Teaching Artificial Intelligence Literacy: A Challenge in the Education of Radiology Residents

Abstract

The growth of cross-sectional imaging (particularly MR and CT) has contributed significantly to increase the workload of radiologists, particularly due to larger datasets to interpret in a shorter amount of time ( 1 Kwee T.C. Kwee R.M. Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and the role of artificial intelligence. Insights Imaging. 2021; 12: 88https://doi.org/10.1186/s13244-021-01031-4 Google Scholar ). In one institution, the number of cross-sectional images increased tenfold between 1990 and 2010 ( 2 McDonald R.J. Schwartz K.M. Eckel L.J. et al. The effects of change in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol. 2015; 22: 1191-1198https://doi.org/10.1016/j.acra.2015.05.007 Google Scholar ). This has led to increased intra- and inter-observer variability, burnout and diagnostic errors. Against this backdrop, Artificial Intelligence (AI) may turn out to be an attractive partner, one that can help in both interpretive and non-interpretive tasks in radiology ( 3 Mello-Thoms C. Mello C.A.B. Clinical applications of artificial intelligence in radiology. Br J Radiol. 2023; https://doi.org/10.1259/bjr.20221031 Google Scholar ). Unfortunately, AI education has not reached many practicing radiologists, and without specialized knowledge, they cannot either understand or explain, to both medical students and radiology residents, how radiology is going to incorporate AI ( 4 Grayev A. Artificial intelligence in radiology: resident recruitment help or hindrance. Acad Radiol. 2019; 26: 699-700https://doi.org/10.1016/j.acra.2019.01.005 Google Scholar ), which propagates the fears in the student population that AI will take over radiologists’ jobs. In this context, AI training could be a protective factor against the disincentive effect of AI on future radiologists ( 5 Gorospe-Sarasúa L. Muñoz-Olmedo J.M. Sendra-Portero F. et al. Challenges in radiology education in the era of artificial intelligence. Radiología. 2022; 64: 54-59https://doi.org/10.1016/j.rxeng.2020.10.012 Google Scholar ), as it has been shown that fear of AI is directly linked to reduced knowledge about it ( 6 Huisman M. Ranschaert E. Parker W. et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol. 2021; 31: 7058-7066https://doi.org/10.1007/s00330-021-07781-5 Google Scholar ).