Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.

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Abstract

Multimodal natural language processing (NLP) represents a transformative approach in ophthalmology by bridging clinical text and medical imaging to enhance diagnostic accuracy, workflow efficiency, and patient-centered care. The mainstream NLP technologies were applied in ophthalmology, such as self-attention and cross-modality attention in multimodal settings, BERT and GPT in text-focused settings, and vision-focused transformers in imaging-focused settings. The applications of multimodal NLP in ophthalmology cover three key domains as follows: clinical text-image integration for comprehensive data analysis, enhanced screening and diagnostic prediction systems, and improved patient communication and management. With the significant advancements of multimodal NLP applications in ophthalmology, the consequent challenges must be addressed, including data privacy concerns, domain-specific terminology adaptation, model interpretability, standardization of multimodal data, and regulatory validation. Emerging directions (e.g., few-shot learning, real-time interactive systems, and privacy-preserving federated learning) offer potential solutions to current challenges. The successful implementation of multimodal NLP in ophthalmology requires collaborative efforts to develop standardized datasets, refine ethical guidelines, and validate clinical utility. By synthesizing textual and visual data, these technologies are poised to reshape ophthalmic practice, ultimately leading to more precise, accessible, and personalized eye care.

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