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Artificial Intelligence and Eye Disease
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Artificial Intelligence and Eye Disease

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Ophthalmology".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1854

Special Issue Editors

Prof. Dr. Andrzej Grzybowski

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Guest Editor
Foundation for Ophthalmology Development, Poznan, Poland
Interests: AI in ophthalmology; myopia prebention and treatment; cataract and refractive surgery; retina
Special Issues, Collections and Topics in MDPI journals
Dr. Louis Arnould

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Guest Editor
Department of Ophthalmology, Dijon University Hospital, Dijon, France
Interests: retinal imaging; AI in ophthalmology; glaucoma
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haoyu Chen

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Guest Editor
Joint Shantou International Eye Center, Shantou University & the Chinese University of Hong Kong, Shantou, China
Interests: retinal imaging; AI in ophthalmology; vitrectomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue entitled Artificial Intelligence (AI) and Eye Disease explores how AI technologies are revolutionizing the diagnosis, treatment, and management of ocular conditions. This collection highlights key advancements, clinical applications, and challenges in integrating AI into ophthalmology.

Key Themes and Highlights:

  1. AI in Early Diagnosis:
    • AI algorithms demonstrate high sensitivity and specificity in detecting diseases like diabetic retinopathy, age-related macular degeneration, cataract, and glaucoma.
  2. Advances in Imaging:
    • The AI-driven analysis or image processing of imaging modalities such as OCT, OCT-A, and fundus photography enables precise diagnostics and personalized treatment planning (i.e., IOL calculation).
  3. Treatment Monitoring:
    • AI tools help track disease progression and therapy responses, assisting clinicians in optimizing treatment.
  4. Future Directions:
    • Topics include AI’s integration with telemedicine, surgical robotics, and augmented reality, signaling its role in the future of ophthalmology.

Relevance and Impact:

This Special Issue showcases AI’s transformative potential to enhance clinical outcomes in ophthalmology. However, addressing challenges through collaboration among clinicians and data scientists remains critical. The insights offered highlight AI’s pivotal role in shaping the future of eye disease management.

Prof. Dr. Andrzej Grzybowski
Dr. Louis Arnould
Prof. Dr. Haoyu Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • machine learning
  • deep learning
  • ophthalmology
  • eye disease
  • eye disorder

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

13 pages, 1246 KiB  
Article
Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders
by Ceren Durmaz Engin, Mahmut Ozan Gokkan, Seher Koksaldi, Mustafa Kayabasi, Ufuk Besenk, Mustafa Alper Selver and Andrzej Grzybowski
J. Clin. Med. 2025, 14(8), 2774; https://doi.org/10.3390/jcm14082774 - 17 Apr 2025
Viewed by 345
Abstract
Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning [...] Read more.
Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning (ML) model in classifying optical coherence tomography (OCT) images of VMI disorders. Materials and Methods: A balanced dataset of OCT images across five classes—normal, epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitreomacular traction (VMT)—was used. The expert-designed model combined ResNet-50 and EfficientNet-B0 architectures with Monte Carlo cross-validation. The AutoML model was created on Google Vertex AI, which handled data processing, model selection, and hyperparameter tuning automatically. Performance was evaluated using average precision, precision, and recall metrics. Results: The expert-designed model achieved an overall balanced accuracy of 95.97% and a Matthews Correlation Coefficient (MCC) of 94.65%. Both models attained 100% precision and recall for normal cases. For FTMH, the expert model reached perfect precision and recall, while the AutoML model scored 97.8% average precision, and 97.4% recall. In VMT detection, the AutoML model showed 99.5% average precision with a slightly lower recall of 94.7% compared to the expert model’s 95%. For ERM, the expert model achieved 95% recall, while the AutoML model had higher precision at 93.9% but a lower recall of 79.5%. In LMH classification, the expert model exhibited 95% precision, compared to 72.3% for the AutoML model, with similar recall for both (88% and 87.2%, respectively). Conclusions: While the AutoML model demonstrated strong performance, the expert-designed model achieved superior accuracy across certain classes. AutoML platforms, although accessible to healthcare professionals, may require further advancements to match the performance of expert-designed models in clinical applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Eye Disease)
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9 pages, 991 KiB  
Article
Triage of Patient Messages Sent to the Eye Clinic via the Electronic Medical Record: A Comparative Study on AI and Human Triage Performance
by Abdulaziz Alsumait, Sharanya Deshmukh, Christine Wang and Christopher T. Leffler
J. Clin. Med. 2025, 14(7), 2395; https://doi.org/10.3390/jcm14072395 - 31 Mar 2025
Viewed by 887
Abstract
Background/Objectives: Assess the ability of ChatGPT-4 (GPT-4) to effectively triage patient messages sent to the general eye clinic at our institution. Methods: Patient messages sent to the general eye clinic via MyChart were de-identified and then triaged by an ophthalmologist-in-training (MD) [...] Read more.
Background/Objectives: Assess the ability of ChatGPT-4 (GPT-4) to effectively triage patient messages sent to the general eye clinic at our institution. Methods: Patient messages sent to the general eye clinic via MyChart were de-identified and then triaged by an ophthalmologist-in-training (MD) as well as GPT-4 with two main objectives. Both MD and GPT-4 were asked to direct patients to either general or specialty eye clinics, urgently or nonurgently, depending on the severity of the condition. Main Outcomes: GPT-4s ability to accurately direct patient messages to (1) a general or specialty eye clinic and (2) determine the time frame within which the patient needed to be seen (triage acuity). Accuracy was determined by comparing percent agreement with recommendations given by GPT-4 with those given by MD. Results: The study included 139 messages. Percent agreement between the ophthalmologist-in-training and GPT-4 was 64.7% for general/specialty clinic recommendation and 60.4% for triage acuity. Cohen’s kappa was 0.33 and 0.67 for specialty clinic and triage urgency, respectively. GPT-4 recommended a triage acuity equal to or sooner than ophthalmologist-in-training for 93.5% of cases and recommended a less urgent triage acuity in 6.5% of cases. Conclusions: Our study indicates an AI system, such as GPT-4, should complement rather than replace physician judgment in triaging ophthalmic complaints. These systems may assist providers and reduce the workload of ophthalmologists and ophthalmic technicians as GPT-4 becomes more adept at triaging ophthalmic issues. Additionally, the integration of AI into ophthalmic triage could have therapeutic implications by ensuring timely and appropriate care, potentially improving patient outcomes by reducing delays in treatment. Combining GPT-4 with human expertise can improve service delivery speeds and patient outcomes while safeguarding against potential AI pitfalls. Full article
(This article belongs to the Special Issue Artificial Intelligence and Eye Disease)
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