Artificial intelligence in ophthalmic electronic medical records, health records, and registries

The integration of electronic health records (EHRs) and electronic medical records (EMRs) is fundamentally transforming ophthalmology by generating vast amounts of clinical data. This shift, coupled with the advancements in Big Data analytics and artificial intelligence (AI), is enhancing patient care, optimizing treatment strategies, and streamlining healthcare delivery. Beyond evaluating imaging and numerical data, AI can be seamlessly integrated into EMRs and EHRs, creating unprecedented opportunities for research, improved clinical decision-making, and advanced patient management. While EMRs represent digital charts within a single institution, EHRs offer a broader, longitudinal collection of patient information from multiple providers, forming the essential foundation for “big data” initiatives such as ophthalmic registries. The transition to a paperless healthcare future, driven by EMRs, offers significant benefits by eliminating inefficient paper-based processes and providing instant access to vital patient information at the point of care, thereby enhancing patient safety and facilitating informed decisions. The article explores the evolving landscape of ophthalmic data, highlighting the crucial role of natural language processing in extracting insights from unstructured text within EHRs, and the application of supervised machine learning and deep learning for disease diagnosis, risk assessment, and treatment planning. It details various prominent global ophthalmic registries that serve as “big data” repositories, and reviews numerous studies demonstrating AI’s effectiveness in predicting ocular diseases, triaging patients, and informing Clinical Decision Support Systems. This synergy of digital records, big data, and AI promises a future of more precise, proactive, and collaborative ophthalmic care.