEEE International Conference on Data Mining Workshops 2022 ICDMW 2022 November 28-December 1, 2022 Orlando, FL 10.1109/icdmw58026.2022.00063 · DOI
  60. [69]Jain B Goyal G Sharma M Evaluating emotional detection and classification capabilities of GPT-2 and GPT-neo using textual data Proceedings of the 14th International Conference on Cloud Computing, Data Science & Engineering 2024 Confluence 2024 January 18-19, 2024 Noida, India 10.1109/confluence60223.2024.10463396 · DOI
  61. [70]Teferra BG Rose J Predicting generalized anxiety disorder from impromptu speech transcripts using context-aware transformer-based neural networks: model evaluation study JMIR Ment Health 2023 03 28 10 e44325 10.2196/44325 36976636 v10i1e44325 36976636 PMC10131846 · DOI
  62. [71]Kim J Leonte KG Chen ML Torous JB Linos E Pinto A Rodriguez CI Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder NPJ Digit Med 2024 07 19 7 1 193 10.1038/s41746-024-01181-x 39030292 10.1038/s41746-024-01181-x 39030292 PMC11271579 · DOI
  63. [72]Gargari OK Fatehi F Mohammadi I Firouzabadi SR Shafiee A Habibi G Diagnostic accuracy of large language models in psychiatry Asian J Psychiatr 2024 10 100 104168 10.1016/j.ajp.2024.104168 39111087 S1876-2018(24)00261-2 39111087 · DOI
  64. [73]Wang Y Yu Y Liu Y Ma Y Pang PC Predicting patients' satisfaction with mental health drug treatment using their reviews: unified interchangeable model fusion approach JMIR Ment Health 2023 12 05 10 e49894 10.2196/49894 38051580 v10i1e49894 38051580 PMC10731562 · DOI
  65. [74]Hadar-Shoval D Elyoseph Z Lvovsky M The plasticity of ChatGPT's mentalizing abilities: personalization for personality structures Front Psychiatry 2023 9 1 14 1234397 10.3389/fpsyt.2023.1234397 37720897 37720897 PMC10503434 · DOI
  66. [75]Tanana MJ Soma CS Kuo PB Bertagnolli NM Dembe A Pace BT Srikumar V Atkins DC Imel ZE How do you feel? Using natural language processing to automatically rate emotion in psychotherapy Behav Res Methods 2021 10 22 53 5 2069 82 10.3758/s13428-020-01531-z 33754322 10.3758/s13428-020-01531-z 33754322 PMC8455714 · DOI
  67. [76]Wan C Ge X Wang J Zhang X Yu Y Hu J Liu Y Ma H Identification and impact analysis of family history of psychiatric disorder in mood disorder patients with pretrained language model Front Psychiatry 2022 5 20 13 861930 10.3389/fpsyt.2022.861930 35669265 35669265 PMC9163373 · DOI
  68. [77]Dai HJ Su CH Lee YQ Zhang YC Wang CK Kuo CJ Wu CS Deep learning-based natural language processing for screening psychiatric patients Front Psychiatry 2020 1 15 11 533949 10.3389/fpsyt.2020.533949 33584354 33584354 PMC7874001 · DOI
  69. [78]Gargari OK Habibi G Nilchian N Shafiee A Comparative analysis of large language models in psychiatry and mental health: a focus on GPT, AYA, and Nemotron-3-8B Asian J Psychiatr 2024 09 99 104148 10.1016/j.ajp.2024.104148 39047354 S1876-2018(24)00241-7 39047354 · DOI
  70. [79]Rathje S Mirea DM Sucholutsky I Marjieh R Robertson CE Van Bavel JJ GPT is an effective tool for multilingual psychological text analysis Proc Natl Acad Sci U S A 2024 08 20 121 34 e2308950121 10.1073/pnas.2308950121 39133853 39133853 PMC11348013 · DOI · Paper page
  71. [80]Lossio-Ventura JA Weger R Lee AY Guinee EP Chung J Atlas L Linos E Pereira F A comparison of ChatGPT and fine-tuned open pre-trained transformers (OPT) against widely used sentiment analysis tools: sentiment analysis of COVID-19 survey data JMIR Ment Health 2024 01 25 11 e50150 10.2196/50150 38271138 v11i1e50150 38271138 PMC10813836 · DOI
  72. [81]Stigall W Khan MA Attota D Nweke F Pei Y Large language models performance comparison of emotion and sentiment classification Proceedings of the 2024 ACM Southeast Conference 2024 ACMSE '24 April 18-20, 2024 Marietta, GA 10.1145/3603287.3651183 · DOI
  73. [82]Yongsatianchot N Torshizi PG Marsella S Investigating large language models’ perception of emotion using appraisal theory Proceedings of the 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos 2023 ACIIW 2023 September 10-13, 2023 Cambridge, MA 10.1109/aciiw59127.2023.10388194 · DOI
  74. [83]Goyal T Rajeshbai DH Gopalkrishna N M T HR M Mobile machine learning models for emotion and sarcasm detection in text: a solution for alexithymic individuals Proceedings of the 3rd International Conference for Innovation in Technology 2024 INOCON 2024 March 01-03, 2024 Bangalore, India 10.1109/inocon60754.2024.10511772 · DOI
  75. [84]Elyoseph Z Refoua E Asraf K Lvovsky M Shimoni Y Hadar-Shoval D Capacity of generative AI to interpret human emotions from visual and textual data: pilot evaluation study JMIR Ment Health 2024 02 06 11 e54369 10.2196/54369 38319707 v11i1e54369 38319707 PMC10879976 · DOI
  76. [85]Fan Y Nie J Sun X Jiang X Exploring foundation models in detecting concerning daily functioning in psychotherapeutic context based on images from smart home devices Proceedings of the IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things 2024 FMSys 2024 May 13-15, 2024 Hong Kong 10.1109/fmsys62467.2024.00012 · DOI
  77. [86]Yang X Chen A PourNejatian N Shin HC Smith KE Parisien C Compas C Martin C Costa AB Flores MG Zhang Y Magoc T Harle CA Lipori G Mitchell DA Hogan WR Shenkman EA Bian J Wu Y A large language model for electronic health records NPJ Digit Med 2022 12 26 5 1 194 10.1038/s41746-022-00742-2 36572766 10.1038/s41746-022-00742-2 36572766 PMC9792464 · DOI · Paper page
  78. [87]Huang S Fu F Yang K Zhang K Yang F Empowerment of large language models in psychological counseling through prompt engineering Proceedings of the 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence 2024 SEAI 2024 June 21-23, 2024 Xiamen, China 10.1109/seai62072.2024.10674052 · DOI
  79. [88]Adhikary PK Srivastava A Kumar S Singh SM Manuja P Gopinath JK Krishnan V Gupta SK Deb KS Chakraborty T Exploring the efficacy of large language models in summarizing mental health counseling sessions: benchmark study JMIR Ment Health 2024 07 23 11 e57306 10.2196/57306 39042893 v11i1e57306 39042893 PMC11303879 · DOI
  80. [89]Hodson N Williamson S Can large language models replace therapists? Evaluating performance at simple cognitive behavioral therapy tasks JMIR AI 2024 07 30 3 e52500 10.2196/52500 39078696 v3i1e52500 39078696 PMC11322688 · DOI
  81. [90]McElroy E Wood T Bond R Mulvenna M Shevlin M Ploubidis GB Hoffmann MS Moltrecht B Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data BMC Psychiatry 2024 07 24 24 1 530 10.1186/s12888-024-05954-2 39049010 10.1186/s12888-024-05954-2 39049010 PMC11267737 · DOI
  82. [92]Wang X Liu K Wang C Knowledge-enhanced pre-training large language model for depression diagnosis and treatment Proceedings of the IEEE 9th International Conference on Cloud Computing and Intelligent Systems 2023 CCIS 2023 August 12-13, 2023 Dali, China 10.1109/ccis59572.2023.10263217 · DOI
  83. [93]Friedman SF Ballentine G Trajectories of sentiment in 11,816 psychoactive narratives Hum Psychopharmacol 2024 01 20 39 1 e2889 10.1002/hup.2889 38117133 38117133 · DOI
  84. [94]Kamoji S Rozario S Almeida S Patil S Patankar S Pendhari H Mental health prediction using machine learning models and large language model Proceedings of the 2024 Second International Conference on Inventive Computing and Informatics 2024 ICICI 2024 June 11-12, 2024 Bangalore, India 10.1109/icici62254.2024.00040 · DOI
  85. [95]Heston T Safety of large language models in addressing depression Cureus 2023 12 15 12 e50729 10.7759/cureus.50729 38111813 38111813 PMC10727113 · DOI · Paper page
  86. [96]Llanes-Jurado J Gómez-Zaragozá L Minissi ME Alcañiz M Marín-Morales J Developing conversational virtual Humans for social emotion elicitation based on large language models Expert Syst Appl 2024 07 246 123261 10.1016/j.eswa.2024.123261 · DOI
  87. [97]Berrezueta-Guzman S Kandil M Martin-Ruiz ML de la Cruz IP Krusche S Exploring the efficacy of robotic assistants with ChatGPT and Claude in enhancing ADHD therapy: innovating treatment paradigms Proceedings of the International Conference on Intelligent Environments 2024 IE 2024 June 17-20, 2024 Ljubljana, Slovenia 10.1109/ie61493.2024.10599903 · DOI
  88. [98]Gabor-Siatkowska K Sowański M Rzatkiewicz R Stefaniak I Kozłowski M Janicki A AI to train AI: using ChatGPT to improve the accuracy of a therapeutic dialogue system Electronics 2023 11 18 12 22 4694 10.3390/electronics12224694 · DOI
  89. [100]Wu Y Mao K Zhang Y Chen J CALLM: enhancing clinical interview analysis through data augmentation with large language models IEEE J Biomed Health Inform 2024 12 28 12 7531 42 10.1109/JBHI.2024.3435085 39074002 39074002 · DOI
  90. [102]Aygün İ Kaya M Use of large language models for medical synthetic data generation in mental illness IET Conf Proc 2024 02 27 2023 44 652 6 10.1049/icp.2024.1033 · DOI
  91. [103]Sung CW Lee YK Tsai YT A new pipeline for generating instruction dataset via RAG and self fine-tuning Proceedings of the 2024 IEEE 48th Annual Computers, Software, and Applications Conference 2024 COMPSAC 2024 July 02-04, 2024 Osaka, Japan 10.1109/compsac61105.2024.00371 · DOI
  92. [104]Crasto R Dias L Miranda D Kayande D CareBot: a mental health ChatBot Proceedings of the 2nd International Conference for Emerging Technology 2021 INCET 2021 May 21-23, 2021 Belagavi, India 10.1109/incet51464.2021.9456326 · DOI
  93. [105]Ma Z Mei Y Su Z Understanding the benefits and challenges of using large language model-based conversational agents for mental well-being support AMIA Annu Symp Proc 2023 2023 1105 38222348 406 38222348 PMC10785945
  94. [106]Zygadlo A A therapeutic dialogue agent for Polish language Proceedings of the 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos 2021 ACIIW 2021 September 28-October 1, 2021 Nara, Japan 10.1109/aciiw52867.2021.9666281 · DOI
  95. [108]Thapa S Adhikari S GPT-4o and multimodal large language models as companions for mental wellbeing Asian J Psychiatr 2024 09 99 104157 10.1016/j.ajp.2024.104157 39053243 S1876-2018(24)00250-8 39053243 · DOI
  96. [109]Grabb D The impact of prompt engineering in large language model performance: a psychiatric example J Med Artif Intell 2023 10 6 20 10.21037/jmai-23-71 · DOI
  97. [110]Milligan G Bernard A Dowthwaite L Vallejos EP Davis J Salhi L Goulding J Developing a single‐session outcome measure using natural language processing on digital mental health transcripts Couns Psychother Res 2024 05 30 24 3 1057 68 10.1002/capr.12766 · DOI
  98. [112]Wang L Chen X Deng X Wen H You M Liu W Li Q Li J Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs NPJ Digit Med 2024 02 20 7 1 41 10.1038/s41746-024-01029-4 38378899 10.1038/s41746-024-01029-4 38378899 PMC10879172 · DOI
  99. [113]Lainwright N Pemberton M Assessing the response strategies of large language models under uncertainty: a comparative study using prompt engineering OSF Preprints Preprint posted online on August 01, 2024 10.31219/osf.io/34yqj · DOI
  100. [114]Hadi MU Tashi QA Qureshi R Shah A Irfan M Zafar A Shaikh M Akhtar N Hassan SZ Shoman M Wu J Mirjalili S Shah M A survey on large language models: applications, challenges, limitations, and practical usage TechRxiv Preprint posted online on July 10, 2023 10.36227/techrxiv.23589741.v1 · DOI
  101. [115]Lawrence HR Schneider RA Rubin SB Matarić MJ McDuff DJ Jones Bell M The opportunities and risks of large language models in mental health JMIR Ment Health 2024 07 29 11 e59479 10.2196/59479 39105570 v11i1e59479 39105570 PMC11301767 · DOI · Paper page
  102. [116]Neame R Privacy protection for personal health information and shared care records Inform Prim Care 2014 21 2 84 91 10.14236/jhi.v21i2.55 24841409 24841409 · DOI
  103. [117]Paavola J Ekqvist J Privacy preserving and resilient cloudified IoT architecture to support eHealth systems Proceedings of the Third International Conference on Interoperability, Safety and Security in IoT 2017 InterIoT 2017 November 6-7, 2017 Valencia, Spain 10.1007/978-3-319-93797-7_15 · DOI
  104. [119]Potter L Zawadzki MJ Eccleston CP Cook JE Snipes SA Sliwinski MJ Smyth JM The intersections of race, gender, age, and socioeconomic status: implications for reporting discrimination and attributions to discrimination Stigma Health 2019 08 4 3 264 81 10.1037/sah0000099 31517056 31517056 PMC6741774 · DOI
  105. [121]Freyer O Wiest IC Kather JN Gilbert S A future role for health applications of large language models depends on regulators enforcing safety standards Lancet Digit Health 2024 09 6 9 e662 72 10.1016/s2589-7500(24)00124-9 39179311 · DOI
  106. [122]Han T Kumar A Agarwal C Lakkaraju H Towards safe large language models for medicine ArXiv Preprint posted online on May 1, 2024
  107. [123]Fu G Zhao Q Dan L Li J Song J Wei Z Liu S Wang F Wang Y Cheng L Zhang J Yang BY Enhancing psychological counseling with large language model: a multifaceted decision-support system for non-professionals arXiv Preprint posted online on Septeber 11, 2023 10.2196/preprints.52656 · DOI
  108. [124]De Choudhury M Pendse SR Kumar N Benefits and harms of large language models in digital mental health PsyArXiv Preprints Preprint posted online on November 15, 2023 10.31234/osf.io/y8ax9 · DOI · Paper page

Cited by

Sorted by citations

Authors
Jin, Y., Liu, J., Li, P., Wang, B., Yan, Y., Zhang, H., Ni, C., Wang, J., Li, Y., Bu, Y., & Wang, Y.
Affiliations
  • Beijing Normal University (CN)
  • South China Normal University (CN)
  • Guangdong Polytechnic Normal University (CN)
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2025-05-05
Volume
27
Pages
e69284
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/69284
PMC
PMC12089884
OpenAlex
W4408437554
Cited by
94
References
14
Citations by year
2026: 72 · 2025: 22
FWCI
18.39
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (72304040, 82201708)
  • South China Normal University
  • Guangdong Office of Philosophy and Social Science (GD23YXL01)
  • National Center for Mental Health (MHSK2024B24)
Topics
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
large language models, mental health, depression detection, suicide risk prediction, mental disorder screening, mental health counseling, chatbots, LLM performance comparison, access to mental health care
MeSH terms
Large Language Models, Mental Disorders, Humans, Language, Mental Health, Natural Language Processing

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