Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

• Published in: Health systems Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 9
• Main Authors: Hersh, William R., Hoyt, Robert E., Chamberlin, Steven, Ancker, Jessica S., Gupta, Aditi, Borlawsky-Payne, Tara B.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.07.2023
• Subjects: artificial intelligence; biomedical research; clinicians; Data science; health and clinical informatics; machine learning; Titan Paper
• ISSN: 2047-6965; 2047-6973
• DOI: 10.1080/20476965.2023.2237745

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.