arXiv:2606.18063v1 Announce Type: cross Abstract: Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity. This challenge is particularly pronounced in pathological scar classification, where differentiating keloids from hypertrophic scars requires subtle expert knowledge and labeled images are extremely limited. We propose a novel paradigm that repositions large language models (LLMs) as know
The increasing sophistication of LLMs and the persistent challenge of data scarcity in specialized medical imaging are converging, making this a timely innovation.
This development represents a significant step towards enabling advanced AI diagnostics in fields previously hindered by limited annotated data, democratizing access to expert knowledge.
LLMs can now be leveraged to extract clinically meaningful features from medical images, bypassing the need for extensive, costly human annotation in specific domains like scar classification.
- · Medical diagnostic AI companies
- · Healthcare providers in specialized fields
- · Patients with rare conditions
- · LLM developers
- · Traditional medical image annotation services
- · Deep learning models requiring vast datasets
Pathological scar classification becomes more accessible and accurate, leading to better patient outcomes.
This paradigm extends to other medical imaging domains facing data scarcity, accelerating AI adoption in diverse diagnostic areas.
The role of human experts shifts from primary annotation to validation and refinement of LLM-generated insights, enhancing their leverage.
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Read at arXiv cs.AI