Applied machine learning in intelligent systems: knowledge graph-enhanced ophthalmic contrastive learning with “clinical profile” prompts

Abstract

The results underscore the importance of explainable AI in medical diagnostics, particularly in ophthalmology, where model transparency is critical for clinical acceptance and utility. By incorporating domain-specific knowledge with advanced machine learning techniques, the proposed approach not only enhances model performance but also ensures that AI-generated insights are interpretable and reliable for clinical decision-making. These findings suggest that integrating structured medical knowledge with machine learning frameworks can address key challenges in AI-driven diagnostics, ultimately contributing to improved patient outcomes. Future research should explore the adaptability of this approach across various medical domains to further advance AI-assisted diagnostic systems.

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