Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review

被引:1
作者
Srisuwananukorn, Andrew [1 ]
Krull, Jordan E. [2 ,3 ]
Ma, Qin [2 ,3 ]
Zhang, Ping [2 ,4 ,5 ]
Pearson, Alexander T. [6 ]
Hoffman, Ronald [7 ]
机构
[1] Ohio State Univ, Comprehens Canc Ctr, Dept Internal Med, Div Hematol, Columbus, OH 43210 USA
[2] Ohio State Univ, Coll Med, Dept Biomed Informat, Columbus, OH 43210 USA
[3] Ohio State Univ, Pelotonia Inst Immuno Oncol, Comprehens Canc Ctr, Columbus, OH 43210 USA
[4] Ohio State Univ, Coll Engn, Dept Comp Sci & Engn, Columbus, OH 43210 USA
[5] Ohio State Univ, Translat Data Analyt Inst, Columbus, OH 43210 USA
[6] Univ Chicago, Dept Med, Sect Hematol Oncol, Chicago, IL USA
[7] Icahn Sch Med Mt Sinai, Tisch Canc Inst, Div Hematol & Med Oncol, New York, NY USA
关键词
Myeloproliferative neoplasms; myelofibrosis; artificial intelligence; machine learning; deep learning; INTERNATIONAL WORKING GROUP; ESSENTIAL THROMBOCYTHEMIA; PRIMARY MYELOFIBROSIS;
D O I
10.1080/17474086.2024.2389997
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
IntroductionArtificial intelligence (AI) is a rapidly growing field of computational research with the potential to extract nuanced biomarkers for the prediction of outcomes of interest. AI implementations for the prediction for clinical outcomes for myeloproliferative neoplasms (MPNs) are currently under investigation.Areas coveredIn this narrative review, we discuss AI investigations for the improvement of MPN clinical care utilizing either clinically available data or experimental laboratory findings. Abstracts and manuscripts were identified upon querying PubMed and the American Society of Hematology conference between 2000 and 2023. Overall, multidisciplinary researchers have developed AI methods in MPNs attempting to improve diagnostic accuracy, risk prediction, therapy selection, or pre-clinical investigations to identify candidate molecules as novel therapeutic agents.Expert opinionIt is our expert opinion that AI methods in MPN care and hematology will continue to grow with increasing clinical utility. We believe that AI models will assist healthcare workers as clinical decision support tools if appropriately developed with AI-specific regulatory guidelines. Though the reported findings in this review are early investigations for AI in MPNs, the collective work developed by the research community provides a promising framework for improving decision-making in the future of MPN clinical care.
引用
收藏
页码:669 / 677
页数:9
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