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Diagnostics
Special Issues
Innovative Research and Operational Advances in the Diagnosis of Hematologic...
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Innovative Research and Operational Advances in the Diagnosis of Hematologic Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1598

Special Issue Editor

Dr. Robert S. Ohgami

E-Mail Website
Guest Editor
Department of Pathology and ARUP Laboratories, University of Utah, Salt Lake City, UT 84112, USA
Interests: lymphoma; leukemia; pathology; flow cytometry; hematological malignancies; hematologic diseases; myelodysplastic neoplasms; clinical hematology; hematopathology; acute myeloid leukemia; translational research; novel technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative research and operational advances that improve the diagnosis of hematologic diseases and accelerate translation into routine clinical practice. Emphasis is placed on approaches that move beyond discovery to implementation, scalability, and real-world impact within clinical laboratories and healthcare systems.

Topics of interest include molecular and genomic diagnostics, artificial intelligence and digital pathology, biomarker validation, and updates to hematopathology classification systems, as well as operational innovations such as laboratory workflows, quality metrics, standardization, automation, and informatics-driven decision support. Submissions highlighting the clinical adoption of new technologies, integration into existing diagnostic pathways, and measurable improvements in diagnostic accuracy, efficiency, or patient care are particularly encouraged.

The goal of this Special Issue is to provide a practical and forward-looking perspective on how research advances and operational innovation together are reshaping modern hematologic diagnostics.

Dr. Robert S. Ohgami
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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

  • hematologic diseases
  • genomic diagnostics
  • digital pathology
  • biomarker
  • laboratory diagnostics

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Published Papers (1 paper)

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Review

12 pages, 543 KB  
Review
Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations
by Fnu Alnoor, Shuvam Mukherjee, Madhu P. Menon, David Ng, Peng Li and Robert S. Ohgami
Diagnostics 2026, 16(6), 913; https://doi.org/10.3390/diagnostics16060913 - 19 Mar 2026
Viewed by 1209
Abstract
Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and [...] Read more.
Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight. Full article
(This article belongs to the Special Issue Innovative Research and Operational Advances in the Diagnosis of Hematologic Diseases)
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