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

Introduction: The integration of artificial intelligence (AI) into ophthalmic diagnostics has the potential to significantly enhance diagnostic accuracy and interpretability, thereby supporting clinical decision-making. However, a major challenge in AI-driven medical applications is the lack of transparency, which limits clinicians' trust in automated recommendations. This study investigates the application of machine learning techniques by integrating knowledge graphs with contrastive learning and utilizing "clinical profile" prompts to refine the performance of the ophthalmology-specific large language model, MeEYE, which is built on the CHATGLM3-6B architecture. This approach aims to improve the model's ability to capture clinically relevant features while enhancing both the accuracy and explainability of diagnostic predictions. Methods: This study employs a novel methodological framework that incorporates domain-specific knowledge through knowledge graphs and enhances feature representation using contrastive learning. The MeEYE model is fine-tuned with structured clinical knowledge, enabling it to better distinguish subtle yet significant ophthalmic features. Additionally, "clinical profile" prompts are incorporated to further improve contextual understanding and diagnostic precision. The proposed method is evaluated through comprehensive performance benchmarking, including quantitative assessments and clinical case studies, to ensure its efficacy in real-world ophthalmic diagnosis. Results: The experimental findings demonstrate that integrating knowledge graphs and contrastive learning into the MeEYE model significantly improves both diagnostic accuracy and model interpretability. Comparative analyses against baseline models reveal that the proposed approach enhances the identification of ophthalmic conditions with higher precision and clarity. Furthermore, the model's ability to generate transparent and clinically relevant AI recommendations is substantiated through rigorous evaluation, highlighting its potential for real-world clinical implementation. Discussion: 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.