Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions

This paper proposes AI applications for early ovarian tumour diagnosis that are examined in the systematic review, which covers relevant data up to the stated date. It includes Machine Learning, Deep Learning, and Computer-Aided diagnostic methods are used in ultrasound, MRI, CT, clinical, and genomic information. AI models showed improved accuracy, sensitivity, and specificity in discriminating benign and malignant ovarian tumours. Despite advances, many problems remain. To train and evaluate AI models, richer and more varied datasets are needed. AI algorithms are lack in standardisation, making repeatability and (comparability between researches difficult. AI models may perform well in research, but clinical application requires thorough validation in real-world condition. Collaboration is essential to overcome these obstacles for progress the profession. Clinicians, researchers, and data scientists may collaborate to integrate AI-driven techniques into clinical operations. Data sharing and standardisation may also improve AI model robustness and generalizability. These Prospective studies are to verify the proposed AI models in various clinical situations and ensure their effectiveness and safety in everyday practice. While AI might revolutionise early ovarian tumour diagnosis, its broad implementation needs coordinated efforts to address hurdles. Using AI-driven techniques to enhance the ovarian cancer outcomes requires addressing the algorithm standardisation, clinical validation and size of dataset. Collaborative efforts are advance the field and maximise AI's potential in early ovarian tumour identification.