Abstract
Justified by spectacular achievements facilitated through applied deep learning methodology (based on neural networks), the “Everything is possible” view dominates this new hour in the “boom and bust” curve of AI performance. The optimistic view collides head on with the “It is not possible”—ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI—now or at some moment in the future? AI or not, medicine should incorporate the anticipation perspective in providing care.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bayes T (1763) An essay towards solving a problem in the doctrine of chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S”. Philos Trans R Soc Lond 53:370–418
Decety J, Jackson P (2006) A social-neuroscience perspective on empathy. Curr Direct Psychol Sci 15:54–58
Elia F, Apra F (2019) Walking away from conveyor-belt medicine. N Engl J Med 380(1):8–9
Gallese V, Fadiga L et al (1996) Action recognition in the premotor cortex. Brain 119(2):593–609
Goldhahn J, Rampton V, Spinas GA (2018) Could artificial intelligence make doctors obsolete? Br J Med 363:4563
Hamill, LV (2019) Lisa V. Hamill MD@dr_hamill 8 May 2019
Hinton G (2018) The deep learning revolution, ACM Turing Award Lecture Video. Retrieved from https://amturing.acm.org/vp/hinton_4791679.cfm
Hinton G (2019) Geoffrey Hinton says machines can do anything humans can. Google I/O 2019 AI Conference, Mountain View, CA. (Hinton in a dialog with Nicholas Thompson, Editor of Wired). Retrieved from https://syncedreview.com/2019/05/10/google-i-o-2019-geoffrey-hinton-says-machines-can-do-anything-humans-can/
Illich I (1974) Medical nemesis: the expropriation of health (Open Forum/Ideas in Progress Series) New York: Knopf Doubleday Publishing Group
Jabbi M, Keysers C (2008) Inferior frontal gyrus activity triggers anterior insula response to emotional facial expressions. Emotion 8(6):775–780
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS’12 Proceedings of the 25th international conference on neural information processing systems, vol 1, pp 1097–1105
Kulikowski CA (2019) Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art—with reflections on present AIM challenges, yearbook of medical informatics. Stuttgart: IMIA and Georg Thieme Verlag KG. https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0039-1677895.pdf
Laplace P-S (1814) Essai philosophique sur les probabilités Paris: Imprimeur-Libraire pour Mathématiques (1814). Retrieved from: https://books.google.com/books?id=rDUJAAAAIAAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
Legendre A-M (1805) Appendice sur la méthodes des moindres quarrés, Appendix (p.72–80) to Nouvelles méthodes pour la détermination des orbites des comètes. Paris: Firmin-Didot
Mandrola J (2019) A contrarian view of digital health, Quillette. May 17. Retrieved from https://quillette.com/2019/05/17/a-contrarian-view-of-digital-health/
Markov A (1906). “Pacпpocтpaнeниe зaкoнa бoльшиx чиceл нa вeличины, зaвиcящиe дpyг oт дpyгa”. “Извecтия Физикo-мaтeмaтичecкoгo oбщecтвa пpи Кaзaнcкoм yнивepcитeтe”, 2-я cepия, тoм 15, cт. 135–156. See also: Extension of the limit theorems of probability theory to a sum of variables connected in a chain. Reprinted in Howard R (1971). Dynamic Probabilistic Systems, Vol. 1: Markov Chains. (Appendix B). John Wiley and Sons, New York
Mirsky Y, Mahler T, Shelef I, Elovici Y (2019) CT-GAN: malicious tampering of 3D medical imagery using deep learning. ArXiv, June 6. Retrieved from https://arxiv.org/abs/1901.03597
Muse ED, Topol EJ (2019) Digital orthodoxy of human data collection. Lancet 394(10198):556
Nadin M (2014) G-complexity, quantum computation and anticipatory processes (Academic Research Center of Canada). Comput Commun Collab 2(1):16–34
Nadin M (2017) Medicine: the decisive test of anticipation: anticipation and medicine. Springer International Publishers, Cham, pp 1–27
Nadin M (2018a) Machine intelligence—a chimera. Springer, AI & Society, London, pp 1–28
Nadin M (2018b) Redefining medicine from an anticipatory perspective. Prog Biophys Mol Biol. https://doi.org/10.1016/j.pbiomolbio.2018.04.003
Neumann M, Edelhauser F, Tauschel D, Fischer MR, Wirtz M, Woopen C, Haramati A, Scheffer C (2011) Empathy decline and its reasons: a systematic review of studies with medical students and residents. Acad Med 86(8):996–1009
Pisanelli DM (2004) Ontologies in medicine. IOS Press, Amsterdam
Preston S, de Waal F (2002) Empathy: its ultimate and proximate bases. Behav Brain Sci 25:1–72
Rizzolati G, Craighero L (2004) The mirror neuron system. Annu Rev Neurosci 27:169–192
Shannon C (1938) A symbolic analysis of relay and switching circuits. Electr Eng 57:713–723
Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423
Sim I (2019) Mobile devices and health. N Engl J Med 381:956–968. https://doi.org/10.1056/NEJMra1806949
Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York
Turing A (1950) Computing machinery and intelligence. Mind 59:433–460
Weng SF, Vaz L, Qureshi N, Kai J (2019) Prediction of premature all-cause mortality: a prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS One 14(3):e0214365. https://doi.org/10.1371/journal.pone.0214365
Windelband, W (1894) Geschicthte und Naturwissenschaft. Präludien. Aufsätze und Reden zur Philosophie und ihrer Geschichte. Tubingen: J.C.B. Mohr, 136–160. (History and Natural Science. Speech of the Rector of the University of Strassburg)
Acknowledgements
Many practicing physicians, to whom I wish to express gratitude for their tolerance of someone who questioned their profession, educated me without turning me into a practicing doctor. They read this paper and offered very serious feedback. No agreement was actually reached on any of the major value judgments made in this paper regarding the danger of a new theology of medicine. In particular, I wish to express gratitude to Jean-Paul Pianta, Matthew Goldberg, Thomas O. Staiger, and Oleg Kubryak. The Ontolog-Forum, in particular through Azamat Abdoullaev and John F. Sowa, educated me in matters of medical ontology. Funding for the research on which this paper is based was provided by the antÉ-Institute for Research in Anticipatory Systems. Dr. Asma Naz was, as usual, prepared to help me make ideas in this text more accessible to a larger audience. Elvira Nadin could claim at least co-authorship by challenging almost every hypothesis herewith formulated and eventually tested in our lab. One reviewer, who identified himself, received the following message from me: Of course, I would respect you less were I not convinced of your integrity. Confirmed again. I thank you deeply for your review. The luxury of having you provide a competent reading cannot be redeemed. But I am grateful. A lot to think about, a lot to learn. The reviewer was Terry Winograd—who, as I learned, chaired the launch of the AI & Society journal in USA in 1986, during the Computers for Social Responsibility workshop held in Seattle.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nadin, M. Aiming AI at a moving target: health (or disease). AI & Soc 35, 841–849 (2020). https://doi.org/10.1007/s00146-020-00943-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-020-00943-x