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Clinical and Laboratory Applications of Artificial Intelligence in Haematology
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Clinical and Laboratory Applications of Artificial Intelligence in Haematology

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Clinical Medicine".

Deadline for manuscript submissions: 30 March 2027 | Viewed by 1144

Editors

Dr. Emmanuel Favaloro

E-Mail Website1 Website2
Guest Editor
1. Haematology, Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR) and Research and Education Network (REN), Westmead Hospital, Westmead, NSW 2145, Australia
2. School of Dentistry and Medical Sciences, Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW 2650, Australia
3. School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia
Interests: hemostasis/haemostasis; thrombosis; von Willebrand factor/VWF; von Willebrand disease/VWD; coagulation; platelet function; thrombophilia; lupus anticoagulant; antiphospholipid antibodies
Special Issues, Collections and Topics in MDPI journals
Dr. Bingwen Eugene Fan

E-Mail Website
Guest Editor
1. Department of Haematology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore 308433, Singapore
2. Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore 768828, Singapore
3. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
4. Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block 1E Kent Ridge Road, Singapore 119228, Singapore
5. Department of Haematology, Northern Health, Epping, VIC 3076, Australia
Interests: medical innovation (medical devices, diagnostics, artificial intelligence); thrombosis and haemostasis; sustainability in healthcare; art and medicine

Special Issue Information

Dear Colleagues,

The field of haematology is ever advancing. Although not unique to haematology, artifical inteligence (AI) and machine learning (ML) are strongly influencing contemporay medical and laboratory advances. These advances are fundamentally transforming the field of haematology. Associated technologies offer powerful tools to enhance diagnostic precision, refine prognostic assessments and personalize treatment strategies. This Special Issue is dedicated to exploring the forefront of these applications. 

Areas of interest include AI-driven laboratory automation for workflow enhancement, prognostic and predictive modelling for risk stratification and treatment response, and clinical decision support systems for therapeutic guidance in areas such as haemostasis and thrombosis and haematological malignancy. Further topics include AI for diagnostic image analysis of peripheral blood smears and bone marrow biopsies, interpretation of complex genomic and flow cytometry data, and the detection of rare cell populations like minimal residual disease. We also encourage submissions on big data analytics using electronic health records, federated learning models, and critical discussions on the practical challenges of implementing AI in clinical hematology and laboratory settings. We invite original research, reviews, and perspectives from clinicians, laboratory professionals, and data scientists. The goal is to foster interdisciplinary dialogue and showcase advancements that promote the responsible integration of AI into hematology, ultimately improving patient outcomes and healthcare delivery.

Dr. Emmanuel Favaloro
Dr. Bingwen Eugene Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hematology
  • artificial intelligence/AI
  • machine learning/ML
  • innovation
  • hemostasis
  • thrombosis
  • diagnostics
  • prog-nosis
  • laboratory automation
  • clinical decision support
  • precision medicine
  • big data
  • digital pathology

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Published Papers (1 paper)

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Research

12 pages, 543 KB  
Article
Predicting Iron Deficiencies Using Routine Complete Blood Cell Count Parameters: A Machine Learning Approach and Evaluation
by Davide Negrini, Laura Pighi, Simone Mignolli, Gian Luca Salvagno and Giuseppe Lippi
J. Clin. Med. 2026, 15(12), 4783; https://doi.org/10.3390/jcm15124783 - 19 Jun 2026
Viewed by 600
Abstract
Background/Objectives: Iron deficiency remains a prevalent condition, needing specific laboratory tests for diagnosis. This study aimed to evaluate whether routine complete blood cell count (CBC) parameters can be used within a machine learning framework to predict low ferritin and low transferrin saturation, used [...] Read more.
Background/Objectives: Iron deficiency remains a prevalent condition, needing specific laboratory tests for diagnosis. This study aimed to evaluate whether routine complete blood cell count (CBC) parameters can be used within a machine learning framework to predict low ferritin and low transferrin saturation, used as biochemical markers of altered iron status, potentially supporting more targeted laboratory test utilization. Methods: In this single-center retrospective outpatient study, we analyzed 32,437 records from subjects undergoing both complete blood cell count and iron metabolism testing between 2023 and 2026. Low ferritin and low transferrin saturation were defined using sex-specific thresholds. Low ferritin was present in 14,344 subjects (44.2%), whereas low transferrin saturation was present in 7791 subjects (24.0%). After cleaning data and excluding incomplete records, demographic variables and CBC indices were tested as potential predictors. The dataset was split into training and test sets with stratified sampling. Multiple supervised machine learning models, including logistic regression, decision tree, random forest, XGBoost, support vector machine, k-nearest neighbors, and Naive Bayes, were trained. Hyperparameter tuning and model selection were performed using repeated stratified 10-fold cross-validation, optimizing the area under the curve (AUC). Model performance was assessed by AUC, sensitivity, and specificity, and validated on an independent test set. Results: All models showed predictive capability for low ferritin and low transferrin saturation using CBC parameters alone. Ensemble methods, especially random forest and XGBoost, reached the best performance (AUC values of 0.80–0.87 for ferritin and 0.85–0.96 for transferrin saturation). Sensitivity and specificity were balanced, supporting clinical screening applicability. Results were maintained across validation and confirmed in the test set. Prediction of transferrin saturation showed slightly higher accuracy than ferritin. Feature importance analysis identified mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and red blood cell distribution width (RDW) as key predictors. Conclusions: CBC-based machine learning models may help identify subjects with low ferritin or low transferrin saturation, supporting subsequent targeted assessment of iron status. Full article
(This article belongs to the Special Issue Clinical and Laboratory Applications of Artificial Intelligence in Haematology)
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