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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Machine learning and artificial intelligence in haematology.

Roni Shouval^1,2, Joshua A Fein³, Bipin Savani⁴

¹Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

British Journal of Haematology

|July 1, 2020

Summary

This summary is machine-generated.

This review explains machine learning (ML) for hematology, covering basic concepts, applications, and study design guidelines. It aims to help clinicians interpret and evaluate ML research in hematology.

Keywords:

artificial intelligence haematology leukaemia machine learning prediction models

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Area of Science:

Medical Informatics
Artificial Intelligence
Genomic Medicine

Background:

Digitalization of medical records and genomic data integration generate vast healthcare information.
Machine learning (ML), a subset of artificial intelligence, extracts insights from complex datasets.
Increasing ML applications in hematology necessitate understanding its core principles for clinicians.

Purpose of the Study:

To equip clinicians and researchers with foundational knowledge of machine learning.
To enable critical appraisal and interpretation of machine learning literature in hematology.
To provide practical guidance on designing and evaluating ML studies within hematology.

Main Methods:

Review of fundamental machine learning terminology and concepts.
Exploration of diverse machine learning applications in hematology.
Presentation of guidelines for the design and evaluation of ML studies.

Main Results:

Clarification of essential ML concepts and terminology.
Illustrative examples of ML implementation in hematological contexts.
Framework for assessing the validity and applicability of ML research.

Conclusions:

Understanding ML is crucial for advancing hematological research and practice.
Standardized guidelines enhance the rigor of ML study design and interpretation.
Awareness of ML limitations is vital for responsible clinical integration.