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Diagnostics
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Artificial Intelligence Application in Cornea and External Diseases
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Artificial Intelligence Application in Cornea and External Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3937

Special Issue Editors

Dr. Ali R. Djalilian

E-Mail Website
Guest Editor
Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL 60612, USA
Interests: bioengineering; ocular surface; stem cells
Dr. Mohammad Soleimani

E-Mail Website
Guest Editor
Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL 60612, USA
Interests: cornea; artificial intelligence; ocular surface diseases

Special Issue Information

Dear Colleagues,

AI has attracted the attention of diverse medical disciplines. It has exhibited promise in enhancing the efficiency of healthcare via supporting automated clinical diagnoses through complex analyses across numerous dimensions. Moreover, it has the potential to decrease the reliance on expensive diagnostic tools and personnel, facilitating improved healthcare delivery. Furthermore, the recent global health crisis caused by the COVID-19 pandemic has significantly affected patients and services in the field of ophthalmology, underscoring the significance of digital healthcare. Within the realm of ophthalmology, AI has proven to possess diagnostic precision akin to that of medical professionals in identifying conditions such as macular degeneration, glaucoma, and diabetic retinopathy. However, there is still a need for expanded research and development in the areas of the cornea and anterior segment. Scientific teams worldwide have evaluated the effectiveness of AI algorithms in diagnosing various diseases of the anterior segment of the eye. In this Special Issue, we aim to cover the latest innovations and advancements in the application of AI in the diagnosis, monitoring, and management of cornea and ocular surface conditions.

Dr. Ali R. Djalilian
Dr. Mohammad Soleimani
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. Diagnostics 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
  • cornea
  • ocular surface
  • ophthalmology
  • external eye diseases
  • mobile health
  • telemedicine

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Published Papers (3 papers)

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Research

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15 pages, 747 KiB  
Article
Comparative Analysis of LLMs in Dry Eye Syndrome Healthcare Information
by Gloria Wu, Hrishi Paliath-Pathiyal, Obaid Khan and Margaret C. Wang
Diagnostics 2025, 15(15), 1913; https://doi.org/10.3390/diagnostics15151913 - 30 Jul 2025
Viewed by 154
Abstract
Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims [...] Read more.
Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article
(This article belongs to the Special Issue Artificial Intelligence Application in Cornea and External Diseases)
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Review

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20 pages, 944 KiB  
Review
Cornea Oculomics: A Clinical Blueprint for Extending Corneal Diagnostics and Artificial Intelligence in Systemic Health Insights
by Ryung Lee, Rahul Kumar, Alex Weaver, Ji Hyun Kim, Arriyan Raza, Joshua Ong, Ethan Waisberg and Rahul Pandit
Diagnostics 2025, 15(5), 643; https://doi.org/10.3390/diagnostics15050643 - 6 Mar 2025
Viewed by 1292
Abstract
Oculomics is an emerging field that leverages ophthalmic imaging data to identify biomarkers of systemic disease, facilitating early diagnosis and risk stratification. Despite its growing recognition, gaps remain in the literature regarding the clinical applications of oculomics. Various systemic diseases—including metabolic disorders (e.g., [...] Read more.
Oculomics is an emerging field that leverages ophthalmic imaging data to identify biomarkers of systemic disease, facilitating early diagnosis and risk stratification. Despite its growing recognition, gaps remain in the literature regarding the clinical applications of oculomics. Various systemic diseases—including metabolic disorders (e.g., diabetes mellitus), infectious diseases (e.g., COVID-19), neurodegenerative diseases (e.g., dementia), hematologic disorders (e.g., thalassemia), autoimmune conditions (e.g., rheumatoid arthritis), and genetic syndromes (e.g., Fabry disease)—exhibit ocular manifestations detectable through in vivo confocal microscopy and anterior segment optical coherence tomography, among other imaging modalities. Increasing evidence supports the role of corneal imaging in identifying systemic disease biomarkers, a process further enhanced by artificial intelligence-driven analyses. This review synthesizes the current findings on corneal biomarkers of systemic disease, their ophthalmic imaging correlates, and the expanding role of corneal oculomics in translational medicine. Additionally, we explore future directions for integrating oculomics into clinical practice and biomedical research. Full article
(This article belongs to the Special Issue Artificial Intelligence Application in Cornea and External Diseases)
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Other

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22 pages, 2102 KiB  
Systematic Review
Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations
by Alireza Hayati, Mohammad Reza Abdol Homayuni, Reza Sadeghi, Hassan Asadigandomani, Mohammad Dashtkoohi, Sajad Eslami and Mohammad Soleimani
Diagnostics 2025, 15(6), 737; https://doi.org/10.3390/diagnostics15060737 - 15 Mar 2025
Cited by 2 | Viewed by 1870
Abstract
Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables [...] Read more.
Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools. Full article
(This article belongs to the Special Issue Artificial Intelligence Application in Cornea and External Diseases)
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