New Collection: Multimodal AI in Retinal Disease: Imaging, Clinical Data, and Real‑World Endpoints
Published in Computational Sciences and General & Internal Medicine
Can artificial intelligence deliver meaningful improvements in retinal care?
Artificial intelligence is rapidly transforming ophthalmology, with new opportunities to improve the detection, monitoring and treatment of retinal disease. Yet the greatest clinical impact will come from AI systems that move beyond analysing single images and instead integrate imaging, clinical information and real-world patient outcomes to support decision-making in routine practice.
To help advance this next generation of research, International Ophthalmology is pleased to launch a new Special Collection:
Multimodal AI in Retinal Disease: Imaging, Clinical Data, and Real-World Endpoints
This collection seeks high-quality research that develops, validates and implements clinically relevant AI approaches for retinal diseases. Particular emphasis is placed on studies that combine retinal imaging with longitudinal clinical, demographic, laboratory and treatment data to generate insights that are useful in real-world care.
Topics of interest include:
- Multimodal AI integrating fundus photography, OCT, OCTA, fluorescein angiography, ultra-widefield imaging and other retinal imaging modalities
- Imaging biomarkers and AI-enabled disease phenotyping
- Prediction of disease onset, progression and vision-related outcomes
- Longitudinal disease monitoring and personalised risk stratification
- Treatment response assessment and decision support for retinal therapies
- External, prospective, multicentre and real-world validation studies
- Clinical workflow integration and human–AI collaboration
- Safety, transparency, robustness, fairness and equity in retinal AI
- Patient-centred outcomes and health-system impact analyses
What are we looking for?
We welcome studies with:
- Strong clinical relevance
- Rigorous methodology
- Transparent dataset development
- Independent validation cohorts
- Real-world applicability and implementation insights
Original research articles, systematic reviews and methodological papers are all encouraged, provided they contribute to the safe, effective and equitable translation of AI into retinal care. Please do not submit AI engineering-focused papers.
Why publish in International Ophthalmology?
International Ophthalmology provides a global platform for clinically relevant ophthalmic research. The journal reaches a broad international audience of clinicians, researchers and healthcare professionals, with more than 850,000 annual downloads and a median time to first decision of 11 days.
This collection will be particularly relevant to:
- Retina specialists
- Comprehensive ophthalmologists
- Ophthalmic imaging researchers
- Data scientists and AI developers
- Clinical informaticians
- Industry innovators
- Health services and outcomes researchers
📅 Submission deadline: 31 March 2027
👉 Learn more and submit your manuscript:
https://link.springer.com/collections/ahfdedajbg
As AI continues to evolve, robust evidence that demonstrates clinical utility, generalisability and impact on patient outcomes is increasingly important. We look forward to receiving submissions that help bridge the gap between technological innovation and everyday retinal care.
I look after a portfolio of Ophthalmology journals within Biomedicine journals at Springer Nature.
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Multimodal AI in Retinal Disease: Imaging, Clinical Data, and Real‑World Endpoints
Artificial intelligence is increasingly shaping the diagnosis, monitoring, and management of retinal disease. However, clinically useful AI must move beyond isolated image classification toward models that integrate retinal imaging with longitudinal clinical information and are evaluated against outcomes that matter in real-world care. This Special Collection invites high-quality research that advances multimodal, clinically grounded, and responsibly validated AI for retinal diseases. We particularly welcome studies that combine retinal imaging with clinical, demographic, laboratory, treatment, and longitudinal health-record data to support meaningful decision-making across screening, diagnosis, prognosis, monitoring, and treatment.
Topics of interest include, but are not limited to:
• Multimodal AI and retinal foundation models integrating color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), fluorescein angiography, ultra-widefield imaging, and other retinal modalities;
• Imaging-based biomarkers and AI-enabled phenotyping for retinal diseases;
• Prediction of disease onset, progression, and vision-related outcomes;
• Longitudinal disease monitoring and personalized risk stratification;
• Treatment-response assessment, therapeutic monitoring, and decision support for retinal interventions;
• External, prospective, multicenter, and real-world validation of retinal AI systems;
• Clinical workflow integration, human-AI collaboration, and implementation in routine eye care;
• Safety, robustness, transparency, fairness, generalizability, and equity in retinal AI;
• Patient-centered outcomes, health-system impact, and real-world endpoints relevant to retinal care.
Submissions should demonstrate clear clinical relevance and rigorous methodology. Studies reporting transparent dataset construction, appropriate reference standards, independent validation, subgroup analysis, and meaningful outcome assessment are especially encouraged. The Collection welcomes original research, systematic reviews, and methodological studies, that help translate multimodal AI into safer, more effective, and more equitable retinal care.
Publishing Model: Hybrid
Deadline: Mar 31, 2027
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