The Price of Artificial Intelligence
25 April 2019 (online)
Introduction: Whilst general artificial intelligence (AI) is yet to appear, today’s narrow AI is already good enough to transform much of healthcare over the next two decades.
Objective: There is much discussion of the potential benefits of AI in healthcare and this paper reviews the cost that may need to be paid for these benefits, including changes in the way healthcare is practiced, patients are engaged, medical records are created, and work is reimbursed.
Results: Whilst AI will be applied to classic pattern recognition tasks like diagnosis or treatment recommendation, it is likely to be as disruptive to clinical work as it is to care delivery. Digital scribe systems that use AI to automatically create electronic health records promise great efficiency for clinicians but may lead to potentially very different types of clinical records and workflows. In disciplines like radiology, AI is likely to see image interpretation become an automated process with diminishing human engagement. Primary care is also being disrupted by AI-enabled services that automate triage, along with services such as telemedical consultations. This altered future may necessarily see an economic change where clinicians are increasingly reimbursed for value, and AI is reimbursed at a much lower cost for volume.
Conclusion: AI is likely to be associated with some of the biggest changes we will see in healthcare in our lifetime. To fully engage with this change brings promise of the greatest reward. To not engage is to pay the highest price.
- 1 Roy Amara 1925–2007, American futurologist. In: Ratcliffe S. editor. Oxford Essential Quotations. 4th ed; 2016
- 2 Schwartz WB. Medicine and the Computer. The Promise and Problems of Change. N Engl J Med 1970; 283 (23) 1257-64
- 3 Kay P, Kempton W. What is the Sapir-Whorf hypothesis?. Am Anthropol 1984; 86 (01) 65-79
- 4 Coiera E, Kocaballi B, Halamka J, Laranjo L. The digital scribe. npj Digital Medicine. 2018 1. (58) doi:10.1038/s41746-018-0066-9
- 5 Lyell D, Coiera E. Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc 2017; 24 (02) 423-31
- 6 Siegler EL, Adelman R. Copy and paste: a remediable hazard of electronic health records. Am J Med 2009; Jun; 122 (06) 495-96
- 7 Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; Dec; 42: 60-88
- 8 Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA 2016; 315 (06) 551-2
- 9 Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. New Engl J Med 2017; 376 (26) 2507-09
- 10 Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med 2018; Nov 6 15 (11) e1002683
- 11 Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010; 22 (10) 1345-59
- 12 McCartney M. General practice can’t just exclude sick people. BMJ 2017; 359: j5190
- 13 Fraser H, Coiera E, Wong D. Safety of patient-facing digital symptom checkers. Lancet 2018; Nov 24 392 (10161) 2263-4
- 14 Marshall M, Shah R, Stokes-Lampard H. Online consulting in general practice: making the move from disruptive innovation to mainstream service. BMJ 2018; Mar 26 360: k1195
- 15 Coiera E. The fate of medicine in the time of AI. Lancet 2018; 392 (10162) 2331-2