Editorial & Opinion
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Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.

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Τίτλος: Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.
Συγγραφείς: Gordon AJ; Program for Addiction Research, Clinical Care, Knowledge and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.; Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA., Bounthavong M; University of California San Diego, Skaggs School of Pharmacy & Pharmaceutical Sciences, La Jolla, CA, USA., Corte C; University of Illinois Chicago College of Nursing, IL, USA., Siantz E; University of Utah College of Social Work, Salt Lake City, UT, USA., Mahmoud KF; Department of Community Health Nursing, Faculty of Nursing, Applied Science Private University, Amman, Jordan., Finnell DS; Johns Hopkins School of Nursing, Baltimore, MD, USA.; University at Buffalo School of Nursing, NY, USA., Scheuermann T; Department of Population Health, University of Kansas Medical Center, KS, USA., Kyei EF; Center for Substance Use Research and Related Conditions, Capstone College of Nursing, The University of Alabama, Tuscaloosa, AL, USA., Wyse JJ; Center to Improve Veteran Involvement in Care (CIVIC), VA Portland Health Care System, OR, USA.; School of Public Health, Oregon Health & Science University, Portland, OR, USA., Oliva EM; Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA., Pugatch M; Potocsnak Family Division of Adolescent and Young Adult Medicine, Department of Pediatrics and Pritzker Department of Psychiatry and Behavioral Health, Ann & Robert H. Lurie Children's Hospital of Chicago, IL, USA.; Department of Psychiatry and Behavioral Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA., Halmo RS; School of Nursing, Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA., Rosenwohl-Mack S; University of California San Francisco, Department of Family and Community Medicine, CA, USA., Marks SJ; Department of Health Policy, School of Public Health and Medical Scientist Training Program, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA., Faseru B; Department of Population Health, University of Kansas Medical Center, KS, USA.
Πηγή: Substance use & addiction journal [Subst Use Addctn J] 2026 Oct; Vol. 47 (4), pp. 941-952. Date of Electronic Publication: 2026 Aug 13.
Τύπος έκδοσης: Editorial
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Sage Publishing Country of Publication: United States NLM ID: 9918750589006676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2976-7350 (Electronic) Linking ISSN: 29767342 NLM ISO Abbreviation: Subst Use Addctn J Subsets: MEDLINE
Imprint Name(s): Original Publication: Thousand Oaks, California : Sage Publishing, [2024]-
Ιατρικοί όροι (MeSH): Artificial Intelligence* , Publishing* , Disclosure* , Social Responsibility* , Addiction Medicine* , Biomedical Research*, Humans ; Generative Artificial Intelligence ; Editorial Policies
Περίληψη: Artificial intelligence (AI) is embedded in addiction scholarship, not only as a methodological tool in research but also as a tool for manuscript preparation. In addiction research and clinical care, AI is already used to analyze data and support clinical decision-making. In parallel, AI tools are increasingly used to summarize literature, revise prose, generate outlines, draft text, and assist with interpretation and presentation of findings. These developments create an urgent need for addiction journals to clarify how AI use should be governed and disclosed. In this editorial, we argue for a principled, proportionate framework for AI disclosure grounded in materiality. We distinguish AI used in the conduct of research from AI used in manuscript preparation and propose a practical taxonomy of assistive, intermediate, and generative AI mapped onto 6 escalating levels of involvement. We identify intermediate AI as a key governance challenge because it may appear to provide editorial assistance while materially shaping scholarly content. We argue that disclosure should be required when AI materially contributes to the conduct of research or to the intellectual content, interpretation, argument, or presentation of a manuscript, while routine low-risk assistive uses should not require disclosure. Transparent governance, organized around materiality rather than specific technologies, is the appropriate response to AI in addiction publishing.
Competing Interests: Declaration of Conflicting InterestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AJG reports remuneration from the medical online resource UpToDate, Inc, for an authored chapter unrelated to this work. Authors have served, in the last 3 years, on the board of directors for the Association for Multidisciplinary Education and Research in Substance Use and Addiction (AMERSA) (authors AJG, DSF, and BF) and the International Society of Addiction Journal Editors (ISAJE) (author AJG), which are not-for-profit organizations that they received no remuneration. All authors are part of the Editorial Team of the Substance Use & Addiction Journal and may receive remuneration for their role on the journal’s Editorial Team.
Contributed Indexing: Keywords: addiction medicine; artificial intelligence; editorial; editorial policies; generative artificial intelligence; large language models; medical writing; substance-related disorders; writing
Entry Date(s): Date Created: 20260813 Date Completed: 20260915 Latest Revision: 20260915
Update Code: 20260915
DOI: 10.1177/29767342261471638
PMID: 42592893
Βάση Δεδομένων: MEDLINE
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  Data: Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.
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  Data: <searchLink fieldCode="AU" term="%22Gordon+AJ%22">Gordon AJ</searchLink>; Program for Addiction Research, Clinical Care, Knowledge and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.; Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.<br /><searchLink fieldCode="AU" term="%22Bounthavong+M%22">Bounthavong M</searchLink>; University of California San Diego, Skaggs School of Pharmacy & Pharmaceutical Sciences, La Jolla, CA, USA.<br /><searchLink fieldCode="AU" term="%22Corte+C%22">Corte C</searchLink>; University of Illinois Chicago College of Nursing, IL, USA.<br /><searchLink fieldCode="AU" term="%22Siantz+E%22">Siantz E</searchLink>; University of Utah College of Social Work, Salt Lake City, UT, USA.<br /><searchLink fieldCode="AU" term="%22Mahmoud+KF%22">Mahmoud KF</searchLink>; Department of Community Health Nursing, Faculty of Nursing, Applied Science Private University, Amman, Jordan.<br /><searchLink fieldCode="AU" term="%22Finnell+DS%22">Finnell DS</searchLink>; Johns Hopkins School of Nursing, Baltimore, MD, USA.; University at Buffalo School of Nursing, NY, USA.<br /><searchLink fieldCode="AU" term="%22Scheuermann+T%22">Scheuermann T</searchLink>; Department of Population Health, University of Kansas Medical Center, KS, USA.<br /><searchLink fieldCode="AU" term="%22Kyei+EF%22">Kyei EF</searchLink>; Center for Substance Use Research and Related Conditions, Capstone College of Nursing, The University of Alabama, Tuscaloosa, AL, USA.<br /><searchLink fieldCode="AU" term="%22Wyse+JJ%22">Wyse JJ</searchLink>; Center to Improve Veteran Involvement in Care (CIVIC), VA Portland Health Care System, OR, USA.; School of Public Health, Oregon Health & Science University, Portland, OR, USA.<br /><searchLink fieldCode="AU" term="%22Oliva+EM%22">Oliva EM</searchLink>; Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.<br /><searchLink fieldCode="AU" term="%22Pugatch+M%22">Pugatch M</searchLink>; Potocsnak Family Division of Adolescent and Young Adult Medicine, Department of Pediatrics and Pritzker Department of Psychiatry and Behavioral Health, Ann & Robert H. Lurie Children's Hospital of Chicago, IL, USA.; Department of Psychiatry and Behavioral Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.<br /><searchLink fieldCode="AU" term="%22Halmo+RS%22">Halmo RS</searchLink>; School of Nursing, Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA.<br /><searchLink fieldCode="AU" term="%22Rosenwohl-Mack+S%22">Rosenwohl-Mack S</searchLink>; University of California San Francisco, Department of Family and Community Medicine, CA, USA.<br /><searchLink fieldCode="AU" term="%22Marks+SJ%22">Marks SJ</searchLink>; Department of Health Policy, School of Public Health and Medical Scientist Training Program, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.<br /><searchLink fieldCode="AU" term="%22Faseru+B%22">Faseru B</searchLink>; Department of Population Health, University of Kansas Medical Center, KS, USA.
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  Data: Artificial intelligence (AI) is embedded in addiction scholarship, not only as a methodological tool in research but also as a tool for manuscript preparation. In addiction research and clinical care, AI is already used to analyze data and support clinical decision-making. In parallel, AI tools are increasingly used to summarize literature, revise prose, generate outlines, draft text, and assist with interpretation and presentation of findings. These developments create an urgent need for addiction journals to clarify how AI use should be governed and disclosed. In this editorial, we argue for a principled, proportionate framework for AI disclosure grounded in materiality. We distinguish AI used in the conduct of research from AI used in manuscript preparation and propose a practical taxonomy of assistive, intermediate, and generative AI mapped onto 6 escalating levels of involvement. We identify intermediate AI as a key governance challenge because it may appear to provide editorial assistance while materially shaping scholarly content. We argue that disclosure should be required when AI materially contributes to the conduct of research or to the intellectual content, interpretation, argument, or presentation of a manuscript, while routine low-risk assistive uses should not require disclosure. Transparent governance, organized around materiality rather than specific technologies, is the appropriate response to AI in addiction publishing.
– Name: Abstract
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  Data: Declaration of Conflicting InterestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AJG reports remuneration from the medical online resource UpToDate, Inc, for an authored chapter unrelated to this work. Authors have served, in the last 3 years, on the board of directors for the Association for Multidisciplinary Education and Research in Substance Use and Addiction (AMERSA) (authors AJG, DSF, and BF) and the International Society of Addiction Journal Editors (ISAJE) (author AJG), which are not-for-profit organizations that they received no remuneration. All authors are part of the Editorial Team of the Substance Use & Addiction Journal and may receive remuneration for their role on the journal’s Editorial Team.
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