ArticleFrontiers in artificial intelligence2025
Detecting body dysmorphic disorder in the age of algorithms.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Body Dysmorphic Disorder (BDD) is increasingly recognized in the aesthetic practice, yet it remains underdiagnosed and often misunderstood. With its high prevalence, particularly in cosmetic consultations, BDD poses significant ethical and clinical challenges. Aesthetic providers must be vigilant in identifying at-risk individuals and prioritizing psychological well-being alongside procedural outcomes. Artificial Intelligence (AI), with its capacity to analyze behavioral patterns, automate screening tools, and detect subtle indicators of cognitive distortion, presents a new frontier in managing BDD. However, integrating AI into clinical practice requires caution to prevent reinforcing appearance-focused biases and to ensure privacy and fairness. This commentary discusses the opportunities, limitations, and ethical considerations of leveraging AI to assist clinicians in detecting BDD, fostering safer patient outcomes, and advancing the compassionate practice of aesthetic medicine. AI should not accelerate aesthetic procedures but promote reflective, ethically sound decision-making. When integrated responsibly, it can enhance recognition of BDD, support psychological safety, and preserve patient trust through transparency, data protection, and clinician oversight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.