Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

By S. Bini. Journal of Arthroplasty, 2018

536 citations recorded by the source corpus.

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it.

Subfields: Artificial Intelligence, Biomedical Engineering, Health Informatics

View Paper: https://www.semanticscholar.org/paper/846ff7afb7670d62f88b4a8cc99d306ffb81b075

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In Artificial Intelligence

Citations in the corpus

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “In computer science, it is defined as the machine’s capacity to emulate intelligent behavior by itself, using nothing but ML [3].”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “Recent increases in computational speed have led to the development of even more powerful artificial neural networks and deep learning algorithms that can handle and optimize very complex datasets (Bini, 2018; Kalfa et al., 2020).”
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, background): “Actually, what makes deep learning different from standard machine learning is not the count of layers but how the information being processed and transformed over times (40,41).”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “This quite lax definition has allowed companies to claim they offer AI-powered products and services (a strategy dubbed “ AI washing, ” see Bini, 2018), where most AI researchers would be dubious at best to qualify them as such.”