Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review

ABSTRACT

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 the 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.

KEYWORDS:

• Myeloproliferative neoplasms
• myelofibrosis
• artificial intelligence
• machine learning
• deep learning

Disclaimer

As a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.

Article highlights

• The diagnosis and management of myeloproliferative neoplasm (MPN) patients requires a subjective analysis of clinical data.

• Artificial intelligence (AI) includes a flexible framework to assess clinical parameters, laboratory values, imaging, and text data to potentially improve care and outcomes of MPN patients.

• The potential use of AI to assist in MPN care can vary widely, but to date has focused primarily on disease classification, prediction of patient outcomes, and selection for novel therapeutic options for patients.

• There is ample opportunity for future investigations of AI in improving MPN patient care, due to a growing amount of data available to the medical and research community.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

A Srisuwananukorn was funded by the Conquer Cancer Research Foundation during the drafting of this paper.