The Clinician in the Loop: How Multimodal AI Affects Clinical Decision-Making in Mental Healthcare

The Clinician in the Loop: How Multimodal AI Affects Clinical Decision-Making in Mental Healthcare | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Clinician in the Loop: How Multimodal AI Affects Clinical Decision-Making in Mental Healthcare Jack Noto, Om Panda, Chioma Uka, Omid Tabatabaee, Nathan Carroll, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8216300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Multimodal artificial intelligence (AI) presents a promising avenue for enhancing precision in psychiatry; however, practical and ethical challenges hinder its clinical adoption. This study surveyed thirty-three mental health professionals to investigate the impact of AI-generated multimodal data on clinical decision-making, perceived data reliability, and barriers to implementation. Results demonstrated that multimodal data significantly altered clinical management. Data suggesting a clear diagnostic shift prompted clinicians to adjust their recommendations, whereas nonspecific data had little effect. The low explainability of multimodal data was a significant factor in clinician responses and actually led to clinical errors. Clinicians identified ethical concerns, patient trust, and knowledge gaps as the primary barriers to implementation, rather than logistical issues. These findings suggest that while clinicians are receptive to using specific, interpretable multimodal data to augment their reasoning, the widespread adoption of AI in psychiatry depends on building trust through education, transparency, and robust clinical validation. Scientific community and society/Business and industry Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Scientific community and society/Scientific community Psychiatric Ethics Multimodal AI Clinical Decision-Making Algorithmic Bias Digital Phenotyping Therapeutic Alliance Explainable AI (XAI) Data Privacy SDG3 SDG9 SDG10 Full Text Additional Declarations Competing interest reported. The author O.P. discloses a financial competing interest as the Founder and equity holder of Blank Atom LLC, the commercial entity developing Open Mind, a multimodal diagnostic and therapeutic interface for psychiatric care. The findings of this study regarding the clinical risks of low explainability in AI inform the safety and design protocols relevant to Open Mind’s technology. The author O.P. discloses a non-financial competing interest serving in an unpaid advisory capacity as an advisor for GrowQ360 providing strategic consultation and expertise for a wide range of healthcare ventures. All other authors declare no financial or non-financial competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8216300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557496787,"identity":"de510dbe-0afd-4978-9ce8-feac22c4ed2a","order_by":0,"name":"Jack 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The author O.P. discloses a financial competing interest as the Founder and equity holder of Blank Atom LLC, the commercial entity developing Open Mind, a multimodal diagnostic and therapeutic interface for psychiatric care. The findings of this study regarding the clinical risks of low explainability in AI inform the safety and design protocols relevant to Open Mind’s technology. The author O.P. discloses a non-financial competing interest serving in an unpaid advisory capacity as an advisor for GrowQ360 providing strategic consultation and expertise for a wide range of healthcare ventures. 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