AccScience Publishing / IJOSI / Volume 9 / Issue 1 / DOI: 10.6977/IJoSI.202502_9(1).0004
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ARTICLE

A systematic meta-analysis on the role of artificial intelligence and machine learning in detection of gynaecological disorders 

Jyoti I. Nandalwar1* ,  Pradeep M. Jaeandhiya2
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1 CSE Dept., Shree Siddheshwar Women’s College of Engineering, Solapur, Maharashtra, India
2 Department Principal, P.R. PotePatil College of Engineering and Management, Amravati, Maharashtra, India
IJOSI 2025 , 9(1), 44–56; https://doi.org/10.6977/IJoSI.202502_9(1).0004
Submitted: 29 July 2024 | Revised: 11 October 2024 | Accepted: 21 October 2024 | Published: 19 February 2025
© 2025 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Globally, one of the major concerns in women’s health issues is gynecological disorders such as cancer, which needs to be observed at its early stage. With traditional approaches, it is quite difficult to detect such disorders at its early stages. Therefore, more advanced tools need to be integrated. This paper focuses the advancements of artificial intelligence (AI) and machine learning (ML), exploring their potential in the early detection and diagnosis of these disorders. This paper presents a systematic meta-analysis of AI/ML approaches employed in the diagnosis of gynecological disorders using medical imaging modalities such as magnetic resonance imaging (MRI), ultrasound, etc. The flow for systematic meta-analysis is based on designing the research objective, selection andsearching approach with inclusion and exclusion strategy; quality assessment is performed then; and finally, discussion of interpretations is also presented. This paper investigates how ML algorithms can extract characteristics from MRI images and how to use ML to extract and recognize the features from medical images such as MRI, ultrasound, computed tomography (CT) scans, etc. for early detection of gynecological tumors and provision of more personalized risk assessment. However, it is observed that there is a significant impact of advancement of AI/ML on medical technology in the future. Therefore, this paper presents a significant contribution for future medical applications and innovations.

Keywords
Artificial Intelligence
Gynecological Cancer
Machine Learning
MRI
Ultrasound
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