ReviewTherapeutic advances in ophthalmology
Facial artificial intelligence in ophthalmology and medicine: fundamental and transformative applications.
Review in Therapeutic advances in ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A multi-category eye image dataset for AI-based segmentation and analysis of eyelid disorders.Scientific data · 2026Article
- Validation of a Machine Learning-Derived Algorithm for the Measurement of Facial First Impressions.Aesthetic plastic surgery · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) in healthcare, particularly in the domain of facial processing tasks, has witnessed substantial growth in the 21st century. However, this requires sufficient appraisal for clinicians and researchers to adequately understand nomenclature and key concepts commonly used in this field. This article aims to elucidate the diverse applications of facial processing tasks, such as facial landmark extraction, face detection, face tracking, facial expression recognition and action unit detection, and their relevance to ophthalmology and other medical specialties. The keywords 'ophthalmology', 'facial artificial intelligence', 'facial recognition' and 'periorbital measurements' were used on PubMed and Ovid, between September 2012 and September 2022, to identify and screen for eligible articles. Studies reporting on human patients in ophthalmology, plastic, maxillofacial and cosmetic surgery with ocular lesions whose facial biometrics were processed by AI and written in the English language were included. A total of 291 and 513 articles were identified on PubMed and Ovid respectively. Twenty articles were included for analysis in this literature review after duplicates, inaccessible articles and articles without full manuscripts were excluded. Although fully automated algorithms can share the workload in healthcare systems and relieve strains on manpower, rigorous testing is crucial, followed by the challenges of convincing management bodies that it would work in reality, coupled with the costs of implementing specialised functional hardware and software. While patients have a valid concern that it would reduce physical contact with clinicians, it is important for clinicians not to replace clinical decision-making with AI alone.
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.