Georgiou, M. et al. Phenotyping and genotyping inherited retinal diseases: molecular genetics, clinical and imaging features, and therapeutics of macular dystrophies, cone and cone-rod dystrophies, rod-cone dystrophies, Leber congenital amaurosis, and cone dysfunction syndromes. Prog. Retin. Eye Res. 100, 101244 (2024).
Ben-Yosef, T. Inherited retinal diseases. Int. J. Mol. Sci. 23, 13467 (2022).
Schneider, N. et al. Inherited retinal diseases: linking genes, disease-causing variants, and relevant therapeutic modalities. Prog. Retin. Eye Res. 89, 101029 (2022).
Sullivan, L. S. & Daiger, S. P. RetNet: Retinal Information Network. https://retnet.org/
Britten-Jones, A. C. et al. The diagnostic yield of next generation sequencing in inherited retinal diseases: a systematic review and meta-analysis. Am. J. Ophthalmol. 249, 57–73 (2023).
Sheck, L. H. N. et al. Panel-based genetic testing for inherited retinal disease screening 176 genes. Mol. Genet. Genomic Med. 9, e1663 (2021).
Nguyen, Q. et al. Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene). BMJ Open 13, e071043 (2023).
Wong, W. et al. Inherited retinal disease pathway in the UK: a patient perspective and the potential of AI. Br. J. Ophthalmol. 109, 1266–1271 (2025).
Pierce, E. A. et al. Gene editing for CEP290-associated retinal degeneration. N. Engl. J. Med. 390, 1972–1984 (2024).
Botto, C. et al. Early and late stage gene therapy interventions for inherited retinal degenerations. Prog. Retin. Eye Res. 86, 100975 (2022).
Suh, S., Choi, E. H., Raguram, A., Liu, D. R. & Palczewski, K. Precision genome editing in the eye. Proc. Natl Acad. Sci. USA 119, e2210104119 (2022).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
Ting, D. S. W. et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318, 2211–2223 (2017).
Dai, L. et al. A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med. 30, 584–594 (2024).
Meng, Z. et al. Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study. Lancet Digit. Health 7, 100868 (2025).
Fujinami-Yokokawa, Y. et al. Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques. Br. J. Ophthalmol. 105, 1272–1279 (2021).
Fujinami-Yokokawa, Y. et al. Prediction of causative genes in inherited retinal disorders from spectral-domain optical coherence tomography utilizing deep learning techniques. J. Ophthalmol. 2019, 1691064 (2019).
Shah, M., Roomans Ledo, A. & Rittscher, J. Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning. Acta Ophthalmol. 98, e715–e721 (2020).
Miere, A. et al. Deep learning to distinguish ABCA4-related Stargardt disease from PRPH2-related pseudo-Stargardt pattern dystrophy. J. Clin. Med. 10, 5742 (2021).
Pontikos, N. et al. Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene. Nat. Mach. Intell. 7, 967–978 (2025).
100,000 Genomes Project Pilot Investigators et al. 100,000 Genomes pilot on rare-disease diagnosis in health care—preliminary report. N. Engl. J. Med. 385, 1868–1880 (2021).
Conway, M. P. et al. The role of the ophthalmic genetics multidisciplinary team in the management of inherited retinal degenerations—a case-based review. Life (Basel) 14, 107 (2024).
Parker, M. A. et al. Test–retest variability of functional and structural parameters in patients with Stargardt disease participating in the SAR422459 gene therapy trial. Transl. Vis. Sci. Technol. 5, 10 (2016).
Guziewicz, K. E. et al. BEST1 gene therapy corrects a diffuse retina-wide microdetachment modulated by light exposure. Proc. Natl Acad. Sci. USA 115, E2839–E2848 (2018).
Rahman, N., Georgiou, M., Khan, K. N. & Michaelides, M. Macular dystrophies: clinical and imaging features, molecular genetics and therapeutic options. Br. J. Ophthalmol. 104, 451–460 (2020).
A preliminary assessment of the potential impact of rare diseases on the NHS. https://imperialcollegehealthpartners.com/a-preliminary-assessment-of-the-potential-impact-of-rare-diseases-on-the-nhs/ (Imperial College Health Partners, 2018).
Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, e214–e215 (2022).
Kundu, S. AI in medicine must be explainable. Nat. Med. 27, 1328 (2021).
Desai, A. N. Artificial intelligence: promise, pitfalls, and perspective. JAMA 323, 2448–2449 (2020).
Miere, A. et al. Deep learning-based classification of inherited retinal diseases using fundus autofluorescence. J. Clin. Med. 9, 3303 (2020).
Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).
Kim, C. et al. Transparent medical image AI via an image-text foundation model grounded in medical literature. Nat. Med. 30, 1154–1165 (2024).
Guo, L. L. et al. A multi-center study on the adaptability of a shared foundation model for electronic health records. NPJ Digit. Med. 7, 171 (2024).
Wang, M. et al. Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model. Nat. Commun. 16, 5528 (2025).
Han, R. et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit. Health 6, e367–e373 (2024).
Angus, D. C. Randomized clinical trials of artificial intelligence. JAMA 323, 1043–1045 (2020).
Lawrence, N. D. A unifying probabilistic perspective for spectral dimensionality reduction: insights and new models. J. Mach. Learn. Res. 13, 1609–1638 (2012).
Consultation on development of standards for characterization of vision loss and visual functioning: Geneva, 4–5 September 2003. https://iris.who.int/handle/10665/68601 (World Health Organization, 2003).
Vasey, B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat. Med. 28, 924–933 (2022).
Sun, X. An Al-based clinician-decision support system for inherited retinal diseases: a multicenter clinical validation trial. Zenodo. https://zenodo.org/records/20489924 (2026).
