Article ; Online: Digital health interventions for cervical cancer care: A systematic review and future research opportunities.
2023 Volume 18, Issue 12, Page(s) e0296015
Abstract: Background: Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature ... ...
Abstract | Background: Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations. Methods: This study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion. Results: The review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care. Conclusions: The findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society. |
---|---|
MeSH term(s) | Female ; Humans ; Uterine Cervical Neoplasms/diagnosis ; Prospective Studies ; Artificial Intelligence |
Language | English |
Publishing date | 2023-12-15 |
Publishing country | United States |
Document type | Systematic Review ; Journal Article |
ZDB-ID | 2267670-3 |
ISSN | 1932-6203 ; 1932-6203 |
ISSN (online) | 1932-6203 |
ISSN | 1932-6203 |
DOI | 10.1371/journal.pone.0296015 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.