ReviewJournal of current ophthalmology
Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.
Review in Journal of current 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.
- Bridging Therapeutics and Nanotechnology in Dry Eye Disease: A Review of Risk Factors, Drug Delivery Barriers, and Recent and Future Endeavors for Disease Management.AAPS PharmSciTech · 2026Review
- Dry Eye Disease: From Mechanisms to Management and Future Directions.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To introduce the applications of artificial intelligence (AI) in the clinical diagnosis and treatment of dry eye (DE) and to explore its common workflows, effectiveness, challenges, and future development directions. Methods: This article conducts a literature review, focusing on the applications of AI in the diagnosis and treatment of DE. The primary search terms include "artificial intelligence", "machine learning", "deep learning", "computer-aided", and "Dry Eye". Results: A total of 48 relevant original studies were identified, and their algorithms, sample characteristics, and data types were summarized. Through data analysis and image recognition, AI assists in DE examinations, identifies risk factors, aids diagnosis, and manages and monitors treatment. AI excels in enhancing diagnostic efficiency, accuracy, and objectivity, and shows promise in cloud-based treatment management. However, the applications of AI in DE also face certain challenges that need to be addressed. Conclusions: AI has the potential to revolutionize the diagnosis of DE and recommend personalized treatment strategies. This review summarizes existing challenges and offers clinicians and researchers a comprehensive, objective overview of AI applications and workflows in DE.
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.