Purpose <p>Inherited retinal dystrophies (IRDs) are a heterogeneous group of diseases characterized by genotypic and phenotypic variability and are among the leading causes of blindness in the working-age population in developed countries. Despite advancements in genetic testing, obtaining a molecular diagnosis remains a lengthy and challenging process due to limited resources and high costs. Artificial intelligence (AI) offers promising solutions to streamline diagnostic pathways and improve clinical outcomes.</p> Methods <p>We reviewed current literature on AI-driven approaches in IRDs.</p> Results <p>AI models have demonstrated potential in predicting disease-causing variants, distinguishing phenotypically similar IRDs, and segmenting retinal layers. However, their routine clinical adoption remains limited. Efforts are ongoing to lower genetic testing costs, reduce diagnostic delays, and enhance molecular diagnostic yield. Future applications may include genetic counselling, prediction of IRD progression, evaluation of novel mutation pathogenicity, identification of gene therapy candidates, and prediction of personalized outcomes post-treatment. Barriers to widespread AI adoption include a lack of standardized nomenclature, ethnic and regional variations, inconsistent multimodal imaging quality, data ownership concerns, clinical safety, cybersecurity, and the opaque nature of AI decision-making, known as the “black-box” problem.</p> Conclusions <p>AI models have shown promise in diagnosing IRDs, predicting causative genes, and segmenting retinal layers. While AI has the potential to revolutionize IRD diagnosis and management, significant challenges must be addressed. Continued research and collaboration are crucial to overcoming these barriers and unlocking AI’s full potential in IRDs.</p>