Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care
arXiv:2605.08445v1 Announce Type: new Abstract: AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical
arXiv:2604.27899v1 Announce Type: new Abstract: Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central
arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a
arXiv:2610.21449v1 Announce Type: new Abstract: Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for
arXiv:2609.02191v1 Announce Type: new Abstract: Human interventions at fault points can alter the diagnostic accuracy of multi-agent medical systems. We defined fault points as
A new study examines how large language models perform in a variety of medical contexts, including real emergency room cases — where at least one model seemed to be more accurate
arXiv:2603.18418v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for
NewsHive monitors these sources continuously. All signal titles above link to the original reporting.
Intelligence by NewsHive. Need help navigating what this means for your business? Contact GeekyBee →