Literature DB >> 35528308

Why Machine Learning Should Be Taught in Medical Schools.

Matthew Nagy1, Nathan Radakovich1, Aziz Nazha2.   

Abstract

The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught broadly to medical students across the country.
© The Author(s) under exclusive licence to International Association of Medical Science Educators 2022.

Entities:  

Keywords:  Artificial intelligence; Machine learning; Medical education

Year:  2022        PMID: 35528308      PMCID: PMC9054965          DOI: 10.1007/s40670-022-01502-3

Source DB:  PubMed          Journal:  Med Sci Educ        ISSN: 2156-8650


  16 in total

Review 1.  Anniversary paper: evolution of ultrasound physics and the role of medical physicists and the AAPM and its journal in that evolution.

Authors:  Paul L Carson; Aaron Fenster
Journal:  Med Phys       Date:  2009-02       Impact factor: 4.071

2.  Creating the Medical Schools of the Future.

Authors:  Susan E Skochelak; Steven J Stack
Journal:  Acad Med       Date:  2017-01       Impact factor: 6.893

3.  Machine learning outperforms human experts in MRI pattern analysis of muscular dystrophies.

Authors:  Jasper M Morrow; Maria Pia Sormani
Journal:  Neurology       Date:  2020-02-06       Impact factor: 9.910

4.  Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.

Authors:  Milena A Gianfrancesco; Suzanne Tamang; Jinoos Yazdany; Gabriela Schmajuk
Journal:  JAMA Intern Med       Date:  2018-11-01       Impact factor: 21.873

5.  What do medical students actually need to know about artificial intelligence?

Authors:  Liam G McCoy; Sujay Nagaraj; Felipe Morgado; Vinyas Harish; Sunit Das; Leo Anthony Celi
Journal:  NPJ Digit Med       Date:  2020-06-19

Review 6.  Machine learning and medical education.

Authors:  Vijaya B Kolachalama; Priya S Garg
Journal:  NPJ Digit Med       Date:  2018-09-27

7.  Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition.

Authors:  Antoine Buetti-Dinh; Vanni Galli; Sören Bellenberg; Olga Ilie; Malte Herold; Stephan Christel; Mariia Boretska; Igor V Pivkin; Paul Wilmes; Wolfgang Sand; Mario Vera; Mark Dopson
Journal:  Biotechnol Rep (Amst)       Date:  2019-03-07

8.  The orphan child: humanities in modern medical education.

Authors:  Mary E Kollmer Horton
Journal:  Philos Ethics Humanit Med       Date:  2019-01-04       Impact factor: 2.464

9.  Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

Authors:  Julia Amann; Alessandro Blasimme; Effy Vayena; Dietmar Frey; Vince I Madai
Journal:  BMC Med Inform Decis Mak       Date:  2020-11-30       Impact factor: 2.796

10.  Introducing Artificial Intelligence Training in Medical Education.

Authors:  Ketan Paranjape; Michiel Schinkel; Rishi Nannan Panday; Josip Car; Prabath Nanayakkara
Journal:  JMIR Med Educ       Date:  2019-12-03
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