Transformative Insights: Harnessing Artificial Intelligence for Enhanced Ovarian Cancer Prediction and Prognosis

Introduction

The oncology field is still grappling with devastatingly poor survival rates and remarkably high incidence rates of ovarian cancer in places like Indonesia (Olatunji et al., 2023). Ovarian cancer is the fifth most common cancer in women, and human clinical decision-making is struggling to keep up with the illness because of the growing complexity of prognostic indications, treatment options, and their sequencing (Poort et al., 2021). In this light, it becomes clear that incorporating Artificial Intelligence (AI) into ovarian cancer management decision-making processes is a viable option (Akazawa & Hashimoto, 2020). The vital role of AI in ovarian cancer prognosis is examined in this chapter. The increasing amount of data accessible and the need for detailed analysis necessitate a method that is beyond what humans are capable of. The computing power of AI provides a mechanism to analyze complex patterns in patient data, make predictions about outcomes, and help doctors choose the best course of therapy. The creation and implementation of AI systems for ovarian cancer patient knowledge, prediction, and possible improvement of outcomes is the focus of this chapter. Worrying patterns and statistics in ovarian cancer highlight the significance of the research. As the sixth most common cancer in women, ovarian cancer has a dismal five-year survival rate in Indonesian healthcare. (Dewi et al., 2022). Indonesia has one of the worst incidence rates in Europe, and they are becoming worse. This means that we need new solutions quickly (Reid, 2020). Because of the growing complexity of both prognostic indicators and treatment sequences, as well as the difficulties in making informed clinical decisions by humans, sophisticated computational tools are urgently needed (Degu et al., 2023). Analogies with other studies also confirm the study's usefulness. The knowledge and information yielded by this research hold significant implications for various stakeholders involved in the management of ovarian cancer, including Clinicians can leverage the predictive models developed through AI to enhance their decision-making processes in ovarian cancer treatment. The ability to analyze prognostic markers and predict outcomes, including overall survival and surgical success, equips healthcare professionals with valuable insights for personalized patient care. Patients stand to benefit directly from the improved predictive accuracy of AI models. Access to more accurate prognoses can empower patients to make informed decisions about their treatment options and actively participate in their healthcare journey. Researchers in the field of oncology and cancer biology can use the findings to explore further and refine AI applications in cancer research. The identification of novel prognostic biomarkers using AI techniques contribute to the broader understanding of ovarian cancer at the molecular level. Healthcare institutions and lawmakers can think about using AI technology in clinical practice. Ovarian cancer management healthcare policies and recommendations may be impacted by this research's findings, which show that AI may improve prediction models. Companies involved in pharmaceuticals and biotechnology may find value in the identification of novel prognostic biomarkers. This knowledge could guide the development of targeted therapies or interventions for specific subgroups of ovarian cancer patients, contributing to advancements in treatment modalities. Academic institutions can incorporate the findings into medical curricula, ensuring that the next generation of healthcare professionals is well-versed in the potential applications of AI in oncology. Organizations focused on global health, such as the World Health Organization (WHO), can use the research findings to inform strategies for addressing ovarian cancer on a broader scale, particularly in regions with high incidence rates. These stakeholders may work together to improve outcomes, make better-informed decisions, and enhance ovarian cancer care by sharing and using the information from this study.