Summary: Artificial intelligence (AI) is rapidly impacting on the practice of medicine, and will shortly impact on medical education; we need to position ourselves so that we can utilise it properly for effective medical education and the delivery of healthcare. Description: From Descartes’ Automata (Descartes, 1637) to Babbage’s Analytical Engine, which could only perform “whatever we know how to order it to perform” (Menabrea, 1843) through to Alan Turing’s question of “Can machines think?” (Turing, 1950), humans have been fascinated by the idea of the intelligent machine.

In the 21st century, Artificial Intelligence (AI) is in its infancy, and medicine has already benefited from some of the work in this field, as AI systems perform many medical tasks far faster and/or more accurately than humans (Boguševičius et al., 2002; Esteva et al., 2017; Litjens et al., 2017; Mobadersany et al., 2017; Betancur et al., 2018; De Fauw et al., 2018; Haenssle et al., 2018).

There have also been embarrassing AI failures, but mostly these failure point to a failure to teach properly, and this is where good medical educators and good medical education is crucial. Although teachers will look to AI to lighten some of their drudgery, the most important impact of AI on medical education will be the need to educate medical students for the new roles that will be opened in medicine by AI. These will include being proactive in the design of systems, working properly with AI diagnostic systems, communication with and through AI systems, and dealing with patients whose health and well-being will be changed by AI..

Medical schools who fail to be proactive and start teaching their students now, will have medical doctors totally unprepared for medical service within 10 years’ time.

If you would like to know little more about AI in medical education, see the newly-published AMEE Guide to Artificial Intelligence in medical education at:


Betancur, J., Commandeur, F., Motlagh, M., Sharir, T., et al. (2018) ‘Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT. A Multicenter Study’, JACC: Cardiovascular Imaging, 11(11), pp. 1654–1663.

Boguševičius, A., Maleckas, A., Pundzius, J. and Skaudickas, D. (2002) ‘Prospective randomised trial of computer-aided diagnosis and contrast radiography in acute small bowel obstruction’, European Journal of Surgery, 168(2), pp. 78–83.

Descartes (1637) ‘Discourse on the method’, Descartes. Key philosophical writings. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., et al. (2017) ‘Dermatologist-level classification of skin cancer with deep neural networks’, Nature. Nature Publishing Group, 542(7639), pp. 115–118.

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., et al. (2018) ‘Clinically applicable deep learning for diagnosis and referral in retinal disease’, Nature Medicine.

Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., et al. (2018) ‘Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists’, Annals of Oncology, 29(8), pp. 1836–1842.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., et al. (2017) ‘A survey on deep learning in medical image analysis’, Medical Image Analysis. Elsevier B.V., 42(December), pp. 60–88.

Menabrea, L. (1843) Sketch of The Analytical Engine invented by Charles Babbage. London: Richard & John Taylor. Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., et al. (2017) ‘Predicting cancer outcomes from histology and genomics using convolutional networks’, bioRxiv, p. 198010.

Turing, A. (1950) ‘Computing machinery and intelligence’, Mind, 59(236), pp. 433–460.