Artificial Intelligence in Imaging Diagnostics

Abstract

Artificial intelligence (AI) is touted as one of the promising advancements in medical technology, especially in diagnostic imaging. As the use of AI promises and proves that it improves accuracy and efficiency while seamlessly optimizing current medical workflows, its ubiquity is inevitable. This chapter focuses on the road taken to get to this point and invokes the necessary discussion required to achieve a sustainable and equitable healthcare ecosystem. Establishing a foundation of AI through a historical and technical exploration of technologies, ranging from early automation to modern architectures such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), helps contextualize how AI has and will continue to have an unprecedented impact in fields such as radiology, oncology, pathology, and cardiovascular imaging. This background allows readers to understand why emphasis should be placed on hybrid models that can optimize workflows, electronic health records, and decision support systems. Given the significant level of incorporation, this chapter also assesses the ethical and nonethical limitations to highlight the importance of regulatory oversight, data security, and bias propagation. At the crux of this chapter is AI’s usage as a tool instead of a replacement to ensure AI’s future in improving patient outcomes through diagnostics that are personalized and accurate without being intrusive.

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