Generative AI-assisted clinical interviewing of mental health

StatusVoR
Simple item page
dc.abstract.enThe standard assessment of mental health typically involves clinical interviews conducted by highly trained clinicians. While effective, this approach faces substantial limitations, including high costs, high clinician workload, variability in expertise, and a lack of standardization. Recent progress in large language models (LLMs) offer a promising avenue to address these limitations by simulating clinician-administered interviews through AI-powered systems. However, few studies have rigorously validated such tools. In this study, we used TalkToAlba to develop and evaluat an AI assistant designed to conduct clinical interviews aligned with DSM-5 criteria. Participants (N = 303) included individuals with self-reported clinician-diagnosed mental health disorders, namely, major depressive disorder (MDD), generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention-deficit/hyperactivity disorder (ADD/ADHD), autism spectrum disorder (ASD), eating disorders (ED), substance use disorder (SUD), and bipolar disorder (BD)—alongside healthy controls. The AI assistant conducted diagnostic interviews and assessed the likelihood of each disorder, while another AI system analyzed interview transcripts to verify diagnostic criteria and generate comprehensive justifications for its conclusions. The results showed that the AI-powered clinical interview achieved higher agreement (i.e., Cohen’s Kappa), sensitivity, and specificity in identifying self-reported, clinician-diagnosed disorders compared to established rating scales. It also exhibited significantly lower co-dependencies between diagnostic categories. Additionally, most participants rated the AI-powered interview as highly empathic, relevant, understanding, and supportive. These findings suggest that AI-powered clinical interviews can serve as accurate, standardized, and person-centered tools for assessing common mental disorders. Their scalability, low cost, and positive user experience position them as a valuable complement to traditional diagnostic methods, with potential for widespread application in mental health care delivery.
dc.affiliationWydział Psychologii i Prawa w Poznaniu
dc.contributor.authorLasota, Marta
dc.contributor.authorSikström, Sverker
dc.contributor.authorBoehme, Rebecca Astrid
dc.contributor.authorMirström, Mariam
dc.contributor.authorAgbotsoka, Thibaud
dc.contributor.authorGyőri, Gergő
dc.contributor.authorTabesh, Mona
dc.contributor.authorStille, Lotta
dc.contributor.authorGarcia, Danilo
dc.date.access2025-11-04
dc.date.accessioned2025-11-04T08:19:01Z
dc.date.available2025-11-04T08:19:01Z
dc.date.created2025-07-24
dc.date.issued2025-10-29
dc.description.accesstimeafter_publication
dc.description.physical1-12
dc.description.sdgNoSDGsAreRelevantForThisPublication
dc.description.versionfinal_published
dc.description.volume15
dc.identifier.doi10.1038/s41598-025-13429-x
dc.identifier.issn2045-2322
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/1936
dc.languageen
dc.pbn.affiliationpsychologia
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOPEN_JOURNAL
dc.subject.enAI-powered clinical interviews
dc.subject.enMental health assessment
dc.subject.enLarge language models (LLMs)
dc.subject.enGenerative AI
dc.subject.enMajor depressive disorder (MDD)
dc.subject.enGeneralized anxiety disorder (GAD)
dc.subject.enObsessive-compulsive disorder (OCD)
dc.subject.enPost-traumatic stress disorder (PTSD)
dc.subject.enAttention-deficit/hyperactivity disorder (ADD/ ADHD)
dc.subject.enAutism spectrum disorder (ASD)
dc.subject.enEating disorders
dc.subject.enSubstance use disorder (SUD)
dc.subject.enBipolar disorder (BD)
dc.swps.sciencecloudsend
dc.titleGenerative AI-assisted clinical interviewing of mental health
dc.title.journalScientific Reports
dc.typeJournalArticle
dspace.entity.typeArticle