Commonly measured implementation outcomes were acceptability, feasibility, and adoption of the digital innovation. Of 252 studies, 247 studies (98%) did not measure service outcomes, while patient ...

This practice had to change when the European Union introduced Right to be Forgotten (RTBF)—first in 2014, as a standalone ...

AI-enabled research tools can accelerate health research, but their data-science roots may clash with epidemiological ...

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...

Cycle detection in directed graphs, topological sort, Kahn’s algorithm. These are the ones that feel simple until you’re implementing them and something quietly goes wrong. Same idea as BFS: try to ...

Abstract: Robots are becoming essential in human environments, requiring them to behave in a socially compliant manner. Although previous learning-based methods have shown potential in social ...

aCentre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia bObservatory on the Future of Healthcare, ...

In 2026, Azure Machine Learning has evolved from a sandbox for data scientists into a robust platform for operational forecasting, yet many teams still struggle to see what happens after deployment.

😭 GraphRAG is good and powerful, but the official implementation is difficult/painful to read or hack. 😊 This project provides a smaller, faster, cleaner GraphRAG, while remaining the core ...

Chances are you know the climate is changing and that means trouble. But what exactly is driving the climate crisis, how bad are things now and how much worse could they get? To answer those questions ...

Experimental - This project is still in development, and not ready for the prime time. A minimal, secure Python interpreter written in Rust for use by AI. Monty avoids the cost, latency, complexity ...