Digital Technologies to Improve Diagnostic Cytopathology: Future Outlook

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 489

Special Issue Editor


E-Mail Website
Guest Editor
Facoltà di Medicina e Psicologia, Sede Ospedale S. Andrea Via di Grottarossa 1035, Università Sapienza, 00189 Roma, Italy
Interests: artificial intelligence; machine learning; neural network for cancer cells; diagnostic cytopathology

Special Issue Information

Dear Colleagues,

Advancements in technology and AI-driven tools are revolutionizing cytopathology. The ability to scan, store, view and share images, combined with machine learning applications, is expanding alongside the field itself, providing significant opportunities for professional growth. Digital technologies, such as virtual and augmented reality and natural language processing, could offer current and future generations new ways of working. A traditional microscope could be complemented by a 3D view of cytological samples, revealing critical morphological details; diagnostic uncertainties, such as in the decision to use a specific antibody in immunohistochemistry for differential diagnoses, could be overcome by virtual cytologists; similarly, the simultaneous visualization of the same cytological sample by other pathologists could facilitate collaborative case discussions. The integration of digital-driven diagnostic methodologies—including those involving AI—with traditional approaches will improve their effectiveness and patient care in the era of precision medicine. This Special Issue represents an opportunity for cytopathologists and those interested in the field to exchange ideas, laboratory experiences, and to potentially collaborate through articles and reviews on the possibility of developing new technologies that could improve cytopathological diagnostics.

Dr. Enrico Giarnieri
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • machine learning
  • cytopathology
  • digital cytology
  • computer-aided diagnosis
  • virtual reality
  • natural language process

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

28 pages, 878 KiB  
Review
AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies
by Daniele Giansanti, Andrea Lastrucci, Antonia Pirrera, Sandra Villani, Elisabetta Carico and Enrico Giarnieri
Bioengineering 2025, 12(7), 769; https://doi.org/10.3390/bioengineering12070769 - 16 Jul 2025
Viewed by 320
Abstract
Background: The integration of artificial intelligence (AI) into cervical cancer diagnostics has shown promising advancements in recent years. AI technologies, particularly in the analysis of cytological images, offer potential improvements in diagnostic accuracy and screening efficiency. However, challenges regarding model generalizability, explainability, and [...] Read more.
Background: The integration of artificial intelligence (AI) into cervical cancer diagnostics has shown promising advancements in recent years. AI technologies, particularly in the analysis of cytological images, offer potential improvements in diagnostic accuracy and screening efficiency. However, challenges regarding model generalizability, explainability, and operational integration into clinical workflows persist, impeding widespread adoption. Aim: This narrative review aims to critically evaluate the current state of AI in cervical cancer diagnostic cytology, identifying trends, key developments, and areas requiring further research. It also explores the potential for AI to improve diagnostic processes, alongside examining international guidelines and consensus on its adoption. Methods: A narrative review was conducted through a comprehensive search of PubMed and Scopus databases. Thirty studies published between 2020 and 2025 were selected based on their relevance. Results: The literature review reveals a growing interest in the application of AI for cervical cancer diagnostics, particularly in the automated interpretation. However, large-scale clinical adoption remains limited. Most studies are experimental or application-based in controlled settings. Consensus efforts and specific recommendations for this domain are still limited and not specific. Key barriers include limited model generalizability, lack of explainability, challenges in integration into clinical workflows, and regulatory and infrastructural constraints. Conclusions: A sustainable and meaningful integration of AI in cervical cancer diagnostics requires a unified framework that addresses both technical challenges and operational needs, supported by context-specific strategies and broader consensus-building efforts. Full article
Show Figures

Figure 1

Back to TopTop