Clinical Decision Support Systems in Ophthalmology: A Systematic Search and a Narrative Review

Abstract

Purpose: Clinical decision support systems (CDSS) are advanced tools that enhance clinical decision-making by integrating patient data with evidence-based evidence. In ophthalmology, these systems can potentially improve diagnostic accuracy, optimize treatment plans, and streamline patient management. This narrative review aimed to explore the current landscape of CDSS in ophthalmology, evaluating its applications, benefits, and limitations. Methods: A systematic literature review was conducted, focusing on publications from the past decade that discuss the development, implementation, and efficacy of CDSS in ophthalmology. PubMed, Scopus and Web of Science databases were searched using relevant keywords. Articles were selected based on their relevance to clinical outcomes, technological innovation, and integration into ophthalmic practice. Results: The review identified various CDSS applications in ophthalmology, including tools for diagnosing retinal diseases, glaucoma management, and diabetic retinopathy screening. These systems leverage artificial intelligence (AI) and machine learning (ML) algorithms to provide real-time support to clinicians. Despite their potential, challenges such as data integration, user adoption, and regulatory approval remain significant barriers to widespread implementation. Conclusion: Clinical decision support systems in ophthalmology offer promising avenues for enhancing patient care and clinical efficiency. However, further research and development are necessary to address current limitations and ensure these systems are effectively integrated into routine ophthalmic practice.