Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection Glaucoma, the leading cause of irreversible blindness worldwide, requires early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) is transforming glaucoma care by enhancing screening, diagnosis, monitoring, and treatment. This review summarizes current AI capabilities, future prospects, and practical considerations for integrating AI into glaucoma care. AI technologies analyze retinal images and machine learning models synthesize risk factors to identify high-risk patients. Deep learning techniques detect glaucomatous patterns from optical coherence tomography (OCT), visual field tests, and fundus photography. AI-powered platforms enable continuous monitoring, alerting physicians to rapid disease progression. AI can guide precision medicine by integrating predictive analytics with patient-specific parameters. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and training. AI chatbots and reminder systems can provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications are critical for successful implementation. AI's capabilities include transforming glaucoma care through enhanced screenings, improved diagnostic accuracy, personalized treatment strategies, and better patient outcomes. AI's role in glaucoma screening is significant, with deep learning models achieving equal or better accuracy than human experts. AI can detect glaucoma from fundus images, with models like Pegasus and Cybersight AI showing high accuracy. However, challenges remain in ensuring model accuracy across different cameras and datasets, and in addressing biases in training data. In glaucoma diagnosis, AI leverages OCT, visual fields, and hybrid models to differentiate glaucoma from other conditions. Deep learning models trained on OCT images achieve high accuracy in detecting glaucoma, with some models outperforming traditional methods. AI can also predict glaucoma progression using machine learning techniques, with studies showing promising results in forecasting disease development. AI's predictive power in glaucoma management includes forecasting disease progression and monitoring changes over time. Vision transformers, a type of AI model, have shown efficacy in analyzing fundus images and longitudinal OCT data. These models can detect subtle patterns indicative of disease progression, enhancing diagnostic accuracy. AI assistance in precision medicine and personalized treatment involves analyzing patient data to provide tailored treatment plans. AI can help identify high-risk patients and recommend appropriate interventions. However, challenges include ensuring data integrity, addressing algorithmic biases, and integrating AI into clinical workflows. AI's role in improving glaucoma outcomes includes enhancing surgical decision-making and intraoperative guidance. AI can analyze surgical recordings to improve techniques and minimize complications. AI systems can also assess surgeon performance, providing feedback for training and improving outcomes. In conclusion, AI is transforming glaucoma care by enhancing screening, diagnosis, monitoring, and treatment. While challenges remain, AI's potential to improve patient outcomes and expand access to care is significant. Continued research and development are essential to address these challenges andAdvancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection Glaucoma, the leading cause of irreversible blindness worldwide, requires early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) is transforming glaucoma care by enhancing screening, diagnosis, monitoring, and treatment. This review summarizes current AI capabilities, future prospects, and practical considerations for integrating AI into glaucoma care. AI technologies analyze retinal images and machine learning models synthesize risk factors to identify high-risk patients. Deep learning techniques detect glaucomatous patterns from optical coherence tomography (OCT), visual field tests, and fundus photography. AI-powered platforms enable continuous monitoring, alerting physicians to rapid disease progression. AI can guide precision medicine by integrating predictive analytics with patient-specific parameters. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and training. AI chatbots and reminder systems can provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications are critical for successful implementation. AI's capabilities include transforming glaucoma care through enhanced screenings, improved diagnostic accuracy, personalized treatment strategies, and better patient outcomes. AI's role in glaucoma screening is significant, with deep learning models achieving equal or better accuracy than human experts. AI can detect glaucoma from fundus images, with models like Pegasus and Cybersight AI showing high accuracy. However, challenges remain in ensuring model accuracy across different cameras and datasets, and in addressing biases in training data. In glaucoma diagnosis, AI leverages OCT, visual fields, and hybrid models to differentiate glaucoma from other conditions. Deep learning models trained on OCT images achieve high accuracy in detecting glaucoma, with some models outperforming traditional methods. AI can also predict glaucoma progression using machine learning techniques, with studies showing promising results in forecasting disease development. AI's predictive power in glaucoma management includes forecasting disease progression and monitoring changes over time. Vision transformers, a type of AI model, have shown efficacy in analyzing fundus images and longitudinal OCT data. These models can detect subtle patterns indicative of disease progression, enhancing diagnostic accuracy. AI assistance in precision medicine and personalized treatment involves analyzing patient data to provide tailored treatment plans. AI can help identify high-risk patients and recommend appropriate interventions. However, challenges include ensuring data integrity, addressing algorithmic biases, and integrating AI into clinical workflows. AI's role in improving glaucoma outcomes includes enhancing surgical decision-making and intraoperative guidance. AI can analyze surgical recordings to improve techniques and minimize complications. AI systems can also assess surgeon performance, providing feedback for training and improving outcomes. In conclusion, AI is transforming glaucoma care by enhancing screening, diagnosis, monitoring, and treatment. While challenges remain, AI's potential to improve patient outcomes and expand access to care is significant. Continued research and development are essential to address these challenges and