An FP’s guide to AI-enabled clinical decision support

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Applied Evidence

An FP’s guide to AI-enabled clinical decision support

J Fam Pract. 2019 November;68(9):486-488,490-492

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References

1. Lindberg DA. Internet access to National Library of Medicine. Eff Clin Pract. 2000;3:256-260.

2. National Center for Health Statistics, Centers for Disease Control and Prevention. Electronic medical records/electronic health records (EMRs/EHRs). www.cdc.gov/nchs/fastats/electronic-medical-records.htm. Updated March 31, 2017. Accessed October 1, 2019.

3. Smith C, McGuire B, Huang T, et al. The history of artificial intelligence. University of Washington. https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf. Published December 2006. Accessed October 1, 2019.

4. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA; 2016;316:2402-2410.

5. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. Cambridge, MA: Academic Press; 2019.

6. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.

7. US Food and Drug Administration. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Press release. www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye. Published April 11, 2018. Accessed October 1, 2019.

8. AI and healthcare: a giant opportunity. Forbes Web site. www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#5906c4014c68. Published February 11, 2019. Accessed October 25, 2019.

9. Boyd K. Six out of 10 people with diabetes skip a sight-saving exam. American Academy of Ophthalmology Website. https://www.aao.org/eye-health/news/sixty-percent-skip-diabetic-eye-exams. Published November 1, 2016. Accessed October 25, 2019.

10. Hornbrook MC, Goshen R, Choman E, et al. Early colorectal cancer detected by machine learning model using gender, age, and complete blood count data. Dig Dis Sci. 2017;62:2719-2727.

11. Goshen R, Choman E, Ran A, et al. Computer-assisted flagging of individuals at high risk of colorectal cancer in a large health maintenance organization using the ColonFlag test. JCO Clin Cancer Inform. 2018;2:1-8.

12. US Preventive Services Task Force. Final recommendation statement: colorectal cancer: screening. www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/colorectal-cancer-screening2#tab. Published May 2019. Accessed October 1, 2019.

13. Komorowski M, Celi LA, Badawi O, et al. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24:1716-1720.

14. Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4:e000234.

15. Banda J, Sarraju A, Abbasi F, et al. Finding missed cases of familial hypercholesterolemia in health systems using machine learning. NPJ Digit Med. 2019;2:23.

16. What is familial hypercholesterolemia? FH Foundation Web site. https://thefhfoundation.org/familial-hypercholesterolemia/what-is-familial-hypercholesterolemia. Accessed November 1, 2019.

17. Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy? Gastroenterology. 2017;153:1460-1464.E1.

Addressing challenges, tapping resources

Advances in the management of diabetic retinopathy, colorectal cancer, and sepsis are the tip of the AI iceberg. There are now ML programs to distinguish melanoma from benign nevi; to improve insulin dosing for patients with type 1 diabetes; to predict which hospital patients are most likely to end up in the intensive care unit; and to mitigate the opioid epidemic.

An ML Web page on the JAMA Network (https://sites.jamanetwork.com/machine-learning/) features a long list of published research studies, reviews, and opinion papers suggesting that the future of medicine is closely tied to innovative developments in this area. This Web page also addresses the potential use of ML in detecting lymph node metastases in breast cancer, the need to temper AI with human intelligence, the role of AI in clinical decision support, and more.

The JAMA Network also discusses a few of the challenges that still need to be overcome in developing ML tools for clinical medicine—challenges that you will want to be cognizant of as you evaluate new research in the field.

Black-box dilemma. A challenge that technologists face as they introduce new programs that have the potential to improve diagnosis, treatment, and prognosis is a phenomenon called the “black-box dilemma,” which refers to the complex data science, advanced statistics, and mathematical equations that underpin ML algorithms. These complexities make it difficult to explain the mechanism of action upon which software is based, which, in turn, makes many clinicians skeptical about its worth.

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers can improve clinical outcomes in patients with severe sepsis.

For example, the neural networks that are the backbone of the retinopathy algorithm discussed earlier might seem like voodoo science to those unfamiliar with the technology. It’s fortunate that several technology-savvy physicians have mastered these digital tools and have the teaching skills to explain them in plain-English tutorials. One such tutorial, “Understanding How Machine Learning Works,” is posted on the JAMA Network (https://sites.jamanetwork.com/machine-learning/#multimedia). A more basic explanation was included in a recent Public Broadcasting System “Nova” episode, viewable at www.youtube.com/watch?v=xS2G0oolHpo.

Continue to: Limited analysis

References

1. Lindberg DA. Internet access to National Library of Medicine. Eff Clin Pract. 2000;3:256-260.

2. National Center for Health Statistics, Centers for Disease Control and Prevention. Electronic medical records/electronic health records (EMRs/EHRs). www.cdc.gov/nchs/fastats/electronic-medical-records.htm. Updated March 31, 2017. Accessed October 1, 2019.

3. Smith C, McGuire B, Huang T, et al. The history of artificial intelligence. University of Washington. https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf. Published December 2006. Accessed October 1, 2019.

4. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA; 2016;316:2402-2410.

5. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. Cambridge, MA: Academic Press; 2019.

6. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.

7. US Food and Drug Administration. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Press release. www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye. Published April 11, 2018. Accessed October 1, 2019.

8. AI and healthcare: a giant opportunity. Forbes Web site. www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#5906c4014c68. Published February 11, 2019. Accessed October 25, 2019.

9. Boyd K. Six out of 10 people with diabetes skip a sight-saving exam. American Academy of Ophthalmology Website. https://www.aao.org/eye-health/news/sixty-percent-skip-diabetic-eye-exams. Published November 1, 2016. Accessed October 25, 2019.

10. Hornbrook MC, Goshen R, Choman E, et al. Early colorectal cancer detected by machine learning model using gender, age, and complete blood count data. Dig Dis Sci. 2017;62:2719-2727.

11. Goshen R, Choman E, Ran A, et al. Computer-assisted flagging of individuals at high risk of colorectal cancer in a large health maintenance organization using the ColonFlag test. JCO Clin Cancer Inform. 2018;2:1-8.

12. US Preventive Services Task Force. Final recommendation statement: colorectal cancer: screening. www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/colorectal-cancer-screening2#tab. Published May 2019. Accessed October 1, 2019.

13. Komorowski M, Celi LA, Badawi O, et al. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24:1716-1720.

14. Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4:e000234.

15. Banda J, Sarraju A, Abbasi F, et al. Finding missed cases of familial hypercholesterolemia in health systems using machine learning. NPJ Digit Med. 2019;2:23.

16. What is familial hypercholesterolemia? FH Foundation Web site. https://thefhfoundation.org/familial-hypercholesterolemia/what-is-familial-hypercholesterolemia. Accessed November 1, 2019.

17. Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy? Gastroenterology. 2017;153:1460-1464.E1.

An FP’s guide to AI-enabled clinical decision support

References

Addressing challenges, tapping resources

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