ReviewPatterns (New York, N.Y.)2025
AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.
Review in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Early identification of Prader-Willi syndrome in newborns using facial phenotype-based machine learning.Pediatric research · 2026Article
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Evaluation of a Facial Dysmorphology Analysis Algorithm (Face2Gene) in Identifying Treacher Collins Syndrome Amongst Diverse Population.Orthodontics & craniofacial research · 2026Article
- ProptoView: AI-based digital exophthalmometry using multi-view facial images in a multinational validation study.Journal of translational medicine · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- Benchmarking clinical knowledge and multi-modal reasoning of large language models in liver cirrhosis.Scientific reports · 2026Article
- The role of agentic artificial intelligence in healthcare: a scoping review.NPJ digital medicine · 2026Article
- Management of Thyroid Eye Disease: A Comparison Between Three Recent Clinical Guidelines.Ophthalmology and therapy · 2026Review
- Sequential sensitivity analysis of multimodal large language models for rare orbital disease detection.Communications medicine · 2026Article
- Enhancing ocular sign detection: AI-based strategic segmentation for improved accuracy and privacy protection.NPJ digital medicine · 2026Article
- Face2Bone explainable AI model predicts osteoporosis risk from facial images in proof of concept study.Scientific reports · 2025Observational
- Article
- Facial Analysis for Plastic Surgery in the Era of Artificial Intelligence: A Comparative Evaluation of Multimodal Large Language Models.Journal of clinical medicine · 2025Article
- AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments-a narrative review.Frontiers in dental medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challenging because of the wide variability in human facial features and disease conditions. The integration of artificial intelligence (AI) into facial analysis represents a promising frontier offering a user-friendly, non-invasive, and cost-effective screening approach. This review explores the potential of AI-assisted facial analysis for identifying subtle facial phenotypes indicative of health disorders. First, we outline the technological framework essential for effective implementation in healthcare settings. Subsequently, we focus on the role of AI-assisted facial analysis in disease screening. We further expand our examination to include applications in health monitoring, support of treatment decision-making, and disease follow-up, thereby contributing to comprehensive disease management. Despite its promise, the adoption of this technology faces several challenges, including privacy concerns, model accuracy, issues with model interpretability, biases in AI algorithms, and adherence to regulatory standards. Addressing these challenges is crucial to ensure fair and ethical use. By overcoming these hurdles, AI-assisted facial analysis can empower healthcare providers, improve patient care outcomes, and enhance global health.
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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.