Multimodal artificial intelligence in retinal vascular and neovascular macular diseases: a systematic review of diagnostic and prognostic applications
Multimodal AI demonstrates superior diagnostic and prognostic performance compared to unimodal models and, in some cases, outperforms expert clinicians in managing retinal vascular diseases. Integrating complementary data sources, such as OCT, fundus imaging, and clinical information, enhances model accuracy and generalizability.
Authors
- Mohamed Baklola (ORCID: https://orcid.org/0000-0002-5572-0852)
- Ghadah Almutairi (ORCID: https://orcid.org/0009-0004-6352-8571)
- Amani A Al Ghramah
- Razan Alshehri
- Naji Al-Bawah
- Khaled AbdelKhalique abdelshafi Ghaith
- Amal Turki Aljuaid
- Abdulmajeed Al Khathami
- Raghad Sayyaf Alshahrani
- Orjuwan Turki Aljuaid
- Reema Hasan AlGarni
- Meshari Ahmed Alalyani
- Rawan Saud Aldreweash
- Safiyah Abdullah Albishi
- Rehab Alsaeed
Institutions
- Mansoura University (EG)
- Sana'a University (YE)
- King Saud bin Abdulaziz University for Health Sciences (SA)
- Majmaah University (SA)
- King Saud University (SA)
- University of Bisha (SA)
- King Fahad Hospital Jeddah (SA)
- National Guard Health Affairs (SA)
- Al Baha University (SA)
- Imam Abdulrahman Bin Faisal University (SA)
Publication Details
- Journal
- BMC Ophthalmology
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1186/s12886-025-04561-3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00