To evaluate the applications of artificial intelligence in glaucoma diagnosis and surgical planning, highlighting its significance in improving patient outcomes.
Approach:
Key Findings:
AI models achieved high diagnostic accuracy with a pooled area under the curve of 0.93, indicating strong potential for clinical application. Multimodal models integrating various imaging techniques showed superior performance (AUC of 0.95) compared to single-modality systems, suggesting a need for integrated approaches in practice.
Interpretation:
AI has the potential to enhance decision-making and optimize surgical outcomes in glaucoma care, representing a significant shift towards data-driven, individualized treatment that could transform clinical practice.
Limitations:
Reliance on retrospective designs and heterogeneity in methodologies may affect the generalizability of findings. Limited external validation and variability in reporting could impact the robustness of conclusions.
Conclusion:
AI integration into clinical workflows is feasible and can significantly improve glaucoma management, although further validation is essential to ensure reliability and effectiveness.
