Artificial Intelligence and Its Impact on Enhancing Women’s Health and Medical Condition Monitoring.
Authors
Tejaswini Reddy Beerapu
Masters in Computer Science (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1410000068
Subject Category: Healthcare
Volume/Issue: 14/10 | Page No: 535-539
Publication Timeline
Submitted: 2025-11-10
Published: 2025-11-10
Abstract
Abstract: Artificial intelligence (AI) is revolutionizing healthcare, particularly in the field of disease detection. AI-powered algorithms can analyze vast datasets of medical images, patient records, and genetic information to identify patterns and predict disease risks with unprecedented accuracy. In women's health, AI applications are proving particularly promising in areas such as breast cancer detection, early diagnosis of ovarian cancer, and prenatal risk assessment. AI-powered imaging analysis can significantly improve the accuracy and efficiency of mammograms and ultrasounds, leading to earlier detection and improved treatment outcomes. AI algorithms can also analyze a woman's medical history, lifestyle factors, and genetic predisposition to predict her risk of developing certain diseases and personalize preventive care strategies. As AI continues to evolve, it holds immense potential to transform women's healthcare by enabling earlier detection, more accurate diagnoses, and more personalized treatment plans, ultimately improving women's health outcomes and longevity.
Keywords
Artificial Intelligence, Disease Detection, Healthcare, Medical Imaging, Predictive Analysis, Personalized Medicine
Downloads
References
1. Liu, Y., et al. (2020). “Artificial Intelligence in Ovarian Cancer Detection: A Systematic Review.” Journal of Ovarian Research, 13(1), 45-60. [Google Scholar] [Crossref]
2. Shbaih, M., et al. (2019). “Artificial Intelligence in Breast Cancer Detection and Diagnosis: A Comprehensive Review.” Journal of Medical Imaging and Health Informatics, 9(5), 1236-1247. [Google Scholar] [Crossref]
3. Sabaté, M., et al. (2020). “Artificial Intelligence in Multiple Sclerosis: Applications and Challenges.” Journal of Neuroinflammation, 17(1), 243. [Google Scholar] [Crossref]
4. Singh, H., et al. (2018). “Artificial Intelligence in Thyroid Disorders: From Diagnosis to Management.” Endocrine Reviews, 39(6), 931-948. [Google Scholar] [Crossref]
5. Gosling, A., et al. (2021). “Artificial Intelligence in Ectopic Pregnancy Diagnosis and Monitoring.” Journal of Obstetrics and Gynaecology Research, 47(1), 22-30. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Decision Tree and Automatic Linear Modeling Approaches to Predict Body Weight in Indigenous Sabi Sheep and Matebele Goat Females of Zimbabwe
- Microcontroller – Based Automatic Railway Crossing Control and Track Obstacle Monitoring System
- Modelling the Role of Absorptive Capacity in Foreign Direct Investment - Economic Growth Nexus: A Focus on South Africa’s Manufacturing Sector
- AI-Powered Wristband for Accurate BAC Monitoring Using Smart Data Fusion
- Climate Change Impacts on Different Regions