Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants
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.
Authors
- Mingzhe Han (ORCID: https://orcid.org/0000-0002-1720-6799)
- Xiaowen Gong (ORCID: https://orcid.org/0000-0001-8332-0402)
- Xueqian Li (ORCID: https://orcid.org/0000-0002-7412-5899)
- Sichang Liu
- Saibing Qi (ORCID: https://orcid.org/0000-0002-5306-5639)
- Junren Chen (ORCID: https://orcid.org/0000-0003-3691-4931)
- Xiaofan Zhu (ORCID: https://orcid.org/0000-0002-2572-6495)
- Qiujin Shen
- Erlie Jiang (ORCID: https://orcid.org/0000-0003-0237-6063)
- Mingming Niu (ORCID: https://orcid.org/0000-0002-4930-3745)
- Yiguo Hu (ORCID: https://orcid.org/0000-0002-3973-2140)
- Xueou Liu (ORCID: https://orcid.org/0000-0003-4974-2382)
- Rongli Zhang (ORCID: https://orcid.org/0009-0005-7444-4370)
- Jialin Wei (ORCID: https://orcid.org/0000-0002-0072-5461)
- Mengyun Chen
- Sizhou Feng (ORCID: https://orcid.org/0000-0002-8587-3446)
- Xianfeng Shao
- Xianjing Zhang
- Jieya Luo (ORCID: https://orcid.org/0000-0002-8761-7075)
- Donglin Yang (ORCID: https://orcid.org/0000-0002-8567-894X)
- Ye Guo (ORCID: https://orcid.org/0009-0003-8042-1840)
- Wanying Zhai (ORCID: https://orcid.org/0000-0002-5845-0018)
- Aiming Pang (ORCID: https://orcid.org/0000-0003-3706-3139)
- Mingyang Wang (ORCID: https://orcid.org/0000-0001-7201-5090)
- Hong Wang (ORCID: https://orcid.org/0000-0002-6215-6348)
- Wei Zhang (ORCID: https://orcid.org/0000-0001-7588-7249)
- Pan Pan (ORCID: https://orcid.org/0000-0002-7214-1393)
- Yan Wen (ORCID: https://orcid.org/0000-0002-4576-7470)
- Ningning Zhao (ORCID: https://orcid.org/0000-0003-3515-2445)
- Dong Zhang (ORCID: https://orcid.org/0000-0001-5551-9721)
- Ruixin Li (ORCID: https://orcid.org/0009-0009-0726-9882)
- Mingjun Xu (ORCID: https://orcid.org/0000-0001-5585-7712)
- Xiaolin Zhai (ORCID: https://orcid.org/0000-0001-6572-9941)
- Liang Chen (ORCID: https://orcid.org/0000-0003-4143-5724)
- Yahui Feng
- Robert Peter Gale
- Zhen Song (ORCID: https://orcid.org/0000-0002-3948-1695)
- Weihua Zhai
- Yigeng Cao (ORCID: https://orcid.org/0000-0002-9097-8103)
- Yu Xu (ORCID: https://orcid.org/0000-0002-7363-742X)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Tianjin Academy of Fine Arts (CN)
- Tianjin Anding Hospital (CN)
- Institute of Hematology & Blood Diseases Hospital (CN)
- Tianjin Metallurgical Vocational Technical College (CN)
- Imperial College London (GB)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1038/s41467-025-62926-0
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- National Institute for Health and Care Research
- National Natural Science Foundation of China
- Chinese Academy of Medical Sciences
- Natural Science Foundation of Tianjin City
- Tianjin Science and Technology Committee
- Fundamental Research Funds for the Central Universities