To survey the performance of AI models designed to predict glaucoma progression, emphasizing their clinical relevance and assessing predictive accuracy, strengths, and limitations for implementation.
Key Findings:
AI models show promise in predicting glaucoma progression, with some approaching diagnostic task performance, such as [specific model or study].
Significant heterogeneity and inconsistency exist in current AI approaches for glaucoma progression, highlighting the need for standardization.
Key issues include interpreting AI outputs, integrating AI into clinical pathways, and establishing consistent reporting guidelines, which are critical for effective implementation.
Interpretation:
AI has the potential to enhance glaucoma management through improved prediction of disease progression, but challenges in implementation and standardization must be addressed to realize this potential.
Limitations:
High variability in study designs and outcomes, which may affect the generalizability of findings.
Lack of clinically applicable tools for personalized treatment based on AI predictions, necessitating further research.
Insufficient exploration of AI model output interpretation and integration into clinical workflows, which could hinder practical application.
Conclusion:
The review highlights the urgent need for rigorous validation and regulatory assessment of AI tools for glaucoma progression prediction, alongside actionable recommendations for future clinical implementation.
