CLINICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN MODERN HEMATOLOGY: DIAGNOSTIC AND PROGNOSTIC PERSPECTIVES

Joanna Strzelczyk; Amelia Kędziora; Olga Chorąży; Aleksandra Stańczyk; Nicole Aleksandra Ordyczyńska-Mardyła; Zuzanna Michalska; Kinga Haduch; Anna Łęczycka; Izabela Zuzanna Stranz; Iga Suchta

doi:10.31435/ijitss.2(50).2026.5461

Authors

Joanna Strzelczyk Nicolaus Copernicus Provincial Multispecialty Center for Oncology and Traumatology in Łódź, Poland https://orcid.org/0009-0006-8212-9431
Amelia Kędziora University of Warmia and Mazury in Olsztyn, Poland https://orcid.org/0009-0001-8883-4370
Olga Chorąży Independent Public Healthcare Center Myślenice, Poland https://orcid.org/0009-0001-3094-2299
Aleksandra Stańczyk Clinical Hospital of the Ministry of Internal Affairs and Administration with the Warmia-Mazury Oncology Centre in Olsztyn, Poland https://orcid.org/0009-0000-2356-7841
Nicole Aleksandra Ordyczyńska-Mardyła District Hospital in Chrzanów, Poland https://orcid.org/0009-0007-1502-8291
Zuzanna Michalska Clinical Hospital of the Ministry of Internal Affairs and Administration with the Warmia Mazury Oncology Centre in Olsztyn, Poland https://orcid.org/0009-0005-8775-102X
Kinga Haduch University Hospital in Kraków, Poland https://orcid.org/0009-0004-4853-9395
Anna Łęczycka Independent Public Health Care Facility of the Ministry of Internal Affairs and Administration in Gdańsk, Poland https://orcid.org/0009-0004-9068-5222
Izabela Zuzanna Stranz Ludwik Rydygier Provincial Hospital in Toruń, Poland https://orcid.org/0009-0002-3689-0132
Iga Suchta District Hospital in Proszowice, Poland https://orcid.org/0009-0005-5837-5790

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.5461

Keywords:

Hematology, Artificial Intelligence, Blood Cell Analysis, Bone Marrow Analysis

Abstract

Artificial intelligence (AI) has recently emerged as an important tool supporting diagnostics and clinical decision-making across multiple fields of medicine, including hematology. Rapid advances in machine learning algorithms and computational data analysis have enabled the processing and interpretation of complex biomedical datasets derived from laboratory diagnostics, medical imaging, and molecular studies [1,2]. These developments are particularly relevant in hematology, where the interpretation of large volumes of morphological, immunophenotypic, and genetic data plays a crucial role in disease diagnosis and monitoring.

In hematological practice, artificial intelligence has demonstrated promising applications in the automated analysis of peripheral blood smears and bone marrow aspirates, as well as in the interpretation of flow cytometry data and molecular diagnostic results [3,4]. Several studies have shown that AI-based systems can achieve high levels of sensitivity and specificity in the detection of abnormal cellular populations, including leukemic blasts, potentially improving diagnostic accuracy and reducing the time required for laboratory evaluation [5].

Despite these promising developments, the clinical implementation of artificial intelligence in hematology remains associated with several challenges, including the need for large, high-quality training datasets, external validation across different patient populations, and integration with existing laboratory information systems [6].

The aim of this article is to review the current state of knowledge regarding the use of artificial intelligence in hematology, with particular emphasis on its applications in laboratory diagnostics, microscopic image analysis, and prognostic modeling in hematological diseases.

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