| Recent Advances in Applications of Machine Learning in Cervical Cancer Research: A Focus on Prediction Models |
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Syed S Abrar1, Seoparjoo Azmel Mohd Isa2, Suhaily Mohd Hairon1, Mohd Pazudin Ismail3, Mohd Nasrullah Bin Nik Ab Kadir4 |
1Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
2Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
3Department of Obstetrics and Gynaecology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
4Public Health Division, Johor State Health Department, Johor Bahru, Malaysia |
Correspondence:
Seoparjoo Azmel Mohd Isa,
Email: seoparjooazmel@usm.my
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Received: 18 February 2025 • Revised: 8 April 2025 • Accepted: 7 May 2025 |
| Abstract |
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Artificial intelligence (AI) and machine learning (ML) are transforming cervical cancer research and offering advancements in diagnosis, prognosis, screening, and treatment. This review explores ML applications with particular emphasis on prediction models. A comprehensive literature search identified studies using ML for survival prediction, risk assessment, and treatment optimization. ML-driven prognostic models integrate clinical, histopathological, and genomic data to improve survival prediction and patient stratification. Screening methods, including deep-learning-based cytology analysis and HPV detection, enhance accuracy and efficiency. ML-driven imaging techniques facilitate early and precise cancer diagnosis, whereas risk prediction models assess susceptibility based on demographic and genetic factors. AI also optimizes treatment planning by predicting therapeutic responses and guiding personalized interventions. Despite significant progress, challenges remain regarding data availability, model interpretability, and clinical implementation. Standardized datasets, external validation, and cross-disciplinary collaborations are crucial for implementing ML innovations in clinical settings. Subsequent investigations should prioritize joint initiatives among data scientists, healthcare providers, and health authorities to translate AI innovations into real-world applications and to enhance the impact of ML on cervical cancer care. By synthesizing recent developments, this review highlights the potential of ML to improve clinical outcomes and shaping the future of cervical cancer management. |
| Key Words:
Cervical cancer, Artificial intelligence, Machine learning, Prognosis, Cancer diagnosis |
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