RESUMO
BACKGROUND: The significance of patient engagement (PE) is widely acknowledged as a crucial element in fostering positive health outcomes, elevating care quality, and streamlining healthcare systems. Despite its recognized advantages, the level of patient engagement in Arab nations remains suboptimal. METHODS: A high-level assembly was convened in Dubai with 11 distinguished patient advocates from diverse Arab countries. Their collective aim was to dissect the obstacles hindering patient engagement in the Arab world and propose pragmatic strategies to surmount them. First, a series of five open-ended, comprehensive questions were posed and thoroughly deliberated upon. Second, the barriers to patient engagement within the experts' respective communities were debated. A qualitative thematic analysis was conducted and two reports were generated by two independent researchers from the original meeting recordings. RESULTS: This paper highlights the importance of patient engagement in advancing healthcare and categorizes barriers to patient engagement as patient-related, provider-related, or system/government-related. The experts identified the primary gaps in patient engagement and proposed strategies to promote it, with a primary focus on motivating both patients and providers toward shared decision-making. CONCLUSIONS: This paper amalgamates the insights and recommendations distilled from the expert gathering, juxtaposing them within the broader context of existing literature on patient engagement. Offering a comprehensive viewpoint, this article delves into the challenges and opportunities intrinsic to bolstering patient engagement in the Arab world. Moreover, it spotlights invaluable tools often overlooked within Arab countries. The practical insights provided here can serve as a roadmap for administrators and decision-makers, providing guidance to enhance patient engagement on both a national and institutional scale.
RESUMO
Artificial intelligence (AI) has become a revolutionary influence in the field of ophthalmology, providing unparalleled capabilities in data analysis and pattern recognition. This narrative review delves into the crucial role that AI plays, particularly in the context of anterior segment diseases with a genetic basis. Corneal dystrophies (CDs) exhibit significant genetic diversity, manifested by irregular substance deposition in the cornea. AI-driven diagnostic tools exhibit promising accuracy in the identification and classification of corneal diseases. Importantly, chat generative pre-trained transformer (ChatGPT)-4.0 shows significant advancement over its predecessor, ChatGPT-3.5. In the realm of glaucoma, AI significantly contributes to precise diagnostics through inventive algorithms and machine learning models, surpassing conventional methods. The incorporation of AI in predicting glaucoma progression and its role in augmenting diagnostic efficiency is readily apparent. Additionally, AI-powered models prove beneficial for early identification and risk assessment in cases of congenital cataracts, characterized by diverse inheritance patterns. Machine learning models achieving exceptional discrimination in identifying congenital cataracts underscore AI's remarkable potential. The review concludes by emphasizing the promising implications of AI in managing anterior segment diseases, spanning from early detection to the tailoring of personalized treatment strategies. These advancements signal a paradigm shift in ophthalmic care, offering optimism for enhanced patient outcomes and more streamlined healthcare delivery.