Researchers introduced SkinMind, an agentic retrieval-augmented generation (RAG) system that adds human-inspired graph probing to improve reliability in dermatology AI. SkinMind targets a stated weakness in conventional RAG, which relies on one-shot retrieval from flat corpora and cannot adapt retrieval granularity across heterogeneous textual and visual sources. The work aims to give dermatology AI access to verifiable evidence spread across those sources.
Reliable dermatology AI requires access to verifiable evidence distributed across heterogeneous textual and visual sources. However, conventional retrieval-augmented generation (RAG) typically relies on one-shot retrieval from flat corpora, limiting its ability to adapt retrieval granularity, explor
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