Recent Developments in Artificial Intelligence for Ophthalmology: Advances, Challenges and Future Directions
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 2769
Editors
Interests: cataract surgery; refractive surgery; ocular infections; evidence-based medicine in ophthalmology; risk stratification in cataract surgery; retinal disorders; myopia control; presbyopia correction
Interests: human-computer interaction; data analytics, statistics, ethics and privacy; generative artificial intelligence (AI); machine learning; explainable AI in healthcare problems
Special Issues, Collections and Topics in MDPI journals
Interests: retina; diabetic retinopathy; macular degeneration; imaging; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) has emerged as a transformative technology in the field of ophthalmology, enhancing the diagnosis, treatment, and management of various eye diseases. From deep learning applications in retinal imaging to AI-powered surgical assistance, the potential of AI is revolutionizing how ophthalmic care is delivered. The continuous evolution of machine learning, natural language processing (NLP), large language models (LLM), and predictive analytics holds promise for improving patient outcomes, personalized care, and early detection of eye disorders.
This Special Issue invites original research and review articles that focus on clinically relevant AI applications in ophthalmology. We are particularly interested in manuscripts that provide real-world insights into how AI is integrated into clinical workflows, improve patient care, or present emerging tools that are transitioning from development to practical use in healthcare settings.
Submissions that discuss clinical outcomes, patient-centered AI applications, and solutions that enhance ophthalmic practices will be prioritized. Additionally, we encourage contributions that highlight the clinical utility of AI models and the challenges of integrating AI into existing healthcare systems.
Suggested topics include, but are not limited to:
- AI for Retinal Disease Diagnosis
- AI tools clinically applied for detecting diabetic retinopathy and macular degeneration.
- AI-driven screening technologies integrated into ophthalmic care.
- Automated Glaucoma Detection Using AI
- AI advancements in glaucoma diagnosis and progression monitoring.
- Predictive analytics for early glaucoma risk assessment.
- AI in Cataract Detection and Surgery
- Machine learning models for optimizing cataract surgery outcomes.
- AI-based pre-operative assessment and post-surgical recovery monitoring.
- AI-Powered Optical Coherence Tomography (OCT) Analysis
- Deep learning innovations for OCT-based diagnosis of retinal diseases.
- Automated analysis of OCT images using AI for early disease detection.
- Teleophthalmology and AI Integration
- AI applications in telemedicine for remote ophthalmic diagnosis and monitoring.
- AI-enhanced teleophthalmology systems for underserved populations.
- AI in Corneal Disease Diagnosis
- Deep learning models for detecting keratoconus and other corneal diseases.
- AI-assisted corneal transplantation planning and post-operative monitoring.
- AI in Ophthalmic Surgery Robotics
- AI-guided robotic platforms for precision ophthalmic surgeries.
- Machine learning applications for enhancing outcomes in robotic-assisted eye surgeries.
- Natural Language Processing (NLP) in Ophthalmology Reports
- NLP for automated analysis of ophthalmology medical records and research literature.
- AI-driven decision support systems in ophthalmology using NLP.
- LLM in Ophthalmology
- Applications of large language models (LLMs), such as ChatGPT, in ophthalmic research, education, and/or practice.
- Using LLMs for improving ophthalmology consultation services, medical records summarization, and patient care.
- Exploring the role of conversational AI tools in ophthalmology patient education, practice, and/or communication.
- Bias and Fairness in AI Ophthalmology Tools
- Ethical considerations and practical healthcare solutions to ensure fairness in clinical AI applications.
- Approaches to addressing algorithmic bias in ophthalmic tools and improving equitable patient care.
We look forward to receiving your contributions that will shape the future of AI in ophthalmology, focusing on its clinical relevance and practical implementation in healthcare settings.
Dr. Andrzej Grzybowski
Dr. Polat Goktas
Dr. Kai Jin
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare 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 2700 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
- ophthalmology
- retinal disease
- glaucoma
- deep learning
- cataract surgery
- optical coherence tomography (OCT)
- teleophthalmology
- large language models (LLMs)
- ChatGPT
- natural language processing (BLP)
- clinical AI integration
- bias and fairness in AI
- ophthalmic AI applications
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