Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants.
Клиническое исследование II фазыСтатьяНазвание и аннотация: Перевод в очереди
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Chen Junren, Cao Yigeng, Feng Yahui, Qi Saibing, Yang Donglin, Hu Yu, Pang Aiming, Shen Qiujin, Luo Jieya, Gong Xiaowen, Zhang Rongli, Zhai Xiaolin, Li Xueqian, Yan Wen, Zhang Xianjing, Chen Mengyun, Niu Mingming, Wei Jialin, Liang Chen, Zhai Weihua, Zhao Ningning, Liu Xueou, Liu Sichang, Zhai Wangsong, Li Ruixin, Shao Xianfeng, Zhang Dong, Wang Mingyang, Pan Pan, Xu Mingyue, Zhang Wei, Xu Yunqiang, Zhu Xiaofan, Guo Ye, Wang Hong, Song Zhen, Gale Robert Peter, Han Mingzhe, Feng Sizhou, Jiang Erlie
Аннотация
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.