A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

Abstract

The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.

Patient-centered precision care in anaesthesia – the PC-square (PC)2 approach

“…Data integration: Collect data from various sources, including anaesthesia records, patient-reported outcomes (PROs), and clinical data, to create a comprehensive patient profile. Integrating this data into the anaesthesia care pathway enhances understanding of the patient's condition and response to anaesthesia [47].…”

Advances in pediatric perioperative care using artificial intelligence

“…This application of AI has been used to help anesthesia trainees develop technical skills, as well as improve communication and collaboration [43 ▪ ]. Simulations using AI-based augmented reality (AR) would allow trainees to practice clinical scenarios in pediatric settings [44 ▪ ]. Furthermore, the use of AI in training may shorten the learning curve for various technical skills such as ultrasound guided peripheral nerve blocks [42 ▪ ].…”

Machine learning and disease prediction in obstetrics

“…In intrapartum management, studies lacked external validation or quality assessment ( Tan et al, 2021 ; Ghi et al, 2022 ; Tsur et al, 2020 ; Venkatesh et al, 2020 ; Chill et al, 2021 ) but are a first step in developing clinical decision assist tools to improve safety standards. Developing multicentric databases with public availability and having multi-disciplinary and international collaborations will therefore accelerate research and improve the diversity of datasets to better represent minority groups ( Meshaka et al, 2022 , Malani et al, 2023 ).…”