Introduction: Psoriasis is an immune-mediated, inflammatory and chronic disease, being considered systemic for the wide range of body systems it can affect. Ocular involvement is present in 10-12% of all psoriatic patients, but is often a neglected and underappreciated manifestation. Objective: This article aims to elucidate the main ocular manifestations related to psoriasis. Development: The research was based on a narrative bibliographic review of reports from the National Library of Medicine (NLM), the Scientific Electronic Library Online (Scielo), Google Scholars and Federal University of Bahia databases. As a result, the authors could describe ocular manifestations in psoriasis, related to specific eye sites: eyelids, conjunctiva, cornea, uvea, and lens- and their main symptoms. Uveitis, conjunctivitis, blepharitis, xerosi, and keratoconjunctivitis sicca can be highlighted as the most prevalent ocular symptoms. Final Considerations: Ocular manifestations in psoriatic patients are a prevalent site of involvement and may lead to ophthalmologic complications. To guarantee psoriatic patients a better prognosis and quality of life, it is of considerable importance to make a detailed investigation of ocular symptoms by both dermatologists and ophthalmologists in those patients.

Introduction: The concentrated evolution of various digital and telecommunication technologies in 2020 due to the emergence of a need to adapt to a new lifestyle, has raised opportunities for their use in medicine and in new models of care based on the growth of artificial intelligence (AI). AI seeks to mirror the human intellect through innovations capable of completing tasks, with Deep Learning, a technology that uses an artificial neural network, as a primordial subdivision. Progress in computational power, refinement of algorithms and learning architectures, the availability of unlimited data, and public access to deep neural networks, have made AI technology a promising reality in healthcare, specifically in the diagnosis and treatment of eye diseases. Objectives: To evaluate the benefits and importance of Artificial Intelligence in the diagnosis and treatment of ophthalmic diseases for the future. Methods: This is a bibliographic research of the narrative review type in which were used, mostly, scientific productions from the period 2018 to 2023 in the electronic databases: PubMed/MEDLINE and Scielo, containing the descriptors: artificial intelligence AND ophthalmology AND machine learning AND diagnosis. We found 21 from the search for the descriptors and 9 were elected who answered the guiding question; these, selected after the process of exclusion of works that associated other themes with the title, were written in a language other than English/Portuguese and paid articles. Results: The diagnosis for ophthalmic diseases is based on clinical evaluation and use of numerous equipment for image capture, being an expensive and time-consuming method. Research using AI would facilitate the process, taking Phelcom® equipment as an example. However, this study requires initial financial resources and a vast manpower, in addition to regulatory approval, the readjustment of algorithms to different data sets, and the black box problem. Conclusion: Artificial intelligence will provide benefits for the diagnosis and treatment of ophthalmic diseases by reducing the discordance and interobserver variability in the classification of diseases, such as glaucoma and diabetic retinopathy. However, it faces major structural challenges for its implementation.