ArticleScientific reports2025
The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.Ophthalmology science · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Progressive Insights into 3D Bioprinting for Corneal Tissue Restoration.Advanced healthcare materials · 2026Review
- Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.Scientific reports · 2025Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed an AI system capable of automatically classifying anterior eye images as either normal or indicative of corneal diseases. This study aims to investigate the influence of AI's misleading guidance on ophthalmologists' responses. This cross-sectional study included 30 cases each of infectious and immunological keratitis. Responses regarding the presence of infection were collected from 7 corneal specialists and 16 non-corneal-specialist ophthalmologists, first based on the images alone and then after presenting the AI's classification results. The AI's diagnoses were deliberately altered to present a correct classification in 70% of the cases and incorrect in 30%. The overall accuracy of the ophthalmologists did not significantly change after AI assistance was introduced [75.2 ± 8.1%, 75.9 ± 7.2%, respectively (P = 0.59)]. In cases where the AI presented incorrect diagnoses, the accuracy of corneal specialists before and after AI assistance was showing no significant change [60.3 ± 35.2% and 53.2 ± 30.9%, respectively (P = 0.11)]. In contrast, the accuracy for non-corneal specialists dropped significantly from 54.5 ± 27.8% to 31.6 ± 29.3% (P < 0.001), especially in cases where the AI presented incorrect options. Less experienced ophthalmologists were misled due to incorrect AI guidance, but corneal specialists were not. Even with the introduction of AI diagnostic support systems, the importance of ophthalmologist's experience remains crucial.
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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.