Preprint Review Version 2 Preserved in Portico This version is not peer-reviewed

Literature Analysis of Artificial Intelligence in Biomedicine

Version 1 : Received: 28 April 2021 / Approved: 5 May 2021 / Online: 5 May 2021 (12:54:40 CEST)
Version 2 : Received: 8 June 2021 / Approved: 9 June 2021 / Online: 9 June 2021 (11:23:48 CEST)

How to cite: Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Preprints 2021, 2021050056 (doi: 10.20944/preprints202105.0056.v2). Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Preprints 2021, 2021050056 (doi: 10.20944/preprints202105.0056.v2).

Abstract

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Subject Areas

artificial intelligence; machine learning; deep learning; neural networks; biomedicine; healthcare; medicine; literature; PubMed; Embase

Comments (1)

Comment 1
Received: 9 June 2021
Commenter: Tim Hulsen
Commenter's Conflict of Interests: Author
Comment: Updated introduction and discussion sections, added table and made other improvements throughout the paper.
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