ArticleMedical science monitor : international medical journal of experimental and clinical research2025
AI-Based Facial Analysis vs Self-Report: A Pilot Study Using Insights From Pre-Procedural Psychological Tests.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence for facial attractiveness assessment in orthodontics: model development and evaluation.European journal of orthodontics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Aesthetic procedures aligning with societal beauty standards are gaining popularity, often driven by unrealistic beauty ideals. However, these treatments frequently disappoint individuals with low self-esteem and distorted self-images. AI tools offer potential for a more objective assessment, generating pre-treatment simulations. This study explores the relationship between psychological profiles and AI evaluations. MATERIAL AND METHODS Forty-seven participants underwent psychological tests (TIPI-PL, SWLS, Rosenberg's Self-Esteem Scale, and Self-Perception Scale). An AI neural network analyzed facial images across 7 traits: attractiveness, competence, restfulness, joy, dominance, health, and trustworthiness. The AI application dedicated to tablets that we used is available online. After taking a photo of the face, the application creates a heatmap with facial features pictured on the scale. Participants also self-assessed these traits using the SPS scale. Pearson's r and the Mann-Whitney U tests were used to examine correlations and compare AI and self-assessment results. RESULTS The AI-based analysis assessed female faces as more attractive (P<0.001), more rested (P=0.004), happier (P<0.001) and more trustworthy (P<0.001). There was a significant association trending towards statistical significance between 'Competent' and 'Rested' AI results with TIPI Extraversion (P=0.045). None of the other results of the analysis of psychological status, personality types, self-esteem, and well-being reached statistical significance. CONCLUSIONS AI applications present promising opportunities for enhancing medical practices. In this study we observed some limitations, especially concerning the intricate interplay between physical appearance, psychological factors, and AI facial evaluation. The SPS scale was significantly correlated with other psychological tests, although AI ratings were generally lower than self-assessments. Therefore, AI should not be the only tool used for planning aesthetic procedures.
Indexed as
Identifiers
What OpenQuestion holds
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.