ArticleTranslational vision science & technology2025
OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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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
4 citing papers in PubMed.
- OphthoChat: A HIPAA-Compliant, Artificial Intelligence-Driven Natural-Language Chatbot for Ophthalmic Electronic Medical Record Querying and Analysis.Ophthalmology science · 2026Article
- AI-guided surgical decision support system for corneal laceration repair: a prospective, non-randomized controlled feasibility study.Scientific reports · 2026Article
- Author Response: OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis.Translational vision science & technology · 2026Article
- In Response To: OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis.Translational vision science & technology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Retrospective chart reviews in ophthalmology are essential for gaining clinical insights, but they remain labor-intensive and prone to error. Despite digitization through electronic health records, extracting and interpreting lengthy, unstructured patient histories remains challenging, particularly in ophthalmology, which relies heavily on both imaging and text-based reports. We introduce OphthoACR, a Health Insurance Portability and Accountability Act-compliant artificial intelligence (AI)-powered tool for automated chart review and cohort analyses in ophthalmology. Methods: OphthoACR was applied to extract 16 variables of increasing task difficulty from the complete chart histories of 91 patients who underwent secondary intraocular lens surgery at the Columbia University Irving Medical Center from January 2020 to August 2024, for a total of 5834 unique documents. The tool integrates a fine-tuned large language model into a robust pipeline to extract and contextualize unstructured clinical data, including operative reports and imaging documents. OphthoACR's performance was compared to manual and AI-assisted chart reviews. Results: OphthoACR achieved 94% accuracy in extracting variables of interest, significantly outperforming manual review (83%). It demonstrated 97% specificity, 92% sensitivity, and a Cohen's κ of 0.70, indicating robust agreement. Average time for OphthoACR to process a patient chart was 80 seconds, a 95% reduction compared to the manual review's average of 25.2 minutes. For cohort-wide processing, the improvement was 99.9% due to parallel processing of patients' charts. Conclusions: OphthoACR significantly improves the accuracy and efficiency of ophthalmology chart reviews, offering an unprecedented automated solution to analyze large patient cohorts. Translational Relevance: OphthoACR provides end-to-end automation of retrospective chart reviews, transforming the currently labor-intensive manual process into an efficient, accurate, and scalable solution that substantially enhances clinical research.
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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.