Eric Topol, a Professor of Genomics and Cardiologist, in his 2015 NYTimes bestseller 'The Patient Will See You Now', foresees the demise of the current form of hospital based acute care delivery (with a shift to delivery of care at homes of patients) along with replacement of human delivered clinical services by smart systems/devices in the coming future. I would not go so far as Professor Topol in his assessment about the replacement of human clinicians but can easily foresee the automation of a large part of human led clinical care in the coming decades.
While I do not remotely purport to be an expert on automated medical systems (that will take some more time and neurons); having undertook months of research to complete a book chapter on the use of Artificial Intelligence in healthcare (book working title: E-Health, ISBN 978-953-51-6136-3; Editor: Thomas.F.Heston, MD), just completed a hands-on AI programming course delivered by Microsoft while undertaking a scoping review of the use of recurrent neural networks in medical diagnosis and preparing the use case of a non-knowledge based clinical decision support system, I can rationally state where the automation of healthcare delivery is heading to.
Before I analyse the 'artificial intelligence take-over of medical care' scenario further, I would like to here express my slight distaste for the term 'Artificial Intelligence'. This term derives from the incorrect assumption intelligence has been primarily a biological construct so far and any intelligence that is now being derived outside the biological domain is artificial. In other words, intelligence is framed exclusively in reference to biologically derived intelligence. However, intelligence is a profound entity of it's own with defined characteristics such as learning and reasoning. Human intelligence is not the most intelligence can be and as trends go, computing programs with their increasingly advanced algorithms can potentially in the next decade or so exceed the learning and reasoning powers of humans in some aspects. Therefore, a source based terminology for intelligence would be more appropriate to frame intelligence. In other words, computational intelligence would be more appropriate than artificial intelligence.
Healthcare has been a fertile domain for computational intelligence (CI) researchers to apply CI techniques including artificial neural networks, evolutionary computing, expert systems and natural language processing. The rise in interest and investment in CI research has coincided with the increasing release of CI driven clinical applications. Many of these applications have automated the three key cornerstones of medical care: diagnosis, prognosis and therapy. So it is not hard to see why commentators, including clinical commentators, are predicting the replacement of human clinicians by CI systems. While it is indeed rational, based on current trends in CI research, to imagine automation of many human clinician (for convenience sake, I am focusing on physicians rather than other professions such as nurses, allied health professionals) led tasks including interpretation of laboratory and imaging results (CI applications are already matching the radiologist's accuracy in interpretation of MRI, CT and Radiological images), predicting clinical outcomes (CI applications have successfully predicted acute conditions by reviewing both structured and unstructured patient data) and diagnosing various acute conditions (in fact the earliest CI applications, dating back to the 70's, already had this ability); it is hard to imagine CI systems completely replacing human clinicians in conveying diagnosis and discussing complex treatment regimens with patients, especially with high risk patients. There are some other areas where it is equally hard to foresee automation of clinical tasks such as some complex procedures and making final treatment decisions. So what is to come?
I foresee a co-habitation model. A model that accepts the inevitable automation of a significant number of tasks that are currently performed by human clinicians (the at-risk areas are where there is less human interaction and where there is a structured process in implementing the task; structure means algorithms can be developed easier you see) but allows for human clinicians to make the final decision and be the lead communicator with patients.
E. Topol, The Patient Will See You Now: The Future of Medicine is in Your Hands. New York: Basic Books, 2015.
J. N. Kok, E. J. W. Boers, W. A. Kosters, P. Van Der Putten, and M. Poel, “Artificial Intelligence: Definition, Trends, Techniques, and Cases,” 2013.
D. L. Poole and A. K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, 2nd Edition. Cambridge University Press, 2017.
B. Milovic and M. Milovic, “Prediction and Decision Making in Health Care using Data Mining,” Int. J. Public Heal. Sci., vol. 1, no. 2, pp. 69–76, 2012.
K. L. Priddy and P. E. Keller, Artificial Neural Networks: An Introduction. Bellingham: SPIE Press, 2005.
S. C. Shapiro, Encyclopedia Of Artificial Intelligence, 2nd Editio. New York: Wiley-Interscience, 1992.
R. Scott, “Artificial intelligence: its use in medical diagnosis.,” J. Nucl. Med., vol. 34, no. 3, pp. 510–4, 1993.
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