AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies
<b>Background:</b> 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 eff...
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MDPI AG
2025-07-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/7/769 |
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| author | Daniele Giansanti Andrea Lastrucci Antonia Pirrera Sandra Villani Elisabetta Carico Enrico Giarnieri |
| author_facet | Daniele Giansanti Andrea Lastrucci Antonia Pirrera Sandra Villani Elisabetta Carico Enrico Giarnieri |
| author_sort | Daniele Giansanti |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Aim:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-da3dc4f7d70c4497abf267f5b4ee624f |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-da3dc4f7d70c4497abf267f5b4ee624f2025-08-20T03:58:31ZengMDPI AGBioengineering2306-53542025-07-0112776910.3390/bioengineering12070769AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge StudiesDaniele Giansanti0Andrea Lastrucci1Antonia Pirrera2Sandra Villani3Elisabetta Carico4Enrico Giarnieri5Centro TISP, Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Rome, ItalyDepartment of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, ItalyCentro TISP, Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Rome, ItalyDepartment of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyDepartment of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, ItalyDepartment of Clinical and Molecular Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189 Rome, Italy<b>Background:</b> 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. <b>Aim:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2306-5354/12/7/769cytologycytopathologycancercervixcervicalartificial intelligence |
| spellingShingle | Daniele Giansanti Andrea Lastrucci Antonia Pirrera Sandra Villani Elisabetta Carico Enrico Giarnieri AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies Bioengineering cytology cytopathology cancer cervix cervical artificial intelligence |
| title | AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies |
| title_full | AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies |
| title_fullStr | AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies |
| title_full_unstemmed | AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies |
| title_short | AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies |
| title_sort | ai in cervical cancer cytology diagnostics a narrative review of cutting edge studies |
| topic | cytology cytopathology cancer cervix cervical artificial intelligence |
| url | https://www.mdpi.com/2306-5354/12/7/769 |
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