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Artificial Intelligence and the Medical Physicist: Welcome to the Machine

descriptionPublicationkeyboard_double_arrow_right Article 13 Feb 2021 Italy Publisher:MDPI AGJournal:Applied Sciences, volume 11, page 1,691 (eissn: 2076-3417,

Authors: Avanzo, Michele; Trianni, Annalisa; Botta, Francesca; Talamonti, Cinzia; Stasi, Michele; Iori, Mauro;

doi: 10.3390/app11041691

handle: 2158/1237017

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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Abstract

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

Country

Italy

Related Organizations

Keywords

Technology, Artificial intelligence; Big data; Deep learning; Machine learning; Medical physicist, QH301-705.5, T, Physics, QC1-999, deep learning, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, machine learning, big data, medical physicist, TA1-2040, Biology (General), QD1-999

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	57
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%