Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care. •The salient features of this review article with potential clinical relevance include:•Using AI and ML algorithms, tumor volume, shape, and size can be reliably quantified.•Three key biological functions- screening, classification, and monitoring of tumors can be improved with radio-genomics.•AI and ML can aid in image-guided interventions, such as tumor biopsies and surgeries.•AI and ML provide a wider variety of tools that have shown great potential for cancer imaging in the early detection and preclusion of cancers.•AI, ML, and DL approaches can analyze genomic and proteomic data to identify specific biomarkers associated with cancers.