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English(EN) One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail

AI在罕见病诊断方面存在困难，新论文揭示

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的研究论文探讨了使用AI进行罕见病诊断中的选择性预测所面临的挑战。研究发现，即使是先进的开放权重LLM在处理超罕见疾病时也存在困难，召回率较低。该论文还强调，仅依靠最高分数来做诊断决策是不可靠的，需要使用置信度信号（如前两名分数之差）的更细致的方法来提高准确性。研究表明，当前的方法不足以可靠地识别罕见病，需要进一步的模型开发。 AI

影响 强调了当前LLM在专业诊断任务中的局限性，表明需要更强大的模型来识别罕见病。

排序理由 arXiv上发表的研究论文，详细介绍了AI在罕见病诊断中的局限性。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI在罕见病诊断方面存在困难，新论文揭示

本文如何被排名

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Tool
arXiv上发表的研究论文，详细介绍了AI在罕见病诊断中的局限性。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
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Topics
paper, model release
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89 days old
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim, Zicheng Li, Xuanqi Peng, Fei Teng, Jiacong Mi, Honghan Wu ·

    一分两判：罕见病尾部选择性预测

    arXiv:2608.14683v1 Announce Type: new Abstract: Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review. This decision is usually made by thresholding the top score. Selective pre…

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