IRIS: An Intelligent Vision-Language System for Ocular Surface Diseases via Topic Tree and Scene-Driven VQA Generation
arXiv:2607.04344v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular surface diseases (OSDs) is severely hindered by a paucity of high-fidelity, multimodal instruction-tuning data. To dismantle this data bottleneck, we introduce IRIS, an Intelligent Recognition and Interaction System tailored for fine-grained OSD understanding via external eye photography. First, we curate IRIS-120K, the largest and most comprehensive OSD visual question-answering (VQA) dataset to dat
The development of specialized data sets like IRIS-120K is critical for advancing Large Vision-Language Models (VLMs) by addressing the current bottleneck of high-fidelity, multimodal instructional data in niche domains.
This development indicates a crucial step towards making AI models, especially VLMs, clinically relevant and reliable for specialized medical diagnostics, moving beyond generic capabilities to practical application.
The primary change is the creation of a comprehensive, specialized dataset that allows VLMs to achieve fine-grained understanding and reasoning in a specific medical domain, thereby unlocking new diagnostic potential.
- · AI healthcare developers
- · Ophthalmology patients
- · Medical AI research institutions
- · General-purpose VLM developers (without specialized data)
- · Traditional diagnostic methods
Specialized AI vision systems will improve diagnostic accuracy and efficiency for ocular surface diseases.
The success of IRIS could catalyze similar specialized dataset creation and VLM development across other medical and clinical domains.
This could lead to widespread adoption of AI-powered diagnostic tools, potentially disrupting traditional medical training and practice, and reducing the reliance on human specialists in certain areas.
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Read at arXiv cs.AI