Evaluating AI Clinically—It’s Not Just ROC AUC!

HomeRadiologyVol. 298, No. 1 PreviousNext Reviews And CommentaryFree AccessEditorialEvaluating AI Clinically—It’s Not Just ROC AUC!Elizabeth A. Krupinski Elizabeth A. Krupinski Author AffiliationsFrom the Department of Radiology and Imaging Sciences, Emory University 1364 Clifton Rd NE, Atlanta, GA 30322.Address correspondence to the author (e-mail: [email protected]).Elizabeth A. Krupinski Published Online:Oct 20 2020https://doi.org/10.1148/radiol.2020203782MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Jiang et al in this issue.Dr Krupinski is a professor and vice chair for research at Emory University in the Departments of Radiology and Imaging Sciences, Psychology and Medical Informatics. She holds degrees from Cornell, Montclair State, and Temple, all in experimental psychology. Her interests are in medical image perception, observer performance, medical decision making, and human factors.Download as PowerPointOpen in Image Viewer George Eliot once said, “History repeats itself,” but I think a twist on it attributed to Mark Twain is usually more on the mark: “The past does not repeat itself, but it often rhymes.” Research on the development, implementation, and clinical evaluation of computer-aided detection and subsequent computer-aided diagnosis schemes actually goes back further in time than most people realize—mid-1960s for computer-aided detection and 1970s for computer-aided diagnosis (1). Machine learning, deep learning, and artificial intelligence (AI), as extensions of computer-aided detection and diagnosis, have the same roots in the 1970s, although their “sudden” appearance in medical imaging did not occur until about 10 years ago. Since then, interest in development and clinical implementation has skyrocketed. The types of images, modalities, and tasks have dramatically changed compared with computer-aided diagnosis or detection applications to include applications across all aspects of the imaging enterprise from classic image analysis to report mining, billing tools, workflow management, and a host of other areas too numerous to list.What has not changed is the critical need to evaluate these tools and decision aids to validate their use in the clinical setting, where patient care and outcomes are the ultimate indicators of success. The article by Jiang et al in this issue of Radiology, “Artificial Intelligence Applied to Breast MRI for Improved Diagnosis” (2), is a good example of the rhythm of history. It nicely incorporates some of the classic evaluation metrics used historically to evaluate computer-based decision aids, adds in a few that I am always looking for, and also sets the stage for future evaluations that will be especially critical as we try to integrate many of these AI-based tools into routine practice and administration of the radiology enterprise.Key strengths of the study included the use of a large sample of readers from different practice settings. All readers met minimum experience qualifications, and they had a range of years of overall and breast imaging experience. In addition, the test set had a good mix of lesion types varying in diameter and volume (and thus presumably subtlety), and the statistically powerful multireader multicase receiver operating characteristic study design and analysis was used (3). Naturally, the area under the receiver operating characteristic curve served as the primary metric to assess performance (finding a significant increase in performance using the AI output vs not using it).What I really appreciated and what went beyond just repeating history and reporting the standard area under the receiver operating characteristic curve and related metrics, are Figures 4 and 5. Simply looking at average performance changes ignores the fact that readers’ demographic differences will always lead to differences in individual performance. Each user of a given tool or decision aid incorporates the provided information into his or her decision-making process, and that information affects his or her decisions compared with not using the tool. Figure 4 (comparing sensitivity and specificity changes with and without AI) nicely demonstrates that although most readers in the study experienced an increase in performance with AI, some stayed the same and some did worse. It is hard to imagine that this will not always be the case no matter what tool is used. Figure 5 shows another thing I like to see—the frequency of decision changes as a function of the AI intervention. AI can help turn false-negative findings into true-positive findings and false-positive findings into true-negative findings, but the opposite is true as well. Good decisions can turn into bad ones.So what is missing? What more do we need to evaluate in order to facilitate the implementation of AI in the radiology enterprise? Where do we need history to rhyme rather than repeat itself? Although some computer-aided diagnosis and detection and AI studies measure viewing and/or decision time, all observer studies should do this. A key claim of many of these proposed tools is that they can improve user efficiency, but without data such claims cannot be verified. Simply measuring viewing time is straightforward, but what would really take things to the next level with respect to efficiency is investigation of a related issue—fatigue. One of the potential key benefits of AI and related tools could be a reduction in radiologists’ fatigue levels (4). This remains a fertile area for research (5) perhaps because we need to explore and adopt models and research methods from other fields such as psychology and human factors.Principles and methods from psychology, in particular image perception, can take AI evaluation in new directions in some other important areas. As already noted, user demographics of all types impact whether and how these tools will affect decisions. To reduce observer variability, however, we need to dig deeper and to discover which demographics or user characteristics are most important. Once we figure that out, we may be able to determine how to improve the ratio of good to bad decision changes and thereby the number of readers whose performance improves with AI versus the number of readers whose performance does not improve or stays the same. Some related perceptual issues that require further research include determining the best way to visually (aurally?) present the AI output to the user. Are heat maps better than numeric probabilities? Are arrows better than bounding boxes? Should we present the output when the image appears or should we require the reader to search and develop his or her own first impression before accessing the output? These are but a few examples, and I would like to be able to say they are not a repeat of history, but to be honest, they have all been raised and studied in the past with computer-aided diagnosis and detection and other decision aids—with mixed results. Cognitive biases (eg, anchoring, confirmation, hindsight) and their impact have been noted as important considerations when using decision aids (6) but have yet to be systematically studied. This could be a fruitful area of future investigation. Assessing the impact of AI and other decision aids in “real life” is also critical. Retrospective studies like the one by Jiang et al (2) are required but fail to capture the complexity of daily image interpretation and reveal nothing about long-term consequences of these decision aids in terms of affecting patient care, timeline, and outcomes.Finally, we need to consider all other application areas and processes in radiology that AI is targeting, for example, report analysis and information extraction for better understanding and use by other providers, billing specialists, and patients; better positioning and dosing of patients during image acquisition; and more efficient use of imaging suites. These areas would benefit from evaluations guided by models and methods from implementation sciences, quality improvement, health services research, and financial sciences. The future of AI in radiology is bright, but if history has taught us nothing else, it is that innovations will always happen and we will always need to evaluate new ideas and innovations from a wide variety of perspectives using methods and theories from many fields to rhyme instead of repeat.Disclosure of Conflicts of Interest: E.A.K. disclosed no relevant relationships.References1. Giger ML. Machine learning in medical imaging. J Am Coll Radiol 2018;15(3 Pt B):512–520. Crossref, Medline, Google Scholar2. Jiang Y, Edwards AV, Newstead G. Artificial intelligence applied to breast MRI for improved diagnosis. Radiology 2021;298:38–46. Link, Google Scholar3. Dorfman DD, Berbaum KS, Metz CE. Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. Invest Radiol 1992;27(9):723–731. Crossref, Medline, Google Scholar4. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018;15(3 Pt B):504–508. Crossref, Medline, Google Scholar5. Taylor-Phillips S, Stinton C. Fatigue in radiology: a fertile area for future research. Br J Radiol 2019;92(1099):20190043. Crossref, Medline, Google Scholar6. Driver CN, Bowles BS, Bartholmai BJ, Greenberg-Worisek AJ. Artificial intelligence in radiology: a call for thoughtful application. Clin Transl Sci 2020;13(2):216–218. Crossref, Medline, Google ScholarArticle HistoryReceived: Sept 16 2020Revision requested: Sept 28 2020Revision received: Sept 28 2020Accepted: Sept 29 2020Published online: Oct 20 2020Published in print: Jan 2021 FiguresReferencesRelatedDetailsCited ByCan incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiographyMichael H.Bernstein, Michael K.Atalay, Elizabeth H.Dibble, Aaron W. 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