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How Clinicians See the Use of AI in Psychiatry
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How Clinicians See the Use of AI in Psychiatry

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Mental Health".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 616

Special Issue Editor

Dr. Georgios Floros

E-Mail Website
Guest Editor
2nd Department of Psychiatry, Aristotle University of Thessaloniki, 54624 Thessaloniki, Greece
Interests: behavioral addictions; psychopathology; personality disorders; psychotherapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of artificial intelligence (AI) as a practical application in everyday life has brought tremendous opportunities along with potential dangers. We invite researchers, clinicians, and interdisciplinary scholars to submit original research to this Special Issue titled “How Clinicians See the Use of AI in Psychiatry”. This Special Issue aims to explore the transformative role of artificial intelligence (AI) in psychiatric practice, addressing its opportunities and challenges. Against the backdrop of rapid AI advancements, we seek to understand clinicians’ perspectives on integrating AI tools into diagnostics, treatment planning, and patient care. The scope encompasses empirical studies and comprehensive reviews, with a focus on cutting-edge research such as AI-driven predictive models for mental health disorders, natural language processing for therapy chatbots, real-time patient monitoring systems, and ethical frameworks for AI deployment in psychiatry. Submissions should highlight practical applications, clinician acceptance, and potential impacts on patient outcomes, contributing to a nuanced understanding of AI’s role in shaping the future of psychiatric care. Please submit by the 25th of May 2026 to foster critical dialogue in this rapidly evolving field.

Dr. Georgios Floros
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • psychiatry
  • patient care
  • psychotherapy

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Published Papers (1 paper)

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15 pages, 1045 KB  
Systematic Review
AI at the Bedside of Psychiatry: Comparative Meta-Analysis of Imaging vs. Non-Imaging Models for Bipolar vs. Unipolar Depression
by Andrei Daescu, Ana-Maria Cristina Daescu, Alexandru-Ioan Gaitoane, Ștefan Maxim, Silviu Alexandru Pera and Liana Dehelean
J. Clin. Med. 2026, 15(2), 834; https://doi.org/10.3390/jcm15020834 - 20 Jan 2026
Viewed by 362
Abstract
Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered [...] Read more.
Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered protocol on protocols.io, we searched PubMed, Scopus, Europe PMC, Semantic Scholar, OpenAlex, The Lens, medRxiv, ClinicalTrials.gov, and Web of Science (2014–8 October 2025). Eligible studies developed/evaluated supervised ML classifiers for BD vs. MDD at first episode and reported test-set discrimination. AUCs were meta-analyzed on the logit (GEN) scale using random effects (REML) with Hartung–Knapp adjustment and then back-transformed. Subgroup (imaging vs. non-imaging), leave-one-out (LOO), and quality sensitivity (excluding high risk of leakage) analyses were prespecified. Risk of bias used QUADAS-2 with PROBAST/AI considerations. Results: Of 158 records, 39 duplicates were removed and 119 records screened; 17 met qualitative criteria; and 6 had sufficient data for meta-analysis. The pooled random-effects AUC was 0.84 (95% CI 0.75–0.90), indicating above-chance discrimination, with substantial heterogeneity (I2 = 86.5%). Results were robust to LOO, exclusion of two high-risk-of-leakage studies (pooled AUC 0.83, 95% CI 0.72–0.90), and restriction to higher-rigor validation (AUC 0.83, 95% CI 0.69–0.92). Non-imaging models showed higher point estimates than imaging models; however, subgroup comparisons were exploratory due to the small number of studies: pooled AUC ≈ 0.90–0.92 with I2 = 0% vs. 0.79 with I2 = 64%; test for subgroup difference Q = 7.27, df = 1, p = 0.007. Funnel plot inspection and Egger/Begg tests found that we could not reliably assess small-study effects/publication bias due to the small number of studies. Conclusions: AI/ML models provide good and robust discrimination of BD vs. MDD at first episode. Non-imaging approaches are promising due to higher point estimates in the available studies and practical scalability, but prospective evaluation is needed and conclusions about modality superiority remain tentative given the small number of non-imaging studies (k = 2). Full article
(This article belongs to the Special Issue How Clinicians See the Use of AI in Psychiatry)
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