Introduces object-level semantic uncertainty for VLM memory, the UQ-DAAAM refinement system, and probabilistic guarantees that selected high-quality views reduce uncertainty more effectively.
arXiv preprint arXiv:2207.07411 , year=
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A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
Med-PaLM 2 achieves 86.5% accuracy on MedQA and approaches or exceeds prior state-of-the-art on other medical QA benchmarks while receiving higher physician preference ratings than human answers on consumer questions.
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
Pre-training on modality-matched data significantly improves downstream performance in medical imaging models while self-supervised learning benefits depend on context.
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