AI-based early detection of cervical cancer: A new hope for cancer prevention in indonesia
DOI: https://doi.org/10.56922/mchc.v4i8.1745
Artificial Intelligence Cervical Cancer Early Detection Medical Ethics
Abstract
Background: Cervical cancer is one of the leading causes of death among women in Indonesia, mostly due to delayed diagnosis. Early detection is a key step in reducing mortality rates, but conventional methods such as Pap smears and visual inspection with acetic acid (VIA) still face obstacles such as limited medical personnel, subjectivity of results, and low screening coverage in remote areas.
Purpose: to analyze the potential application of artificial intelligence (AI) as an innovative solution to improve the effectiveness and efficiency of early detection of cervical cancer in Indonesia.
Method: The study used a descriptive qualitative approach through a literature review of scientific journals, international agency reports (WHO, GLOBOCAN), and national policies related to digital health transformation.
Results: The study shows that the application of deep learning-based AI can increase the sensitivity and specificity of detection to over 90%, speed up the analysis process from day to minute, and reduce the operational costs of examinations by up to 40%. In addition, AI has the potential to expand the scope of screening and strengthen the national health referral system through digital integration and cloud-based telemedicine. However, the main challenges faced include data privacy issues, algorithmic bias, legal liability, and digital infrastructure gaps that must be addressed with strong ethical policies and oversight.
Conclusion: The application of AI in early detection of cervical cancer is a strategic step towards a more equitable, efficient, and sustainable healthcare system in Indonesia, provided that it is implemented responsibly, transparently, and with a focus on patient safety.
Downloads
References
Al-Dmour, J. A., Sagahyroon, A., Al-Ali, A. R., & Abusnana, S. (2019). A fuzzy logic–based warning system for patients classification. Health Informatics Journal, 25(3), 1004-1024.
Allahqoli, L., Lagana, A. S., Mazidimoradi, A., Salehiniya, H., Guenther, V., Chiantera, V., & Alkatout, I. (2022). Diagnosis of cervical cancer and pre-cancerous lesions by artificial intelligence: a systematic review. Diagnostics, 12(11), 2771.
Caloro, E., Cè, M., Gibelli, D., Palamenghi, A., Martinenghi, C., Oliva, G., & Cellina, M. (2023). Artificial Intelligence (AI)-based systems for automatic skeletal maturity assessment through bone and teeth analysis: a revolution in the radiological workflow? Applied Sciences, 13(6), 3860.
Chen, W., Shen, W., Gao, L., & Li, X. (2022). Hybrid loss-constrained lightweight convolutional neural networks for cervical cell classification. Sensors, 22(9), 3272.
Egemen, D., Perkins, R. B., Cheung, L. C., Befano, B., Rodriguez, A. C., Desai, K., & Schiffman, M. (2024). Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening. JNCI: Journal of the National Cancer Institute, 116(1), 26-33.
Gronberg, M. (2023). The Development of Artificial Intelligence-Based Tools for Expert Peer Review of Radiotherapy Treatment Plans.
Harsono, A. B., Susiarno, H., Suardi, D., Owen, L., Fauzi, H., Kireina, J., & Hidayat, Y. M. (2022). Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence. BMC research notes, 15(1), 356.
Hussain, S. I., & Toscano, E. (2024). An extensive investigation into the use of machine learning tools and deep neural networks for the recognition of skin cancer: Challenges, future directions, and a comprehensive review. Symmetry, 16(3), 366.
Khare, S. K., Blanes‐Vidal, V., Booth, B. B., Petersen, L. K., & Nadimi, E. S. (2024). A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(6), e1550.
Kolsanova, A. V., Chechko, S. M., Kira, E. F., & Shamshatdinova, A. R. (2024). Cervical screening and artificial intelligence. Science and Innovations in Medicine, 9(4), 246-250.
Kwon, I. H. (2016). Book Review: Applied Computing in Medicine and Health. Healthcare Informatics Research, 22(2), 151-152.
Latief, S., Syahruddin, F. I., Nulanda, M., & Mokhtar, S. (2023). Faktor Risiko Penderita Kanker Ovarium di Rumah Sakit Ibnu Sina Makassar. Wal'afiat Hospital Journal, 4(1), 46-56.
Lestari, D. P. O., Analysa, A., Kerans, F. A., Winata, I. G. S., & Riasa, I. N. P. (2025). Peningkatan Peran OSIS Dalam Pencegahan Kanker Serviks Dan Vaksinasi HPV Pada Siswi SMP di Bali. INCOME: Indonesian Journal of Community Service and Engagement, 4(3), 130-139.
Magfiroh, N., Sasmito, G. W., & Ilmadina, H. Z. (2025). A Web-Based Chatbot-Integrated Application for Skin Disease Detection Using ResNet50 Architecture. Journal of Applied Informatics Science, 1(1), 5-13.
Manukyan, N. V., Tamamyan, G. N., Avetisyan, A. A., Jilavyan, S. A., Saghatelyan, T. S. (2023). A Review of Challenges and Prospects of Mobile Mammography Screening in Developing Countries.
Merchant, S. A., Shaikh, M. J. S., & Nadkarni, P. (2022). Tuberculosis conundrum-current and future scenarios: a proposed comprehensive approach combining laboratory, imaging, and computing advances. World Journal of Radiology, 14(6), 114.
Nuswil, B., Umi, R., Cornelius, J., Nurmaini, S., Pribadi, A., & Manan, H. (2022). Pengembangan Artificial Intelligence Dan Biomarker Tnf-Alfa, Vegf-D, dan Hb-Egf Untuk Deteksi Dini Penyakit Jantung Kongenital Terdiagnosis Intrauterin.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Paiboonborirak, C., Abu‐Rustum, N. R., & Wilailak, S. (2025). Artificial intelligence in the diagnosis and management of gynecologic cancer. International Journal of Gynecology & Obstetrics, 171, 199-209.
Prawesti, A. D. (2023). The Effectiveness of Implementing Mobile Health Applications in Cancer Patients: A Literature Review. Jurnal Kesehatan Pasak Bumi Kalimantan, 6(1), 19-28.
Rachmatullah, M. N., Nurmaini, S., Agustiansyah, P., Sastradinata, I., Arum, A. W., Darmawahyuni, A., & Islami, A. (2025). TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions. Computer Engineering and Applications Journal (ComEngApp), 14(1), 40-51.
Ramapraba, P. S., Radhika, M., Sumathi, S., Karthik, J., & Senthamilarasi, N. (2024). An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 34(3), 1698-1707.
Sangwa, S., & Mutabazi, P. (2025). AI-Driven Healthcare Entrepreneurship: Transforming Clinical Practice Through Innovation, Access, and Affordability. Access and Affordability (September 15, 2025).
Saripah, S., Putri, R., & Lisca, S. M. (2023). Efektivitas Penyuluhan Kesehatan Dengan Media Power Point Dan Audio Visual Terhadap Peningkatan Pengetahuan Wanita Usia Subur Tentang Kanker Serviks Di Wilayah Kerja Puskesmas Bayongbong Kabupaten Garut Tahun 2023. SENTRI: Jurnal Riset Ilmiah, 2(10), 4387-4400.
Shen, Z., Li, Z., Li, Y., Tang, X., Lu, J., Chen, L., & Zhou, S. (2025). PSMA PET/CT for prostate cancer diagnosis: current applications and future directions. Journal of Cancer Research and Clinical Oncology, 151(5), 155.
Singh, A., & Pravin, S. C. (2025). AI Guided Early Screening of Cervical Cancer. The New Armenian Medical Journal, 3(2), 1–15.
Soimah, N., & Istiyati, S. (2024). Pemberdayaan masyarakat pembentukan kader deteksi dini kanker rahim dan payudara di Dusun Ganggom Bangunkerto Turi Sleman. BEMAS: Jurnal Bermasyarakat, 4(2), 368-374.
Wang, J., Yu, Y., Tan, Y., Wan, H., Zheng, N., He, Z., & Yao, H. (2024). Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer. Nature Communications, 15(1), 4369.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 THE JOURNAL OF Mother and Child Health Concerns

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.









