SHIELD is a new diverse clinical note dataset paired with distilled small language models that achieve 0.89 span-level precision and 0.88 recall for on-premise PHI de-identification.
DeID - GPT : Zero -shot Medical Text De - Identification by GPT -4, December 2023
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VisShield with OPTIC dataset enables VLMs to localize and mask private text in vision data via instruction tuning for privacy preservation.
Headache specialists preferred their own literature summaries over those from Sonnet, GPT-4o, and Llama 3.1 in a blinded evaluation, though AI summaries were sometimes indistinguishable.
Neuro-symbolic framework maps LLM outputs from clinical narratives into fuzzy logic for explainable and verifiable disease diagnosis.
LLaMA-XR fine-tunes LLaMA 3.1 with QLoRA on DenseNet-121 embeddings to generate radiology reports from chest X-rays, reporting ROUGE-L of 0.433 and METEOR of 0.336 on the IU X-ray benchmark.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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