Evidence map›Paper›PMID 39564069›Full record

ReviewCureus2024

Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology.

Ghizal Fatima, Hekmat Alhmadi, Abbas Ali Mahdi, Najah Hadi, Jan Fedacko, Aminat Magomedova, Sidrah Parvez, Ammar Mehdi Raza

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Digital Pathology in Dermatology - Current Status, Applications and Perspectives.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026
    Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Intelligent Stain-Free Histology on Structural Colorimetric Nanocavities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ghizal FatimaBiotechnology, Eras Lucknow Medical College and Hospital, Lucknow, IND.
Hekmat AlhmadiChemistry, Al-Muthanna University, Samawah, IRQ.
Abbas Ali MahdiBiotechnology, Era University, Lucknow, IND.
Najah HadiMedicine, Kufa University, Najaf, IRQ.
Jan FedackoCardiology, Pavol Jozef Šafárik University, Kosice, SVK.
Aminat MagomedovaBiostatistics, Lomonosov Moscow State University, Moscow, RUS.
Sidrah ParvezBiotechnology, Eras Lucknow Medical College and Hospital, Lucknow, IND.
Ammar Mehdi RazaPediatric Dentistry, Career Dental College and Hospital, Lucknow, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital pathology has emerged as a revolutionary field, transforming traditional diagnostic practices by integrating advanced imaging technologies, computational tools, and artificial intelligence (AI). Adopting digital slides over conventional glass slides enables high-resolution imaging, facilitating remote consultations, second opinions, and telepathology. The digitalization of pathology laboratories enhances workflow efficiency and allows for large-scale data storage, retrieval, and analysis, paving the way for developing robust diagnostic algorithms. One of the most transformative aspects of digital pathology is its synergy with AI and machine learning (ML). These technologies have enabled the automation of repetitive processes, including diseased feature detection, biomarker quantification, and tissue segmentation. This has decreased inter-observer variability and increased diagnostic accuracy. AI-driven algorithms are particularly beneficial in complex cases, assisting pathologists in detecting subtle patterns that might be missed through manual examination. Furthermore, digital pathology plays a critical role in personalized medicine by enabling the precise characterization of tumors, which leads to targeted therapy decisions. Integrating digital pathology with genomics and other omics data holds promise for a more holistic understanding of diseases, driving innovation in diagnostics and treatment. However, the transition to digital pathology is challenging. Issues such as data standardization, regulatory compliance, and the need for robust IT infrastructure must be addressed to realize its full potential. This review provides a detailed examination of these advances, their clinical applications, and the challenges faced in the widespread adoption of digital pathology. As the field continues to evolve, it is poised to play a pivotal role in shaping the future of diagnostics, offering new possibilities for improving patient outcomes. This comprehensive review explores the significant advances in digital pathology, highlighting its impact on diagnostics, research, and patient care.

Indexed as

artificial intelligencediagnostic accuracydigital pathologypersonalized medicinetelepathologywhole slide imaging

Identifiers

PMID39564069
PMCPMC11573928

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.