Eye diseases continue to pose a serious global health issue, partly because the eye itself is such a difficult organ to treat. Special construction and protective barriers reduce the bioavailability, the extent to which drugs can penetrate it and reach the target. It usually requires a few doses for patients, so they must take them to keep on track and receive effective treatment. To overcome this, nanocarrier based delivery systems have been explored and have great potential in terms of improved drug delivery. They can increase drug retention, increase penetration and allow for controlled drug release and increased efficacy. However, the process of creating and optimizing such nanosized systems and transferring them to the clinic is a challenging and complicated process. In recent years, Artificial Intelligence has played a big part in finding solutions to these challenges. From drug discovery to drug formulation development, AI helps to streamline different aspects of drug development, including drug development image analysis, clinical trial planning, and even personalized medicine. However, researchers are enhancing their capacity to predict drug behavior and host responses by tools such as machine learning and deep learning. Together with new technologies like AI assisted nanocarrier design and Digital Twin drug delivery models, these can provide the possibility to develop treatments more efficiently, with less animal and human testing, and a quicker path to clinical application. This review focuses on the recent advances in the applications of AI in ocular drug delivery, both from preclinical and clinical perspectives. It also considers current obstacles, such as data integration, transparency and reliability. These advances collectively herald a future of more effective, individualized, safe, and precise treatments in the eye.
Musarrat Husain Warsi (Thu,) studied this question.