Artificial Intelligence Explained for Nonexperts

FEATURED RESEARCH PAPER, 17 Mar 2025

Narges Razavian, Florian Knoll and Krzysztof J. Geras | HHS Public Access - TRANSCEND Media Service

Abstract

Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this paper we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

Artificial Intelligence and Machine Learning

Artificial intelligence (AI) is a broad field that investigates constructing computer programs that exhibit some form of intelligent behavior. Examples of such behavior include playing board games, recognizing speech, identifying objects in images, understanding relations between entities in text, or translating text between two languages. Machinelearning1–3 is currently a dominant approach to AI, based on the principle of learning from data. This principle can be contrasted with attempting to write a program explicitly to achieve a specific task, which was the focus of many early attempts at creating AI.

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Artificial Intelligence Explained for Nonexperts

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Narges Razavian, Ph.D. – Department of Radiology and Population Health, NYU Langone Health and NYU Center for Data Science, New York, New York

Florian Knoll, Ph.D. – Department of Radiology, NYU Langone Health, New York, New York

Krzysztof J. Geras, Ph.D. – Department of Radiology, NYU Langone Health and NYU Center for Data Science, New York

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This article originally appeared on Transcend Media Service (TMS) on 17 Mar 2025.

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