The future of cervical cancer detection: a comprehensive analysis of artificial intelligence technologies
Abstract
Author(s): Nazia Khan, Srikar Praneeth Chilla, Anirudh Reddy Addula, Aditya Kaushal Paul Reddymas, Preeti Kale* and Akshaya N Shetti
Cervical cancer continues to pose a significant global health burden, despite advancements in screening and early detection. In recent years, the integration of Artificial Intelligence (AI) technologies has emerged as a promising approach to enhance cervical cancer detection and diagnosis. The challenges associated with existing screening methods; AI-based approaches that have been developed to address these challenges are explored. Through a systematic review of the literature, we highlight the strengths and limitations of different AI algorithms and methodologies employed in cervical cancer detection, the role of machine learning, deep learning, and other AI techniques in improving the accuracy and efficiency of screening programs, as well as their potential impact on reducing disparities in cervical cancer outcomes. The integration of AI technologies into existing screening frameworks, including the use of automated systems for image interpretation, decision support tools for healthcare providers, and mobile health applications for patient education and engagement and the regulatory and ethical considerations surrounding the deployment of AI in cervical cancer detection are discussed. Review highlights future directions and emerging trends in AI-driven cervical cancer detection, the transformative potential of AI technologies in revolutionizing the future of cervical cancer detection and the importance of collaborative efforts among researchers, healthcare providers, policymakers, and industry stakeholders to realize this vision.
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Editors List
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Ahmed Hussien Alshewered
University of Basrah College of Medicine, Iraq
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Sudhakar Tummala
Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh
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Alphonse Laya
Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis
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Fava Maria Giovanna
- Manuprasad Avaronnan
Onkologia i Radioterapia peer review process verified at publons
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