Investigating Into Artificial Intelligence Trained Algorithms and Their Effectiveness in Addressing Female Infertility
Background Female infertility has always been an issue and social stigma in India and it’s not only the matter of physical health of the female but also related to psychological health and the social status of the women. The diagnosis of this problem criterion requires medical symptoms for the condition, including clinical and metabolic parameters. The application of AI-driven algorithms into medical science is responsible for the specific phrase Medical Artificial Intelligence (MAI) which has introduced a transformative swing in female reproductive health, qualifying higher diagnostic accuracy, reproducibility, and efficiency in addressing complex conditions such as infertility and hormonal disorders. From Ovarian stimulation to Artificial Intelligence in Assisted Reproductive Technology, there have been significant advancements in the incorporation of machine learning and IoT in the medical Labs. AI algorithms are expected to bring about a more calculated, computed, and standardized approach to the infertility problem today females are facing. Aim This review aims to establish connection between female infertility problems with AI and discusses how an effort can be made to resolve such sensitive health issue, keeping in view the recent and past contribution of emerging AI technologies in health sciences. The AI trained models are based on either supervised learning algorithms of unsupervised learning algorithms. Basically the input to the medical trained AI models is the set of instances referred to as medical dataset which can be categorized, clustered or correlated as per requirements. Each instance is further described by the values of a set of attributes like diagnosis, treatment, laboratory tests data, prescription drugs etc. These medical dataset can be further represented in the form of matrix and later can be used to aid learning methods that is supervised or unsupervised learning methods to train AI medical models. That is why the term MAI is being used in this review paper. Methods Used Studies were explored across IEEE Xplore, PubMed, ResearchGate, SpringerLink and npj Digital Medicine for papers published. Following the database probing, identical articles were pull out and the lasting titles and abstracts were inspected for suitability. Studies were incorporated if they portrayed the role of Artificial Intelligence for female reproductive health or infertility problems in reproductive age. Result Following data was extracted from 12 selected articles. These studies were eventually chosen for complete estimation. Conclusion AI has proved to be an incomparable assistance tool in providing specialist approach in the reproductive health issues in females. But there is need to describe ways to develop a framework of wearable technology in the form of sensor to predict correct ovulation period.