arXiv:2411.08881v3 Announce Type: replace-cross
Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bi...
By Jos\'e Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi, Nannan Xi, Juho Hamari, Pekka Abrahamsson
AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.
By Paulius Mui, Dean F. Sittig, Steve Labkoff, Sanjay Basu
arXiv:2606. 26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment.
By Jakob Salfeld-Nebgen
arXiv:2606. 14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved.
By Muhammad Osama, Maheera Amjad, Zartasha Mustansar, Arslan Shaukat, Muhammad U. S. Khan
arXiv:2607. 17225v1 Announce Type: cross Abstract: Agentic AI systems do not just predict or recommend; they plan, maintain state, and act in external environments with varying degrees of autonomy.
By Chetan Arora, Andreas Vogelsang, Abbi Sharma
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc. , yet most deployments remain isolated point solutions locked inside departmental silos, resulting in duplicated effort, hidden risks, and unrealized enterprise value.