Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models
Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 varia...
Uloženo v:
| Vydáno v: | NPJ digital medicine Ročník 8; číslo 1; s. 332 - 6 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Nature Publishing Group
04.06.2025
Nature Publishing Group UK Nature Portfolio |
| Témata: | |
| ISSN: | 2398-6352, 2398-6352 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models' diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems. |
|---|---|
| AbstractList | Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models' diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems.Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models' diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems. Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models’ diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems. Abstract Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models’ diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems. |
| ArticleNumber | 332 |
| Author | Stuart, Elizabeth M. Bouguettaya, Ayoub Aboujaoude, Elias |
| Author_xml | – sequence: 1 givenname: Ayoub surname: Bouguettaya fullname: Bouguettaya, Ayoub – sequence: 2 givenname: Elizabeth M. surname: Stuart fullname: Stuart, Elizabeth M. – sequence: 3 givenname: Elias surname: Aboujaoude fullname: Aboujaoude, Elias |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40467886$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Ustq3DAUFSWlSaf5gS6KoJtu3OplW-qmhNDHQKBQshdXDzsabGsi2QP5-yqeNCRZdCMdrs493Ht03qKTKU4eofeUfKaEyy9Z0FY0FWF1RVYkXqEzxpWsGl6zkyf4FJ3nvCOEMCKkEs0bdCqIaFopmzN0-AM2wIBNgIzDhC-21ehdgNk7vM939qbAFCwupX6KOWQMk8Nz8jCPfpq_YsC3CwxhhjkcPLZx3EMKOU44driLS8IDpN6Xc-oXKGCMzg_5HXrdwZD9-cO9Qdc_vl9f_qqufv_cXl5cVVYoNVcUWtpRC44qxTh42TopiDSEM6W8IFA7MJ4bw52RlHetA-koodBYT5nlG7Q9yroIO71PYYR0pyMEvRZi6jWkOdjBa8W5q8GKGkwrqKTArYFamkYAk7x2RevbUWu_mGKRLdsnGJ6JPn-Zwo3u40FTRnnbkLYofHpQSPF28XnWY8jWD8UbH5esOaO1kjUvo2zQxxfUXfFyKlatrJaQEoLC-vB0pMdZ_n1vIbAjwaaYc_LdI4USfR8jfYyRLjHSa4y0KE3yRZNdvzferxWG_7X-BXXpzUo |
| CitedBy_id | crossref_primary_10_3390_bioengineering12090967 crossref_primary_10_1080_09540261_2025_2559108 |
| Cites_doi | 10.2139/ssrn.4880335 10.4088/JCP.v69n0609 10.1073/pnas.2416228122 10.1101/2024.03.28.24305027 10.1016/j.isci.2024.109713 10.1111/1754-9485.13261 10.1038/s43856-024-00601-z 10.1038/s44184-024-00056-z 10.1038/s41597-023-01947-0 10.18653/v1/2024.naacl-long.198 10.1080/00323187.2024.2335471 10.1038/s41591-024-03258-2 10.1038/s44277-024-00010-z 10.1038/s41586-024-07856-5 10.2147/NDT.S128584 10.1016/j.schres.2023.09.033 10.1007/s10916-024-02090-y 10.1038/s41597-023-02814-8 10.1001/jamanetworkopen.2024.40969 10.1038/s41591-025-03626-6 10.1038/s41746-023-00939-z 10.7916/vib.v6i.5890 10.1111/ijd.17076 10.21037/jmai-21-12 10.1007/s40596-024-01996-6 10.1001/jamapediatrics.2023.5750 10.1016/j.health.2023.100147 10.1176/ps.46.6.586 10.1111/jcpp.12204 10.48550/arXiv.2411.02355 10.3233/SHTI220468 10.1056/CAT.23.0404 10.1177/2156869318811435 10.1016/j.beth.2005.12.002 10.1016/S2589-7500(23)00225-X 10.18653/v1/2024.findings-emnlp.120 10.1002/eat.10070 10.1038/s41746-024-01233-2 10.1002/wps.21079 10.1016/j.psychres.2020.113302 10.1001/jama.2023.22557 10.31234/osf.io/y8ax9 10.1093/jamia/ocae060 |
| ContentType | Journal Article |
| Copyright | 2025. The Author(s). Copyright Nature Publishing Group Dec 2025 The Author(s) 2025 2025 |
| Copyright_xml | – notice: 2025. The Author(s). – notice: Copyright Nature Publishing Group Dec 2025 – notice: The Author(s) 2025 2025 |
| DBID | AAYXX CITATION NPM 3V. 7RV 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. KB0 M0S NAPCQ PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1038/s41746-025-01746-4 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Health & Medical Collection Nursing & Allied Health Premium ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7RV name: Nursing & Allied Health Database url: https://search.proquest.com/nahs sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2398-6352 |
| EndPage | 6 |
| ExternalDocumentID | oai_doaj_org_article_933d5ac45ab74181a3cba58b64a2835d PMC12137607 40467886 10_1038_s41746_025_01746_4 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 53G 7RV 7X7 8FI 8FJ AAJSJ AASML AAYXX ABUWG ACGFS ADBBV AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV BENPR C6C CCPQU CITATION EBLON EBS EIHBH FYUFA GROUPED_DOAJ HMCUK HYE M~E NAO NAPCQ NO~ OK1 PGMZT PHGZM PHGZT PIMPY PPXIY RNT RPM SNYQT UKHRP ALIPV NPM PMFND 3V. 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c499t-1a71f1cad19923ae87d8408b03299e40a5dabe3bb3db813f7da8d101a6ce12c3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001502324200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2398-6352 |
| IngestDate | Fri Oct 03 12:51:58 EDT 2025 Tue Nov 04 02:02:54 EST 2025 Fri Sep 05 15:58:47 EDT 2025 Tue Oct 07 07:16:31 EDT 2025 Sun Jun 08 01:33:39 EDT 2025 Sun Nov 09 14:48:58 EST 2025 Tue Nov 18 22:04:21 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c499t-1a71f1cad19923ae87d8408b03299e40a5dabe3bb3db813f7da8d101a6ce12c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/3215700103?pq-origsite=%requestingapplication% |
| PMID | 40467886 |
| PQID | 3215700103 |
| PQPubID | 5061815 |
| PageCount | 6 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_933d5ac45ab74181a3cba58b64a2835d pubmedcentral_primary_oai_pubmedcentral_nih_gov_12137607 proquest_miscellaneous_3215985393 proquest_journals_3215700103 pubmed_primary_40467886 crossref_primary_10_1038_s41746_025_01746_4 crossref_citationtrail_10_1038_s41746_025_01746_4 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-04 |
| PublicationDateYYYYMMDD | 2025-06-04 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-04 day: 04 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | NPJ digital medicine |
| PublicationTitleAlternate | NPJ Digit Med |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group Nature Publishing Group UK Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group – name: Nature Publishing Group UK – name: Nature Portfolio |
| References | N Obradovich (1746_CR9) 2024; 2 A Alamoodi (1746_CR53) 2024; 48 KH Gordon (1746_CR29) 2002; 32 DM Anglin (1746_CR18) 2008; 69 1746_CR27 1746_CR24 1746_CR25 1746_CR22 1746_CR23 J Gravel (1746_CR44) 2023; 1 LN Yatham (1746_CR45) 2023; 22 J Barile (1746_CR2) 2024; 178 PL Morgan (1746_CR30) 2014; 55 DM Anglin (1746_CR19) 2008; 69 H Chung (1746_CR21) 1995; 46 X Bai (1746_CR26) 2025; 122 JA Omiye (1746_CR11) 2023; 6 A Palmer (1746_CR43) 2023; 75 1746_CR15 1746_CR59 RK Bailey (1746_CR16) 2019; 15 1746_CR13 1746_CR57 1746_CR58 1746_CR55 1746_CR56 X Meng (1746_CR28) 2024; 27 1746_CR54 I-M Mølstrøm (1746_CR46) 2020; 291 F Chen (1746_CR52) 2024; 31 EC Stade (1746_CR8) 2024; 3 P Chlap (1746_CR51) 2021; 65 J Pesa (1746_CR20) 2023; 261 IC Wiest (1746_CR60) 2024; 7 1746_CR49 NF Ayoub (1746_CR14) 2024; 2 1746_CR47 R Khera (1746_CR12) 2023; 330 J Vanderminden (1746_CR17) 2019; 9 1746_CR42 1746_CR40 1746_CR41 1746_CR7 1746_CR6 E Goh (1746_CR10) 2024; 7 KH Gordon (1746_CR31) 2006; 37 Y Yang (1746_CR34) 2024; 4 O Itauma (1746_CR5) 2024; 14 1746_CR37 1746_CR38 1746_CR35 X Bai (1746_CR48) 2025; 122 1746_CR36 1746_CR33 R Fliorent (1746_CR39) 2024; 63 1746_CR1 Y-C Wang (1746_CR50) 2023; 3 1746_CR32 1746_CR3 C Crabtree (1746_CR61) 2023; 10 1746_CR4 |
| References_xml | – ident: 1746_CR32 doi: 10.2139/ssrn.4880335 – volume: 69 start-page: 941 year: 2008 ident: 1746_CR19 publication-title: J. Clin. Psychiatry doi: 10.4088/JCP.v69n0609 – volume: 122 year: 2025 ident: 1746_CR48 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.2416228122 – ident: 1746_CR58 – ident: 1746_CR4 doi: 10.1101/2024.03.28.24305027 – volume: 1 start-page: 226 year: 2023 ident: 1746_CR44 publication-title: Mayo Clin. Proc.: Digit. Health – volume: 27 year: 2024 ident: 1746_CR28 publication-title: Iscience doi: 10.1016/j.isci.2024.109713 – ident: 1746_CR49 – volume: 65 start-page: 545 year: 2021 ident: 1746_CR51 publication-title: J. Med. Imaging Radiat. Oncol. doi: 10.1111/1754-9485.13261 – volume: 4 start-page: 176 year: 2024 ident: 1746_CR34 publication-title: Commun. Med. doi: 10.1038/s43856-024-00601-z – ident: 1746_CR3 – volume: 3 start-page: 12 year: 2024 ident: 1746_CR8 publication-title: NPJ Ment. Health Res. doi: 10.1038/s44184-024-00056-z – volume: 10 year: 2023 ident: 1746_CR61 publication-title: Sci. Data doi: 10.1038/s41597-023-01947-0 – ident: 1746_CR35 doi: 10.18653/v1/2024.naacl-long.198 – ident: 1746_CR42 – ident: 1746_CR15 – ident: 1746_CR57 – volume: 2 start-page: 186 year: 2024 ident: 1746_CR14 publication-title: Mayo Clin. Proc.: Digital Health – volume: 75 start-page: 281 year: 2023 ident: 1746_CR43 publication-title: Political Sci. doi: 10.1080/00323187.2024.2335471 – ident: 1746_CR38 doi: 10.1038/s41591-024-03258-2 – volume: 2 start-page: 8 year: 2024 ident: 1746_CR9 publication-title: NPP—Digit. Psychiatry Neurosci. doi: 10.1038/s44277-024-00010-z – volume: 14 start-page: 09 year: 2024 ident: 1746_CR5 publication-title: Int. J. Bioinform. Biosci. – ident: 1746_CR25 – ident: 1746_CR24 doi: 10.1038/s41586-024-07856-5 – volume: 15 start-page: 603 year: 2019 ident: 1746_CR16 publication-title: Neuropsychiatr. Dis. Treat. doi: 10.2147/NDT.S128584 – volume: 261 start-page: 170 year: 2023 ident: 1746_CR20 publication-title: Schizophr. Res. doi: 10.1016/j.schres.2023.09.033 – volume: 48 start-page: 1 year: 2024 ident: 1746_CR53 publication-title: J. Med. Syst. doi: 10.1007/s10916-024-02090-y – ident: 1746_CR54 doi: 10.1038/s41597-023-02814-8 – volume: 7 start-page: e2440969 year: 2024 ident: 1746_CR10 publication-title: JAMA Netw. Open doi: 10.1001/jamanetworkopen.2024.40969 – ident: 1746_CR37 – ident: 1746_CR23 doi: 10.1038/s41591-025-03626-6 – ident: 1746_CR33 – ident: 1746_CR56 – volume: 6 year: 2023 ident: 1746_CR11 publication-title: NPJ Digit. Med. doi: 10.1038/s41746-023-00939-z – volume: 122 start-page: 8 year: 2025 ident: 1746_CR26 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2416228122 – ident: 1746_CR40 doi: 10.7916/vib.v6i.5890 – volume: 63 start-page: 455 year: 2024 ident: 1746_CR39 publication-title: Int. J. Dermatol. doi: 10.1111/ijd.17076 – ident: 1746_CR41 doi: 10.21037/jmai-21-12 – ident: 1746_CR1 doi: 10.1007/s40596-024-01996-6 – volume: 178 start-page: 313 year: 2024 ident: 1746_CR2 publication-title: JAMA Pediatr. doi: 10.1001/jamapediatrics.2023.5750 – volume: 3 start-page: 100147 year: 2023 ident: 1746_CR50 publication-title: Healthc. Anal. doi: 10.1016/j.health.2023.100147 – volume: 46 start-page: 586 year: 1995 ident: 1746_CR21 publication-title: Psychiatr. Serv. (Washington, DC) doi: 10.1176/ps.46.6.586 – volume: 55 start-page: 905 year: 2014 ident: 1746_CR30 publication-title: J. Child Psychol. Psychiatry doi: 10.1111/jcpp.12204 – ident: 1746_CR7 – ident: 1746_CR36 doi: 10.48550/arXiv.2411.02355 – ident: 1746_CR47 doi: 10.3233/SHTI220468 – ident: 1746_CR6 doi: 10.1056/CAT.23.0404 – volume: 69 start-page: 941 year: 2008 ident: 1746_CR18 publication-title: J. Clin. Psychiatry doi: 10.4088/JCP.v69n0609 – volume: 9 start-page: 111 year: 2019 ident: 1746_CR17 publication-title: Soc. Ment. Health doi: 10.1177/2156869318811435 – volume: 37 start-page: 319 year: 2006 ident: 1746_CR31 publication-title: Behav. Ther. doi: 10.1016/j.beth.2005.12.002 – ident: 1746_CR13 doi: 10.1016/S2589-7500(23)00225-X – ident: 1746_CR55 – ident: 1746_CR27 doi: 10.18653/v1/2024.findings-emnlp.120 – ident: 1746_CR59 – volume: 32 start-page: 219 year: 2002 ident: 1746_CR29 publication-title: Int. J. Eat. Disord. doi: 10.1002/eat.10070 – volume: 7 year: 2024 ident: 1746_CR60 publication-title: NPJ Digit. Med. doi: 10.1038/s41746-024-01233-2 – volume: 22 start-page: 263 year: 2023 ident: 1746_CR45 publication-title: World Psychiatry doi: 10.1002/wps.21079 – volume: 291 year: 2020 ident: 1746_CR46 publication-title: Psychiatry Res. doi: 10.1016/j.psychres.2020.113302 – volume: 330 start-page: 2255 year: 2023 ident: 1746_CR12 publication-title: JAMA doi: 10.1001/jama.2023.22557 – ident: 1746_CR22 doi: 10.31234/osf.io/y8ax9 – volume: 31 start-page: 1172 year: 2024 ident: 1746_CR52 publication-title: J. Am. Med. Inform. Assoc. doi: 10.1093/jamia/ocae060 |
| SSID | ssj0002048946 |
| Score | 2.313989 |
| Snippet | Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in... Abstract Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 332 |
| SubjectTerms | Artificial intelligence Bias Large language models Medical diagnosis Psychologists Race Racism |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pi9UwEA6yiHgR15_VVSJ4k7LNJk3Tva3ioqCLyB72FiaZBCtL37J9u3-_k6SvvCeiF2-lTWmamcnMJF--Yeyt73UbjZY1yAZqpRDrPmqoPaIOrQENPlPmf-nOzszFRf9tq9RXwoQVeuAycIeUcGMLXrXgEtGKAOkdtMZpBYkqDNPsS1HPVjL1M2-vKdMrPZ-SaaQ5nBTF3glvm7Bq6UrteKJM2P-nKPN3sOSW9zl9yB7MYSM_Kd3dZ3fC-Ijd-zpvjD9mt98hrX1zN8DEh5GffK7zmRCKJ_kCaB48x4KsGyYOI_IFZX7MgZfjlZkHnPulOiFfRR7p6_wyQcb5ZnmT5wo60xN2fvrx_MOnei6pUHtKbda1gE5E4QET6lRCMB1ShmdcI8ktBdVAi-CCdE6iM0LGDsEgWS1oH8SRl0_Z3rgaw3PGDSDNDs5ROggqtI2L2GhUwgURo0SomNiMrvUz3XiqenFp87a3NLZIxJJEbJaIVRV7t7xzVcg2_tr6fRLa0jIRZecbpD52Vh_7L_Wp2MFG5Ha23slKioO6XAGjYm-Wx2R3aTMFxrC6KW16inV6avOsaMjSE9WQ-zFGV8zs6M5OV3efjMOPzO2dGPY63XQv_sfPvWT3j7LCk96rA7a3vr4Jr9hdf7sepuvX2WJ-AYSvHDU priority: 102 providerName: Directory of Open Access Journals |
| Title | Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40467886 https://www.proquest.com/docview/3215700103 https://www.proquest.com/docview/3215985393 https://pubmed.ncbi.nlm.nih.gov/PMC12137607 https://doaj.org/article/933d5ac45ab74181a3cba58b64a2835d |
| Volume | 8 |
| WOSCitedRecordID | wos001502324200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: DOA dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: M~E dateStart: 20180101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: 7X7 dateStart: 20181201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: 7RV dateStart: 20181201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: BENPR dateStart: 20181201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 2398-6352 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002048946 issn: 2398-6352 databaseCode: PIMPY dateStart: 20181201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEB6aTSm99N3Gbbqo0FsxsSNZlnspSUlooFmWJZTtSYwebg3BTteb_P5IstbtlpJLL8a2ZDwwGs1o9OkbgPe64kUtOE2RZpgyZkxa1RxTbQy3hUCOOlDmfy1nM7FcVvOYcOsjrHIzJ4aJ2nTa58gPqPNNZahK8OnqV-qrRvnd1VhCYwd2PVMZm8Du8clsvhizLJ6WtmI8npbJqDjomYvBPe7WY9b8HdvySIG4_1_R5t-gyT-80Onj_5X_CTyK8Sc5GgbMU7hn22fw4DzusD-HmwX6JDpRDfakacnRWRoOl7jAlIzI6EYTM0D0mp5ga8gIV_9IkAznNAOhONFjmUPS1aR2fyeXHntONnlSEkrx9C_g4vTk4vOXNNZmSLVbI63THMu8zjUaD1-laEVp3FJRqIw6_2ZZhoVBZalS1CiR07o0KIwzf-Ta5oeavoRJ27V2D4hA46YZpdy6EpktMlWbjBuWK5vXNTWYQL5Rj9SRt9yXz7iUYf-cCjmoVDqVyqBSyRL4MH5zNbB23Nn72Gt97OkZt8OLbvVDRgOWFaWmQM0KVJ7wJ0eqFRZCcYaess4ksL_Ru4zTQC9_Kz2Bd2OzM2C_K4Ot7a6HPpULmirX59UwxEZJWOb8mBA8AbE1-LZE3W5pm5-BJNxT9ZU8K1_fLdcbeHgYbMGZBNuHyXp1bd_CfX2zbvrVFHbKxTd_XZbhKqbRwKYhd-Ge5mfn8--3j34w1Q |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgoAL70eggJHghKIma8dxkBAqj6qrbleoWqHerPEjEKlKymZbxI_iP2I7D1iEeuuBWxQ7iZN8Mx7bn78BeKELnpWC0xhpgjFjxsRFyTHWxnCbCeSog2T-LJ_PxdFR8WkDfg57YTytcvCJwVGbRvs58m3q-qY8ZCV4e_It9lmj_OrqkEKjg8W-_fHdDdnaN9MP7v--nEx2Py7e78V9VoFYu-h-FaeYp2Wq0XjiJUUrcuMGOUIl1HlmyxLMDCpLlaJGiZSWuUFhHHCRa5tONHW3vQSXnRtPPYMsP_w8Tul4DdyC8X5rTkLFdstcwO9Jvp4g54_YWvcXsgT8K7T9m6H5R5e3e_M_-1i34EYfW5Odzhhuw4at78DVg549cBfODtEvEBBVYUuqmuxM47BxxgXdZGR9V5qYjn5YtQRrQ0Yq_muCpNuDGsTSiR5TOJKmJKV7Ojn2vHoyzAGTkGaovQeLi3jp-7BZN7V9CESgcS5UKTdmRmazRJUm4YalyqZlSQ1GkA5okLrXZPepQY5l4AZQITsESYcgGRAkWQSvxmtOOkWSc2u_8yAba3o18XCiWX6RvXOSBaUmQ80yVF7MKEWqFWZCcYZejs9EsDXATPYurpW_MRbB87HYOSe_4oS1bU67OoULCAtX50GH6LElLHF9tBA8ArGG9bWmrpfU1dcggO5lCHOe5I_Ob9czuLa3OJjJ2XS-_xiuT4IZOmtkW7C5Wp7aJ3BFn62qdvk02DEBecGm8AtwrIeg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Racial+bias+in+AI-mediated+psychiatric+diagnosis+and+treatment%3A+a+qualitative+comparison+of+four+large+language+models&rft.jtitle=NPJ+digital+medicine&rft.au=Bouguettaya%2C+Ayoub&rft.au=Stuart%2C+Elizabeth+M.&rft.au=Aboujaoude%2C+Elias&rft.date=2025-06-04&rft.issn=2398-6352&rft.eissn=2398-6352&rft.volume=8&rft.issue=1&rft_id=info:doi/10.1038%2Fs41746-025-01746-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41746_025_01746_4 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2398-6352&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2398-6352&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2398-6352&client=summon |