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)
Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Annals of Translational Medicine 2022, 10, 1284–1284, doi:10.21037/atm-2022-50.
Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Annals of Translational Medicine 2022, 10, 1284–1284, doi:10.21037/atm-2022-50.
Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Annals of Translational Medicine 2022, 10, 1284–1284, doi:10.21037/atm-2022-50.
Hulsen, T. Literature Analysis of Artificial Intelligence in Biomedicine. Annals of Translational Medicine 2022, 10, 1284–1284, doi:10.21037/atm-2022-50.
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 other applications. Therefore, both academia and industry are spending a lot of time and money in AI. This review takes a look at the biomedical literature (in the PubMed and Embase databases) and does some interesting observations: AI is 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. 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. In order for AI to succeed in making healthcare better, it should be fully integrated into the clinician’s workflow so that they can focus on the contact with their patients.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
