Observational studyBMJ quality & safety2025
Large-scale observational study of AI-based patient and surgical material verification system in ophthalmology: real-world evaluation in 37 529 cases.
Observational study in BMJ quality & safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Assessing the Value for Money of AI-Assisted Technologies for Older Adults: Scoping Review of Economic Evaluations.Journal of medical Internet research · 2026Article
- Optimized Nursing Management for Ophthalmic Surgical Instruments: A Retrospective Study.Risk management and healthcare policy · 2026Article
- Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery.Clinics and practice · 2025Review
- Understanding the evidence for artificial intelligence in healthcare.BMJ quality & safety · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety system to prevent errors in patient identification, surgical laterality and intraocular lens (IOL) selection. This study aimed to evaluate its effectiveness in real-world ophthalmic surgical settings.
methodsIn this retrospective observational before-and-after implementation study, we analysed 37 529 ophthalmic surgeries (18 767 pre-implementation, 18 762 post implementation) performed at Tsukazaki Hospital, Japan, between 1 March 2019 and 31 March 2024. The AI system, integrated with the WHO surgical safety checklist, was implemented for patient identification, surgical laterality verification and IOL authentication.
resultsPost implementation, five medical errors (0.027%) occurred, with four in non-authenticated cases (where the AI system was not fully implemented or properly used), compared with one (0.0053%) pre-implementation (p=0.125). Of the four non-authenticated errors, two were laterality errors during the initial implementation period and two were IOL implantation errors involving unlearned IOLs (7.3% of cases) due to delayed AI updates. The AI system identified 30 near misses (0.16%) post implementation, vs 9 (0.048%) pre-implementation (p=0.00067), surgical laterality errors/near misses occurred at 0.039% (7/18 762) and IOL recognition at 0.29% (28/9713). The system achieved>99% implementation after 3 months. Authentication performance metrics showed high efficiency: facial recognition (1.13 attempts, 11.8 s), surgical laterality (1.05 attempts, 3.10 s) and IOL recognition (1.15 attempts, 8.57 s). Cost-benefit analysis revealed potential benefits ranging from US$181 946.94 to US$2 769 129.12 in conservative and intermediate scenarios, respectively.
conclusionsThe AI-based surgical safety system significantly increased near miss detection and showed potential economic benefits. However, errors in non-authenticated cases underscore the importance of consistent system use and integration with existing safety protocols. These findings emphasise that while AI can enhance surgical safety, its effectiveness depends on proper implementation and continuous refinement.
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