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Paper to Practice

重点拆解论文的方法部分:理解方法如何运作、有哪些局限,再考虑如何应用。

Paper to Practice ·

三种商用人工智能算法独立判读筛查乳腺 X 线片的外部评估

斯德哥尔摩一支独立团队在 8805 例筛查检查(739 例癌症、8066 名健康对照)上盲法运行了 3 款商用乳腺 X 线 AI 算法。表现最好的 AI-1 的 AUC 为 0.956;在与第一阅片医生相同的 96.6% 特异度下,其敏感度为 81.9%,第一阅片医生为 77.4%。AI-1 与第一阅片医生组合检出的癌症最多(敏感度 88.6%),但特异度降至 93.0%。

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

An independent team in Stockholm ran 3 commercial mammography AI algorithms blind on 8805 screening exams (739 cancers, 8066 healthy controls). The best one (AI-1) reached an AUC of 0.956 and, at the first readers' specificity of 96.6%, a sensitivity of 81.9% versus 77.4% for first-reader radiologists; pairing AI-1 with the first reader found the most cancers (88.6% sensitivity) but at a lower specificity of 93.0%.

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三种商用人工智能算法独立判读筛查乳腺 X 线片的外部评估External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

Paper to Practice ·

用生成式 Transformer 学习人类疾病的自然史

Delphi-2M 把 GPT 架构用到了健康记录上:它在 402,799 名 UK Biobank 参与者上训练,读取一个人的诊断史,预测 1,256 种疾病和死亡的发生率与发生时间,并能模拟未来的健康轨迹。内部验证的平均 AUC 为 0.76,不重新训练直接用于丹麦 193 万人的登记数据时为 0.67。

Learning the natural history of human disease with generative transformers

Delphi-2M adapts the GPT architecture to health records: trained on 402,799 UK Biobank participants, it reads a person's diagnosis history, predicts the rates and timing of 1,256 diseases plus death, and can simulate future health trajectories. It reached an average AUC of 0.76 internally and 0.67 on 1.93 million Danish registry records without retraining.

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用生成式 Transformer 学习人类疾病的自然史Learning the natural history of human disease with generative transformers
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