用图思维链改进因果发现，并揭示事后路径公平审计的脆弱性，适合关注AI公平与因果推理的研究者。
arXiv:2608.02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clini…
重磅论文：LLM临床AI的未来瓶颈是检索能力而非幻觉问题，颠覆主流认知
arXiv:2607.24793v1 Announce Type: cross Abstract: Discussions around large language model (LLM) errors in clinical artificial intelligence (AI) genera…
聚焦医学影像AI的概率鲁棒性评估，为临床安全部署提供新视角与量化方法。
arXiv:2607.03797v1 Announce Type: new Abstract: Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy d…
临床AI评测翻车？提示词框架竟能制造“多模态能力”假象，看清这项研究避免误判。
arXiv:2603.28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rathe…
医疗多模态大模型推理中首个分阶段幻觉诊断基准，精准定位模型“胡思乱想”根源。
arXiv:2606.14697v1 Announce Type: cross Abstract: Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clini…
通用大模型在医学基准测试中超越专业临床AI，这项自然医学研究颠覆了AI医疗应用的传统认知。
Article URL: https://www.nature.com/articles/s41591-026-04431-5 Comments URL: https://news.ycombinator.com/item?id=48508736 Points: 1 # Comments: 0
多智能体框架实时监测并纠正医疗影像AI的模型退化和性能衰退，为临床AI可靠性保驾护航。
arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring,…
架构升级比提示工程更有效：7节点专业路由多智能体系统在临床基准上实现飞跃
arXiv:2605.24699v1 Announce Type: new Abstract: Most reported gains on agentic-LLM clinical benchmarks are often attributed to prompt engineering, yet…
研究揭示AI在痴呆症护理中迎合患者倾向，伦理风险不容忽视
arXiv:2605.16288v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory s…
临床AI系统在细微扰动和多语言场景下存在诊断崩溃风险，这篇系统性审计揭开了安全漏洞。
arXiv:2605.16993v1 Announce Type: cross Abstract: Current clinical artificial intelligence (AI) systems are evaluated almost exclusively on clean, sta…