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The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising patient numbers accompanied by ongoing developments of therapeutic approaches. Concurrent advances in imaging modalities provide eye care professionals with a large amount of data for each patient. Furthermore, with continuous progress in therapeutics, there is an unmet need for reliable structural and functional biomarkers in clinical trials and practice to optimize personalized patient care and evaluate individual responses to treatment. A fast and objective solution is Artificial intelligence (AI), which has revolutionized assessment of AMD in all disease stages. Reliable and validated AI-algorithms can aid to overcome the growing number of patients, visits and necessary treatments as well as maximize the benefits of multimodal imaging in clinical trials. Therefore, there are ongoing efforts to develop and validate automated algorithms to unlock more information from datasets allowing automated assessment of disease activity and disease progression. This review aims to present selected AI algorithms, their development, applications and challenges regarding assessment and prediction of AMD progression., Competing Interests: Competing interests SF, KB, SR: No financial disclosures. GSR: Apellis (C), Bayer (C), RetInSight (F), Roche (C), Boehringer Ingelheim (C). US-E: Abbvie (C), Apellis Pharmaceuticals (F, C), Aviceda (C), Complement therapeutics (C), Galimedix (C), Genentech (F), Janssen (C), Heidelberg Engineering (C, F), Kodiak (F), Kriya (C), Novartis (F), ONL (C), Roche (C, F), RetInSight (C), Stealth Therapeutics (C), Topcon (F, C)., (© 2024. The Author(s).)