An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Abstract

This review article aims to discuss current trends, techniques, and promising uses of artificial intelligence (AI) in breast imaging, apart from the pitfalls that may hinder its progress. It includes only the commonly used and basic terminology imperative for physicians to know. AI is not just a computerized approach but an interface between humans and machines. Apart from reducing workload and improved diagnostic accuracy, radiologists get more time for patient care or clinical work by using various machine learning techniques that augment their productivity. Inadequate data input with suboptimal pattern recognition, data extraction challenges, legal implications, and exorbitant costs are a few pitfalls that AI algorithms still face while analyzing and giving appropriate outcomes. Various machine learning approaches are used to construct prediction models for clinical decision support and ameliorating patient management. Since AI is still in its fledgling state, with many limitations for clinical implementation, clinical support and feedback are needed to avoid algorithmic errors. Hence, both machine learning and human insight complement each other in revolutionizing breast imaging.

Keywords:

• machine learning
• augmented intelligence
• ANN
• CNN
• CAD
• GANs

Abbreviations

AI, artificial intelligence; NN, neural network; ANN, artificial neural networks; CNN, convolutional neural networks; CADe, computer-aided detection; CAD, computer-aided diagnosis; ML, machine learning; DL, deep learning; GAN, generative adversarial networks.

Acknowledgments

Rushank Goyal, CEO Betsos, Madhya Pradesh, India, for his contributions in delineating various AI techniques and revising the article.

Disclosure

The author reports no conflicts of interest in this work.