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<p>Autonomous artificial intelligence (AI) models for deciding treatment strategies are available but rarely applied prospectively in clinical settings. Here we present a prospective study of deploying daGOAT, an algorithm we have developed, as a conditional autonomous AI agent to prescribe a drug to prevent severe (grade&#xa0;3−4) acute graft-versus-host disease (acute GvHD) following human leukocyte antigen (HLA)-mismatched haematopoietic cell transplantation (ClinicalTrials.gov, NCT05600855). During the enrollment period physicians invite 85% of eligible patients to participate and 88% of the invited patients agree. Among the 110 enrolled participants who receive HLA-haploidentical transplants, daGOAT predicts intermediate to high risk of severe acute GvHD in 57 participants between days +17 and +23 posttransplant and prescribes ruxolitinib in addition to the existing regimen to intensify immune suppression. The initial compliance with AI prescription is 98% (56/57), with dose and/or schedule deviating from the AI prescription within one month in a total of eight participants. In conclusion, we show that many physicians and patients are receptive to using conditional autonomous AI to prescribe a drug and that the decision for pharmaceutical intervention could be facilitated by autonomous AI.</p>

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Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Junren Chen,
  • Yigeng Cao,
  • Yahui Feng,
  • Saibing Qi,
  • Donglin Yang,
  • Yu Hu,
  • Aiming Pang,
  • Qiujin Shen,
  • Jieya Luo,
  • Xiaowen Gong,
  • Rongli Zhang,
  • Xiaolin Zhai,
  • Xueqian Li,
  • Wen Yan,
  • Xianjing Zhang,
  • Mengyun Chen,
  • Mingming Niu,
  • Jialin Wei,
  • Chen Liang,
  • Weihua Zhai,
  • Ningning Zhao,
  • Xueou Liu,
  • Sichang Liu,
  • Wangsong Zhai,
  • Ruixin Li,
  • Xianfeng Shao,
  • Dong Zhang,
  • Mingyang Wang,
  • Pan Pan,
  • Mingyue Xu,
  • Wei Zhang,
  • Yunqiang Xu,
  • Xiaofan Zhu,
  • Ye Guo,
  • Hong Wang,
  • Zhen Song,
  • Robert Peter Gale,
  • Mingzhe Han,
  • Sizhou Feng,
  • Erlie Jiang

摘要

Autonomous artificial intelligence (AI) models for deciding treatment strategies are available but rarely applied prospectively in clinical settings. Here we present a prospective study of deploying daGOAT, an algorithm we have developed, as a conditional autonomous AI agent to prescribe a drug to prevent severe (grade 3−4) acute graft-versus-host disease (acute GvHD) following human leukocyte antigen (HLA)-mismatched haematopoietic cell transplantation (ClinicalTrials.gov, NCT05600855). During the enrollment period physicians invite 85% of eligible patients to participate and 88% of the invited patients agree. Among the 110 enrolled participants who receive HLA-haploidentical transplants, daGOAT predicts intermediate to high risk of severe acute GvHD in 57 participants between days +17 and +23 posttransplant and prescribes ruxolitinib in addition to the existing regimen to intensify immune suppression. The initial compliance with AI prescription is 98% (56/57), with dose and/or schedule deviating from the AI prescription within one month in a total of eight participants. In conclusion, we show that many physicians and patients are receptive to using conditional autonomous AI to prescribe a drug and that the decision for pharmaceutical intervention could be facilitated by autonomous AI.