Literature DB >> 32246300

Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists.

Filippo Pesapane1, Priyan Tantrige2, Francesca Patella3, Pierpaolo Biondetti4, Luca Nicosia4, Andrea Ianniello5, Umberto G Rossi6, Gianpaolo Carrafiello7,8, Anna Maria Ierardi7.   

Abstract

Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. Despite the overwhelmingly positive uptake of the technology, warnings have been published about the potential dangers of AI. Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Interventional radiology; Machine learning

Year:  2020        PMID: 32246300     DOI: 10.1007/s12032-020-01368-8

Source DB:  PubMed          Journal:  Med Oncol        ISSN: 1357-0560            Impact factor:   3.064


  4 in total

Review 1.  Challenges and opportunities of digital health in a post-COVID19 world.

Authors:  Amirreza Manteghinejad; Shaghayegh Haghjooy Javanmard
Journal:  J Res Med Sci       Date:  2021-02-16       Impact factor: 1.852

Review 2.  Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.

Authors:  Martina Gurgitano; Salvatore Alessio Angileri; Giovanni Maria Rodà; Alessandro Liguori; Marco Pandolfi; Anna Maria Ierardi; Bradford J Wood; Gianpaolo Carrafiello
Journal:  Radiol Med       Date:  2021-04-16       Impact factor: 3.469

Review 3.  Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

Authors:  Chandrabose Selvaraj; Ishwar Chandra; Sanjeev Kumar Singh
Journal:  Mol Divers       Date:  2021-10-23       Impact factor: 2.943

4.  In search of a Goldilocks zone for credible AI.

Authors:  Kevin Allan; Nir Oren; Jacqui Hutchison; Douglas Martin
Journal:  Sci Rep       Date:  2021-07-01       Impact factor: 4.379

  4 in total

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