{"id":32877,"date":"2018-11-13T15:57:42","date_gmt":"2018-11-13T20:57:42","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-in-radiology-threat-or-opportunity\/"},"modified":"2018-11-13T15:57:42","modified_gmt":"2018-11-13T20:57:42","slug":"machine-learning-in-radiology-threat-or-opportunity","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-radiology-threat-or-opportunity\/","title":{"rendered":"Machine Learning in Radiology: Threat or Opportunity?"},"content":{"rendered":"

Prompted by recent developments in machine learning and artificial intelligence, there has been a growing national debate about automation and the future of work.[1] Few types of work have received more attention in this debate than radiology. Geoffrey Hinton, one of the leading researchers in machine learning, went so far as to say that machine learning makes \u201cit quite obvious that we should stop training radiologists.\u201d[2] On the other side of the debate, Seth Berkowitz, a notable radiologist at Beth Israel Medical Center, argued unequivocally that \u201cradiologists will not be replaced by machines.\u201d[3] So what is hype and what is reality in this debate? This is the question that the American College of Radiology, the professional medical society representing radiologists in the United States, must answer. How should radiologists respond to the threat posed by machine learning?<\/p>\n

Radiology is a frontier in the application of machine learning. This is true for several reasons. First, radiology has large, categorized datasets, making it ideal for supervised learning.[4] Second, the core task of radiology involves image classification, a demonstrated strength for machine learning.[5] And third, radiology is a massive market; 800 million radiology exams, generating 60 billion images, are conducted annually in the U.S.[6] Two landmark papers recently demonstrated the potential for machine learning algorithms to replace radiologists. In the first paper, researchers at Berkeley matched the ability of radiologists to diagnose Alzheimer Disease using PET scans.[7] In the second paper, researchers at Stanford identified pneumonia in chest x-rays more accurately than a comparison group of radiologists.[8] Results like these have led to expectations that the use of artificial intelligence in radiology is set to grow rapidly; Frost & Sullivan forecasts that the market size for applications of artificial intelligence in radiology will grow over the next five years from $150 million to $1.4 billion [Figure 1].[9]<\/p>\n

The American College of Radiology (ACR) has been increasingly focused on this topic; one recent issue of the ACR\u2019s journal was entirely dedicated to the use of machine learning in radiology.[10] The ACR\u2019s response to the threat seems primarily to be to deny that it exists. One paper summarized a recent conference co-hosted by the ACR: \u201cThe attendants grappled with the potentially disruptive applications of machine learning to image analysis. Although the prospect of algorithms\u2019 interpreting images automatically initially shakes the core of the radiology profession, the group emerged with tremendous optimism about the future of radiology.\u201d[11] Another paper published by the ACR was even more explicit \u201cPredictions have been made that suggest AI will put radiologists out of business. This issue has been overstated.\u201d[12] Even within the ACR, however, a dissenting minority takes the opposite view: \u201cThere is growing anxiety throughout the field that much of the work currently performed by radiologists will be carried out more quickly, accurately, and at lower cost by computers, soon completely replacing man with machine. We believe that talk of the impending triumph of machines in radiology is far more prescient than most radiologists suspect.\u201d[13]<\/p>\n

The reality is that neither side of this debate is correct: machine learning is neither an existential threat nor a non-issue in radiology. On the contrary, machine learning is most likely to become a complement to, rather than a substitute for, radiologists. The reason is simple; while machine learning has already proven its ability to match or exceed the performance of radiologists on some tasks, these tasks represent only a small portion of the responsibilities of a radiologist. Algorithms won\u2019t replace the human touch involved in discussing a diagnosis with a patient.[14] And because algorithms are hyper-focused, they won\u2019t be able to provide a holistic and exhaustive diagnosis.[15] This all means that \u201cthe only radiologists whose jobs may be threatened are the ones who refuse to work with AI.\u201d[16] The ACR should therefore shifting from denying the importance of machine learning to embracing it; it should actively take steps to encourage the adoption of machine learning in radiology and to improve its efficacy.<\/p>\n

Though it is clear that machine learning will significantly impact radiology in the future, several important questions remain. Because machine learning will enable radiologists to read images faster and more effectively, market forces are likely to drive adoption of machine learning applications by radiologists. But how will other stakeholders in the ecosystem respond? How should the FDA regulate an algorithm with limited precedent, particularly one that is dynamic rather than static?[17] And will consumers ever learn to trust an algorithm, even one backed to rigorous studies?[18] The stakes are far too high to let these roadblocks limit adoption. The use of machine learning in radiology will save lives.<\/p>\n

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Figure 1: Total Medical Imaging AI Market: Revenue Forecast, Global, 2015-2022 (Frost & Sullivan)<\/p>\n

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[1] Thompson, Derek, \u201cA World Without Work,\u201d The<\/em> Atlantic<\/em>, July\/August 2015, https:\/\/www.theatlantic.com\/magazine\/archive\/2015\/07\/world-without-work\/395294\/, accessed November 2018.<\/p>\n

[2] \u201cAI, Radiology and the Future of Work,\u201d The Economist<\/em>, June 7,018, https:\/\/www.economist.com\/leaders\/2018\/06\/07\/ai-radiology-and-the-future-of-work, accessed November 2018.<\/p>\n

[3] Pearson, Dave, \u201cArtificial Intelligence in Radiology: The Game-Changer on Everyone\u2019s Mind,\u201d Radiology Business<\/em>, October 13, 2017, https:\/\/www.radiologybusiness.com\/topics\/technology-management\/artificial-intelligence-radiology-game-changer-everyones-mind, accessed November 2018.<\/p>\n

[4] Walach, Elad, \u201cWhich Area of Medicine is Most Ripe for AI Disruption?\u201d, Forbes<\/em>, April 12, 2018, https:\/\/www.forbes.com\/sites\/forbestechcouncil\/2018\/04\/12\/which-area-of-medicine-is-most-ripe-for-ai-disruption\/#1729e5b92943, accessed November 2018.<\/p>\n

[5] Ibid.<\/p>\n

[6] Ip, Greg, \u201cHow Robots May Make Radiologists\u2019 Jobs Easier, Not Redundant\u201d, The Wall Street Journal<\/em>, November 22, 2017, https:\/\/www.wsj.com\/articles\/how-robots-may-make-radiologists-jobs-easier-not-redundant-1511368729, accessed November 2018.<\/p>\n

[7] Ding, Yiming, et al., \u201cA Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain,\u201d Radiology<\/em>, November 6, 2018, https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.2018180958, accessed November 2018.<\/p>\n

[8] Rajpurkar, Pranav et al., \u201cMURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs,\u201d Medical Image Analysis, <\/em>June 5, 2018, https:\/\/openreview.net\/forum?id=r1Q98pjiG, accessed November 2018.<\/p>\n

[9] \u201cGrowth Opportunities Growth Opportunities in the Global Medical Imaging Artificial Intelligence Market, Forecast to 2022,\u201d October 30, 2018, https:\/\/cds-frost-com.prd2.ezproxy-prod.hbs.edu\/p\/71319\/#!\/ppt\/c?id=MD1C-01-00-00-00&hq=global%20artificial%20intelligence%20cognitive%20computing%20healthcare, accessed November 2018.<\/p>\n

[10] \u00a0\u201cSpecial JACR Issue Focuses on Data Science and Artificial Intelligence Use in Medical Imaging,\u201d Journal of the American College of Radiology, <\/em>March 1, 2018, https:\/\/www.acr.org\/Media-Center\/ACR-News-Releases\/2018\/Special-JACR-Issue-on-Data-Science, accessed November 2018.<\/p>\n

[11] Kruskal, Jonathan et al., \u201cBig Data and Machine Learning\u2014Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference,\u201d Journal of the American College of Radiology<\/em>, June 2017, https:\/\/www.jacr.org\/article\/S1546-1440(17)30199-0\/fulltext, accessed November 2018.<\/p>\n

[12] Thrall, James et al., \u201cArtificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success,\u201d Journal of the American College of Radiology<\/em>, March 2018, https:\/\/www.jacr.org\/article\/S1546-1440(17)31671-X\/fulltext, accessed November 2018.<\/p>\n

[13] Kirk, Ray et al., \u201cThe Triumph of the Machines,\u201d Journal of the American College of Radiology<\/em>, March 2018, https:\/\/www.jacr.org\/article\/S1546-1440(17)31175-4\/fulltext, accessed November 2018.<\/p>\n

[14] Pearson, Dave, \u201cArtificial Intelligence in Radiology: The Game-Changer on Everyone\u2019s Mind,\u201d Radiology Business<\/em>, October 13, 2017, https:\/\/www.radiologybusiness.com\/topics\/technology-management\/artificial-intelligence-radiology-game-changer-everyones-mind, accessed November 2018.<\/p>\n

[15] Mukherjee, Siddhartha, \u201cA.I. versus M.D.\u201d, The New Yorker<\/em>, April 3, 2017, https:\/\/www.newyorker.com\/magazine\/2017\/04\/03\/ai-versus-md, accessed November 2018.<\/p>\n

[16] Davenport, Thomas and Keith Dreyer, \u201cAI Will Change Radiology but it Won\u2019t Replace Radiologists,\u201d 性视界 Business Review, <\/em>March 27, 2018, https:\/\/hbr.org\/2018\/03\/ai-will-change-radiology-but-it-wont-replace-radiologists, accessed November 2018.<\/p>\n

[17] Kohli, Marc et al., \u201cImplementing Machine Learning in Radiology Practice and Research,\u201d American Journal of Roentgenology, <\/em>April 2017, https:\/\/www.ajronline.org\/doi\/abs\/10.2214\/AJR.16.17224, accessed November 2018.<\/p>\n

[18] \u201cAccenture 2018 Consumer Survey on Digital Health,\u201d Accenture<\/em>, 2018, https:\/\/www.accenture.com\/us-en\/insight-new-2018-consumer-survey-digital-health, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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