Brief

Researchers have developed ProMUSE, a novel AI system designed to improve the early diagnosis of Alzheimer's disease by adaptively incorporating multi-modal data. This system initially uses low-cost clinical assessments and quantifies diagnostic uncertainty. If the uncertainty is high, ProMUSE progressively integrates more expensive data like MRI and PET scans, using Dempster-Shafer theory to fuse information and reduce reliance on costly imaging. Experiments on benchmark datasets show ProMUSE can achieve comparable accuracy to full-modality approaches while significantly reducing the need for MRI/PET scans, offering a more cost-effective solution for widespread screening. AI

Researchers have developed UMAR, a novel multi-agent framework designed to improve audit risk assessment by explicitly modeling uncertainty and evidence conflict. UMAR utilizes three specialized agents—MD&A Text Agent, Financial Ratio Agent, and CAM Agent—to generate independent risk scores with calibrated uncertainty. These scores are then aggregated using Dempster-Shafer theory, which quantifies inter-agent disagreement. Evaluations on 3,200 firm-year observations from SEC 10-K filings demonstrated UMAR's superior performance over baseline models, achieving an AUROC of 0.782 and a PR-AUC of 0.341, while also providing actionable insights through conflict patterns. AI