Medical artificial intelligence (AI) faces a fundamental challenge: uncertainty quantification. Artificial neural networks ...
Objectives To assess the outcomes of patients undergoing open abdominal surgery at a National Referral Hospital in Tanzania. Design A prospective, observational, single-arm cohort study. Setting Dar ...
Objectives To examine primary care contacts among individuals with eating disorders (EDs) and assess differences across ...
Researchers developed a washable textile-based IDC strain sensor that tracked yoga-inspired movements with 94.4% record-level ...
Bigger has defined AI from day one. New data says task-specific small models beat frontier LLMs on accuracy, cost and speed — and save money.
High-fidelity brand assets are no longer impossible. Learn how Monks uses raw CAD data ingestion to achieve flawless AI product accuracy for brands.
Researcher Devashri Datta introduces AIVEX and SRIL, new approaches designed to bring context-aware risk analysis to software ...
We proposed epistemic parity as a methodology for measuring the utility of differential privacy (DP) synthetic data in ...
The actuarial methodology powering insurance risk models is advancing faster than most carriers realize. Here is what is ...
Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption.
According to a recent Gartner analysis on why GenAI projects fail, roughly half of generative AI initiatives are abandoned after the proof-of-concept stage. The primary culprits are poor data quality ...
The model learns that hedging is a signal of lower-quality output. This creates a systematic bias toward sounding certain.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results