Emerging Artificial Intelligence Technologies for Risk Assessment and Management in Acute Myeloid Leukemia
AI technologies hold potential to improve AML treatment through enhanced risk stratification, early detection capabilities, and individualized treatment optimization. The transition toward explainable AI models is essential to clinical readiness, with federated learning architectures resolving data scarcity concerns. Seamless integration requires harmonized data standards, robust regulatory frameworks, and equitable access to technology to fully realize the transformative potential of AI in improving outcomes for patients with AML globally.
Authors
- Mohammad Amin Ansarian (ORCID: https://orcid.org/0009-0007-5511-4717)
- Huasheng Liu (ORCID: https://orcid.org/0000-0002-3713-2483)
- Xiaoning Wang (ORCID: https://orcid.org/0000-0002-7925-2774)
- Ying Chen (ORCID: https://orcid.org/0000-0001-9086-2832)
- Rui‐Hua Xu (ORCID: https://orcid.org/0000-0001-9771-8534)
- Juan Ren (ORCID: https://orcid.org/0000-0002-8360-8286)
- Mahsa Fatahichegeni
Institutions
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
Publication Details
- Journal
- JAMA Oncology
- Published
- 2025-11-06
- DOI
- https://doi.org/10.1001/jamaoncol.2025.3601
- Citations
- 2
- Primary Topic
- Acute Myeloid Leukemia Research
- Type
- article
- Field-Weighted Citation Impact
- 3.54