Current trends in artificial intelligence in reproductive endocrinology

Abstract

Purpose of reviewArtificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings. Recent findingsArtificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.

Technology in Training Delivery of Education Management: AI in ELT Approach

“…AI is a technology designed to make computer systems capable of imitating human intellectual abilities. It enables computers to learn from experience, identify patterns, make decisions, and complete complex tasks quickly and efficiently (Maecham, 2020; Bhaskar et al, 2022).…”

Blockchain-Based Medical Decision Support System

“…[24] + + Machine learning-based classification models for predicting the success of intrauterine insemination therapy [25] + The different components of artificial intelligence were used for selection of the embryos with high implantation potential and with proper ploidy status, for prediction of later embryo development, and for increase of pregnancy and live birth rates, i.e. in different areas of reproductive medicine [26] + Automated computer vision-based image system to localize and grade blastomeres (as a preprocessing embryos selection step for the in-vitro fertilization) before injection, which supports the localization and counting of blastomeres [27] + Smartphone app "OHSS monitor" for risk calculation and patients' self-monitoring of multiphase prediction models of syndrome of ovarian hyperstimulation based on big data; this app is part of the InVitroFertilization-platform and can be used for patients' self-management and medical decision support [28] + Smart systems with case-based reasoning, which help to doctors to give the rapid responses to in vitro fertilization patients in the case of any abnormal symptoms [29] + Intelligent MDSS about the possibility of the surrogate motherhood [30] + Intelligent agent for support of decision making about in vitro fertilization possibility [31] + (Continued) where A -generated alternatives; B -basic elements; P -the rules that are the basis for generating alternatives of the set A from the elements of the set B; M -methods used in information processing.…”

Inteligência artificial, noções básicas para os profissionais de saúde

“…Em saúde, a IA tem começado a ser incorporada em diversos processos. É possível observar o início do uso de machine learning, por exemplo, para análise de imagens com a finalidade de diagnóstico em diferentes tipos de câncer (7) ou em reprodução assistida para a determinação da qualidade do blastocisto e seu potencial de implantação (8) . Na Saúde Pública, é possível utilizar IA para realizar a predição de custo em saúde (9) ou detectar possíveis procedimentos fraudulentos (10) , além de predizer mortalidade neonatal (11) ou risco de incidência de câncer como o de mama (12) .…”