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Diagnostics
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Latest News in Digital Pathology
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Latest News in Digital Pathology

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 5898

Special Issue Editor

Dr. Tahir Mahmood

E-Mail Website
Guest Editor
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Interests: digital pathology and AI integration; cancer diagnosis and prognosis; histopathology and tissue analysis

Special Issue Information

Dear Colleagues,

The field of digital pathology is rapidly evolving with the introduction of advanced technologies and novel imaging modalities. These innovations are significantly enhancing the accuracy and efficiency of cancer diagnosis and prognosis. This Special Issue, "Recent News in Digital Pathology," aims to explore the latest developments in this field, including the application of generative AI, advancements in cancer diagnosis and prognosis, and novel methods for cancer grading. By bringing together cutting-edge research and expert insights, this issue will provide a comprehensive overview of the current trends and future directions in digital pathology.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Digital pathology;
  2. Generative AI;
  3. Cancer diagnosis;
  4. Cancer prognosis;
  5. Imaging modalities;
  6. Cancer grading;
  7. Artificial intelligence in pathology;
  8. Histopathology;
  9. Image analysis;
  10. Medical imaging.

Dr. Tahir Mahmood
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • digital pathology
  • generative AI
  • cancer diagnosis
  • cancer prognosis
  • imaging modalities
  • cancer grading
  • artificial intelligence in pathology
  • histopathology
  • image analysis
  • medical imaging
  • vision-language models in histopathology
  • large language models for pathology diagnostics
  • multimodal AI integration in digital pathology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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12 pages, 1679 KB  
Article
From Microscopes to Monitors: Unique Opportunities and Challenges in Digital Pathology Implementation in Remote Canadian Regions
by Miquela Daniel, Klaudia Nowak, Rajkumar Vajpeyi, Blaise Clarke, Andrew Evans, Charlotte Carment-Baker, Karen Weiser, Mary Martin, Nancy Girard, Kate Fyfe, Shaza Zeidan, Christine Bruce and George M. Yousef
Diagnostics 2025, 15(16), 1983; https://doi.org/10.3390/diagnostics15161983 - 8 Aug 2025
Viewed by 441
Abstract
Background/Objectives: Digital pathology has the potential to revolutionize pathology diagnostics, especially in geo-graphically isolated and underserved regions. By leveraging technology, telepathology, and integration with computer-aided diagnostic tools, digital pathology can improve access to prompt and accurate diagnostics. Methods: Our key steps to implementing [...] Read more.
Background/Objectives: Digital pathology has the potential to revolutionize pathology diagnostics, especially in geo-graphically isolated and underserved regions. By leveraging technology, telepathology, and integration with computer-aided diagnostic tools, digital pathology can improve access to prompt and accurate diagnostics. Methods: Our key steps to implementing digital pathology and transitioning operations to a digital network are assessing existing infrastructure, identifying gaps in connectivity and resources, and creating a workflow tailored to the needs of the healthcare system. Results: We present an approach of implementing digital pathology in Timmins, Northern Ontario, Canada, focusing on addressing regional disparities and the improvements that come alongside utilizing digital pathology. Our results show that digital pathology can provide prompt, efficient and better-quality diagnostic services to rural and un-deserved areas, improving patient care and outcomes. It also represents a cost-effective option with savings from eliminating travel costs, courier costs and additional operational efficiencies. Conclusions: Implementing digital pathology in rural settings presented with challenges related to infrastructure, technical abilities, workforce readiness, cost and other aspects involved in transitioning from traditional microscopy to a fully digital pathway. Digital pathology systems can help ensuring seamless data flow and improving overall healthcare delivery. Telepathology also allows pathologists to provide diagnostic services from a distance, which is particularly beneficial in areas with a shortage of pathologists. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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11 pages, 2813 KB  
Article
Perspectives on Reducing Barriers to the Adoption of Digital and Computational Pathology Technology by Clinical Labs
by Jeffrey L. Bessen, Melissa Alexander, Olivia Foroughi, Roderick Brathwaite, Emre Baser, Liam C. Lee, Omar Perez and Gary Gustavsen
Diagnostics 2025, 15(7), 794; https://doi.org/10.3390/diagnostics15070794 - 21 Mar 2025
Cited by 1 | Viewed by 2005
Abstract
Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and [...] Read more.
Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and barriers to widespread adoption, we conducted a survey among 63 anatomic pathologists and lab directors within the US health system. Results: The survey results indicated that current use cases for DP/CP involve streamlining traditional manual pathology and that labs would have substantial difficulty providing AI-guided image analysis if it were required by physicians today. Among potential catalysts for the broader adoption of DP/CP, pathologists identified clinical guidelines as a key resource for anatomic pathology, whose endorsement of DP/CP would be highly impactful for reducing current barriers. Conclusions: Expanded access to DP/CP may ultimately benefit all major stakeholders—patients, physicians, clinical laboratory professionals, care settings, and payers—and will therefore require collaboration across these groups. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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16 pages, 2500 KB  
Article
Computer-Aided Diagnosis in Spontaneous Abortion: A Histopathology Dataset and Benchmark for Products of Conception
by Tahir Mahmood, Zeeshan Ullah, Atif Latif, Binish Arif Sultan, Muhammad Zubair, Zahid Ullah, AbuZar Ansari, Talat Zehra, Shahzad Ahmed and Naqqash Dilshad
Diagnostics 2024, 14(24), 2877; https://doi.org/10.3390/diagnostics14242877 - 21 Dec 2024
Cited by 4 | Viewed by 1378
Abstract
Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective [...] Read more.
Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10× through a camera-connected microscope by a consultant histopathologist. Results: The dataset’s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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Review

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18 pages, 2661 KB  
Review
Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review
by Iuliu Gabriel Cocuz, Raluca Niculescu, Maria-Cătălina Popelea, Maria Elena Cocuz, Adrian-Horațiu Sabău, Andreea-Cătălina Tinca, Andreea Raluca Cozac-Szoke, Diana Maria Chiorean, Corina Eugenia Budin and Ovidiu Simion Cotoi
Diagnostics 2025, 15(17), 2196; https://doi.org/10.3390/diagnostics15172196 - 29 Aug 2025
Viewed by 163
Abstract
Background: Digital Pathology (DP) and Artificial Intelligence (AI) have strongly developed in recent years, especially in pathology, with a high interest in dermatopathology. Accelerated by the COVID-19 pandemic, DP and AI are now integrated in pathology, research and education, bringing value to histopathological [...] Read more.
Background: Digital Pathology (DP) and Artificial Intelligence (AI) have strongly developed in recent years, especially in pathology, with a high interest in dermatopathology. Accelerated by the COVID-19 pandemic, DP and AI are now integrated in pathology, research and education, bringing value to histopathological diagnoses, telepathology and personalized medicine. This narrative review presents a comprehensive literature review by defining three research directions, using scientometric analysis, of the current state of DP and AI in pathology and dermatopathology. Methods: The research was conducted through the Pubmed and Web of Science databases, within the research period of January 2019–July 2025: a two-phase methodology. Four independent pathologists selected the articles in accordance with the inclusion and exclusion criteria, and the synthesis of the articles was based on three research directions. Results: The research shows that CNN (Convolutional Neural Network), AI powered diagnostic platforms and telepathology strongly contribute to increasing the speed and accuracy of diagnostics, especially on cutaneous malignant skin tumors. There are still several challenges and limitations in terms of validation, interoperability, initial high implementation costs, ethics and transparency in AI and equity in healthcare. Conclusions: DP and AI are essential pillars of modern dermatopathology, with a high necessity of standardization, regulation and a multidisciplinary approach. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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14 pages, 3170 KB  
Review
Simplified Artificial Intelligence Terminology for Pathologists
by Fatemeh Zabihollahy, Michael Mankaruos, Maxim Mohareb, Timothy Youssef, Yasaman Soleymani and George M. Yousef
Diagnostics 2025, 15(13), 1699; https://doi.org/10.3390/diagnostics15131699 - 3 Jul 2025
Viewed by 510
Abstract
The expanding shift towards digital pathology in clinical practice globally highlights its potential to enhance patient care through artificial intelligence (AI)-powered, computer-assisted diagnostics. Effective communication between AI scientists and pathologists is crucial for this transformation, though their differing technical languages can pose challenges. [...] Read more.
The expanding shift towards digital pathology in clinical practice globally highlights its potential to enhance patient care through artificial intelligence (AI)-powered, computer-assisted diagnostics. Effective communication between AI scientists and pathologists is crucial for this transformation, though their differing technical languages can pose challenges. The manuscript aims to offer simplified explanations of common AI terminology, along with practical examples and illustrations, to help pathologists better grasp AI concepts. This review is divided into the following sections: AI technologies and algorithms in computational pathology; frameworks for training AI models; nomenclature of image analysis; and public datasets for computational pathology research. These sections collectively provide a comprehensive understanding of the current landscape and resources in computational pathology. The manuscript fosters better communication between these fields and showcases the advantages of AI technologies in pathology. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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