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Ken Masters (2019) Artificial intelligence in medical education, Medical Teacher, DOI: 10.1080/0142159X.2019.1595557
"Medical Educator Roles for the Future", focusing on how Technology, Machine Learning, AI and the like will impact the way we educate healthcare professionals and the way we provide care.
IAMSE20 Denver, USA
Medical Educator Roles of the Future
This session will explore how near future technology can impact how we educate healthcare professionals and the way they provide care.
The idea is to examine how “new” methods and platforms for displaying information, engaging an audience, extending and expanding the cognitive presence of “the instructor”, and increasingly "guide" will transform the learning experience, and training outcomes, of our educational efforts; and also explore how these same technologies, which will include Artificial Intelligence (AI) and Machine Learning, Virtual Reality (VR) and Augmented Reality (AR), online and re-imagined out-of-the-simulation-center skill training experiences (inspired and modelled after gaming platforms), can augment, enhance, and transform how we educate and train healthcare professionals, along the whole continuum of learning, from undergraduate learning, through postgraduate training, to lifelong learning and continuing professional development settings.
Associate Professor and Senior Consultant, Department of Diagnostic Radiology, National University of Singapore and National University Hospital and Associate Member, Centre for Medical Education, NUS
Poh-Sun (MBBS(Melb) 1987, FRCR 1993, FAMS 1998, MHPE(Maastricht) 2012 and FAMEE 2017) practices on the clinician educator tract (80/20 time allocation clinical/education) augmenting his education and training time allocation with technology, and regular cumulative early morning focused scholarly efforts, spent developing and evaluating the use of open access online digital repositories in clinical training, and medical education faculty development, under a mastery training and deliberate practice framework. He focuses his efforts on the challenge of transfer to practice, in the widest possible settings, through use of reusable comprehensive digital content, iterative low cost proof of concept implementation combined with collaborations and partnerships to scale, all anchored on a solid foundation of theory and evidence.
(including links to use of AI)
Goh, P.S. Presenting the outline of a proposal for a 5 part program of medical education research using eLearning or Technology enhanced learning to support Learning through the continuum of Undergraduate, through Postgraduate to Lifelong learning settings. MedEdPublish. 2016 Oct; 5(3), Paper No:55. Epub 2016 Dec 7.
Samarasekera DD, Goh PS, Lee SS, Gwee MCE. The clarion call for a third wave
in medical education to optimise healthcare in the twenty-first century. Med
Teach. 2018 Oct;40(10):982-985. doi: 10.1080/0142159X.2018.1500973. Epub 2018 Oct
Andreas Maier, Christopher Syben, Tobias Lasser, Christian Riess, A gentle introduction to deep learning in medical image processing, Zeitschrift für Medizinische Physik, Volume 29, Issue 2, 2019, Pages 86-101, ISSN 0939-3889, https://doi.org/10.1016/j.zemedi.2018.12.003.
Lakhani, P., Gray, D. L., Pett, C. R., Nagy, P., & Shih, G. (2018). Hello World Deep Learning in Medical Imaging. Journal of digital imaging, 31(3), 283–289. doi:10.1007/s10278-018-0079-6