The Role of Artificial Intelligence in Cervical Cancer Screening: From Pap Smears to Deep Learning
DOI:
https://doi.org/10.63501/vxc3yh73Keywords:
Artificial Intelligence, Cancer, Pap smear, Cervical CancerAbstract
Cervical cancer screening has evolved from conventional Pap smears to liquid‐based cytology and HPV DNA testing, and now increasingly leverages AI-driven image analysis. Modern AI approaches – especially convolutional neural networks (CNNs) – can automatically detect and classify cells in digital cytology and whole‐slide images. Emerging transformer‐based models further integrate multimodal data (HPV status, cytology, colposcopy images) to enhance diagnostic sensitivity and reduce missed cases. These tools markedly increase detection rates while alleviating the burden on human screeners. In practice, portable AI-assisted devices are being fielded: for example, smartphone colposcopes with embedded deep learning have demonstrated CIN2+ sensitivities (>90%) that exceed expert readers. Cloud-linked systems (e.g. MobileODT’s EVA Scope) enable centralized analytics and regular model updates, while on-device AI can operate offline for low-connectivity settings.
However, limitations remain. Algorithmic bias from non-representative training data can skew performance, and the “black box” nature of deep models raises interpretability and trust issues. Regulatory and privacy gaps must be addressed – clear guidelines are needed to ensure patient data security and to define AI’s role as an adjunct (not a replacement) to clinician judgment. Moving forward, robust validation in diverse populations, human–AI collaboration in screening workflows, and equitable deployment in underserved regions are essential. By coupling AI with human expertise and broad access, these innovations can help reach WHO targets (70% screening by 2030) and accelerate progress toward global cervical cancer elimination
References
[1] “Cervical cancer.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cervical-cancer
[2] “manual-VPH-English---FINAL-version.pdf.” Accessed: Jun. 10, 2025. [Online]. Available: https://www3.paho.org/hq/dmdocuments/2016/manual-VPH-English---FINAL-version.pdf
[3] “Frontiers | Progress in Vaccination of Prophylactic Human Papillomavirus Vaccine.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2020.01434/full
[4] “Training for Cytotechnologists and Cytopathologists in the Developing World | Request PDF.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.researchgate.net/publication/308483918_Training_for_Cytotechnologists_and_Cytopathologists_in_the_Developing_World
[5] “Determinants of VIA (Visual Inspection of the Cervix After Acetic Acid Application) Positivity in Cervical Cancer Screening of Women in a Peri-Urban Area in Andhra Pradesh, India - PMC.” Accessed: Jun. 10, 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC2913449/
[6] “Screening for Cervical Cancer - PMC.” Accessed: Jun. 10, 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC8881993/
[7] X. Hou, G. Shen, L. Zhou, Y. Li, T. Wang, and X. Ma, “Artificial Intelligence in Cervical Cancer Screening and Diagnosis,” Front. Oncol., vol. 12, p. 851367, Mar. 2022, doi: 10.3389/fonc.2022.851367.
[8] S. Cheng et al., “Robust whole slide image analysis for cervical cancer screening using deep learning,” Nat. Commun., vol. 12, no. 1, p. 5639, Sep. 2021, doi: 10.1038/s41467-021-25296-x.
[9] L. Hu et al., “Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia,” Cancer Med., vol. 13, no. 11, p. e7355, Jun. 2024, doi: 10.1002/cam4.7355.
[10] E. Bengtsson and P. Malm, “Screening for Cervical Cancer Using Automated Analysis of PAP-Smears,” Comput. Math. Methods Med., vol. 2014, no. 1, p. 842037, 2014, doi: 10.1155/2014/842037.
[11] J. Wang et al., “Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer,” Nat. Commun., vol. 15, no. 1, p. 4369, May 2024, doi: 10.1038/s41467-024-48705-3.
[12] A. Alharthi, M. Alaryani, and S. Kaddoura, “A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems,” Array, vol. 26, p. 100406, Jul. 2025, doi: 10.1016/j.array.2025.100406.
[13] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, p. 53, Mar. 2021, doi: 10.1186/s40537-021-00444-8.
[14] “A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis | Artificial Intelligence Review.” Accessed: Jun. 10, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s10462-023-10588-z
[15] W. Gong et al., “Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology,” BMC Cancer, vol. 25, no. 1, p. 10, Jan. 2025, doi: 10.1186/s12885-024-13402-3.
[16] L. Liu, J. Liu, Q. Su, Y. Chu, H. Xia, and R. Xu, “Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis,” EClinicalMedicine, vol. 80, p. 102992, Feb. 2025, doi: 10.1016/j.eclinm.2024.102992.
[17] “Deep-Learning Approaches for Cervical Cytology Nuclei Segmentation in Whole Slide Images.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.mdpi.com/2313-433X/11/5/137
[18] “(PDF) DVS: Blood cancer detection using novel CNN-based ensemble approach.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.researchgate.net/publication/384769104_DVS_Blood_cancer_detection_using_novel_CNN-based_ensemble_approach
[19] “Enhancing cervical cancer cytology screening via artificial intelligence innovation | Scientific Reports.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.nature.com/articles/s41598-024-70670-6?error=cookies_not_supported&code=e843bd22-2a6e-40c1-ac65-e2f3aaec59d6
[20] K. Basak, K. B. Ozyoruk, and D. Demir, “Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls,” Turk. J. Pathol., vol. 39, no. 2, pp. 101–108, doi: 10.5146/tjpath.2023.01601.
[21] A. K. and S. B., “A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer,” J. Imaging Inform. Med., vol. 37, no. 1, pp. 280–296, Jan. 2024, doi: 10.1007/s10278-023-00911-z.
[22] “Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.mdpi.com/1424-8220/24/20/6682
[23] “RePORT ⟩ RePORTER.” Accessed: Jun. 10, 2025. [Online]. Available: https://reporter.nih.gov/search/vGMwt4iJJUqS46N7Jz3XAg/project-details/10920866
[24] S. A. Scientist Data, “On Device AI: What It Is and How It Works?,” Medium. Accessed: Jun. 10, 2025. [Online]. Available: https://medium.com/@sahin.samia/on-device-ai-what-it-is-and-how-it-works-89721ee68792
[25] “Advances of AI in image-based computer-aided diagnosis: A review - ScienceDirect.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2590005624000237
[26] “The Application of Artificial Intelligence-Assisted Colposcopy in a Tertiary Care Hospital within a Cervical Pathology Diagnostic Unit - PubMed.” Accessed: Jun. 10, 2025. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/35054273/
[27] “verixiv.org/articles/2-54.” Accessed: Jun. 10, 2025. [Online]. Available: https://verixiv.org/articles/2-54
[28] “Explore on-device AI: AI without internet and cloud.” Accessed: Jun. 10, 2025. [Online]. Available: https://upstage.ai/blog/en/on-device-ai
[29] “Screening-and-treatment-of-precancerous-lesions-for-secondary-prevention-of-cervical-cancer-technology-landscape-report.pdf.” Accessed: Jun. 10, 2025. [Online]. Available: https://unitaid.org/uploads/Screening-and-treatment-of-precancerous-lesions-for-secondary-prevention-of-cervical-cancer-technology-landscape-report.pdf
[30] “(PDF) The State of Telepathology in Africa in the Age of Digital Pathology Advancements: A Bibliometric Analysis and Literature Review.” Accessed: Jun. 10, 2025. [Online]. Available: https://www.researchgate.net/publication/381993951_The_State_of_Telepathology_in_Africa_in_the_Age_of_Digital_Pathology_Advancements_A_Bibliometric_Analysis_and_Literature_Review
[31] A. Choudhary, “Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions,” Discov. Internet Things, vol. 4, no. 1, p. 31, Dec. 2024, doi: 10.1007/s43926-024-00084-3.
[32] O. Asan, A. E. Bayrak, and A. Choudhury, “Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians,” J. Med. Internet Res., vol. 22, no. 6, p. e15154, Jun. 2020, doi: 10.2196/15154.
[33] D. J. Blezek, L. Olson-Williams, A. Missert, and P. Korfiatis, “AI Integration in the Clinical Workflow,” J. Digit. Imaging, vol. 34, no. 6, pp. 1435–1446, Dec. 2021, doi: 10.1007/s10278-021-00525-3.
[34] K. Palaniappan, E. Y. T. Lin, and S. Vogel, “Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector,” Healthcare, vol. 12, no. 5, p. 562, Feb. 2024, doi: 10.3390/healthcare12050562.
[35] A. M. PhD, “AI in Organizational Change Management — Case Studies, Best Practices, Ethical Implications, and…,” Medium. Accessed: Jun. 10, 2025. [Online]. Available: https://medium.com/@adnanmasood/ai-in-organizational-change-management-case-studies-best-practices-ethical-implications-and-179be4ec2583
[36] H. Y. Wong and E. L. Wong, “Invitation strategy of vaginal HPV self-sampling to improve participation in cervical cancer screening: a systematic review and meta-analysis of randomized trials,” BMC Public Health, vol. 24, no. 1, p. 2461, Sep. 2024, doi: 10.1186/s12889-024-19881-0.
[37] M. Dellino et al., “Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges,” AI, vol. 5, no. 4, Art. no. 4, Dec. 2024, doi: 10.3390/ai5040144.
[38] M. Malhotra, A. K. Shaw, S. R. Priyadarshini, S. S. Metha, P. K. Sahoo, and A. Gachake, “Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis,” Asian Pac. J. Cancer Prev. APJCP, vol. 25, no. 8, pp. 2593–2603, 2024, doi: 10.31557/APJCP.2024.25.8.2593.
[39] S. Mohammadi, A. Balador, S. Sinaei, and F. Flammini, “Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics,” J. Parallel Distrib. Comput., vol. 192, p. 104918, Oct. 2024, doi: 10.1016/j.jpdc.2024.104918.
[40] N. Yadav, S. Pandey, A. Gupta, P. Dudani, S. Gupta, and K. Rangarajan, “Data Privacy in Healthcare: In the Era of Artificial Intelligence,” Indian Dermatol. Online J., vol. 14, no. 6, pp. 788–792, Oct. 2023, doi: 10.4103/idoj.idoj_543_23.
[41] J. JPC Rodrigues, I. de la Torre, G. Fernández, and M. López-Coronado, “Analysis of the Security and Privacy Requirements of Cloud-Based Electronic Health Records Systems,” J. Med. Internet Res., vol. 15, no. 8, p. e186, Aug. 2013, doi: 10.2196/jmir.2494.
[42] S. Nazir, D. M. Dickson, and M. U. Akram, “Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks,” Comput. Biol. Med., vol. 156, p. 106668, Apr. 2023, doi: 10.1016/j.compbiomed.2023.106668.
[43] H. Gaffney and K. M. Mirza, “Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight,” Acad. Pathol., vol. 12, no. 1, p. 100166, Feb. 2025, doi: 10.1016/j.acpath.2025.100166.
[44] D. D. Farhud and S. Zokaei, “Ethical Issues of Artificial Intelligence in Medicine and Healthcare,” Iran. J. Public Health, vol. 50, no. 11, pp. i–v, Nov. 2021, doi: 10.18502/ijph.v50i11.7600.
Downloads
Published
Issue
Section
License
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided appropriate credit is given to the author(s) and the source. To view a copy of this license, visit: https://creativecommons.org/licenses/by/4.0