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Updated: Jun 17, 2025

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
Published on: July 15, 2015
Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review
Andrew Srisuwananukorn1, Jordan E Krull2,3, Qin Ma2,3
1Division of Hematology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
Introduction:
Artificial 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 Covered:
In 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 Opinion:
It 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.
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