Abstract
Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.
Highlights
The term Artificial intelligence (AI) was first coined by John McCarthy at the Dartmouth Summer Research Project on Artificial Intelligence in 1955
This review identified the potential limitations and challenges and discusses the prospects and future directions in the context of reproductive medicine
The feed-forward artificial neural network (FANN) achieved a high accuracy of 91.03%
Summary
The term AI was first coined by John McCarthy at the Dartmouth Summer Research Project on Artificial Intelligence in 1955. AI is defined as the ability of machines to learn and display intelligence, which is in stark contrast to the natural intelligence demonstrated by humans and animals. AI has developed rapidly and gradually penetrated our personal and social life since . Computers, driven by computer power, memory, data storage and large amounts of data, have been handling increasingly complex learning tasks with incredible success (Deo 2015). AI applications used in daily life include speech recognition (Abdel-Hamid et al 2014), face recognition (Taigman et al 2014), game AI (Silver et al 2016), intelligent voice assistant (Strayer et al 2017) and self-driving vehicles (Katrakazas et al 2015). There is no doubt that AI applications will become faster, smarter and more accessible. Despite progress, achieving universality is still a considerable challenge (LeCun et al 2015, Esteva et al 2017)
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