Healthcare-AI

Why this paper matters: Retrieval‑augmented generation (RAG) has been the default answer to “how do we make LLMs factual?” But clinical work is not a single hop to a single document; it’s a workflow—observe, hypothesize, retrieve, cross‑check, and only then decide. Deep‑DxSearch reframes RAG as a sequential policy, trained end‑to‑end with reinforcement learning (RL) so the model learns when to reason internally and when to consult guidelines, match similar patients, or search broader knowledge—before committing to a diagnosis. That design change is the story. ...

When Hidden Variables Become Hidden Costs In causal inference, confounders are the uninvited guests at your data party — variables that influence both treatment and outcome, quietly skewing results. In healthcare, failing to adjust for them can turn life-saving insights into misleading noise. Traditionally, finding these culprits has been the realm of domain experts, a slow and costly process that doesn’t scale well. The paper from National Sun Yat-Sen University proposes a radical alternative: put Large Language Model (LLM)-based agents into the causal inference loop. These agents don’t just crunch numbers — they reason, retrieve domain knowledge, and iteratively refine estimates, effectively acting as tireless, always-available junior experts. ...

Twin It to Win It: How BedreFlyt Reimagines Hospital Resource Planning Hospitals often operate under intense pressure, juggling patient needs, staff availability, and limited resources. Now imagine an AI-powered assistant that anticipates those needs, simulates complex patient flows, and delivers optimized resource plans—without burning out the staff. That’s the promise of BedreFlyt, a modular, simulation-driven Digital Twin (DT) designed for hospital wards. Developed at the University of Oslo, BedreFlyt isn’t just another simulation tool. It uniquely integrates: ...