Abstract
Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting “alert fatigue” and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of “big data” in health care provide the potential not only to perform “in silico” research but also to provide “real time” diagnostic and (potentially) therapeutic recommendations based on empirical data. “On demand” access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.
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References
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• Lee CS, Nagy PG, Weaver SJ, Newman-Token DE. Cognitive and system factors contributing to diagnostic errors in radiology. Am J Roentgenol. 2013;201:611–7. This article provides an excellent overview of the types of diagnostic errors made by radiologists in the interpretation of imaging studies including common biases.
Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med. 2005;165:1493–9.
Weingart SN, Wilson RM, Gibbard RW, Harrison B. Epidemiology of medical error. West J Med. 2000;172:390–3.
Khosla V. “20 % doctor included”: speculations and musings of a technology optimist. Available at: http://www.khoslaventures.com/wp-content/uploads/2012/12/20_percent_doctor_included.doc. Accessed October 14, 2013.
Winters B, Custer J, Calvagno Jr SM, et al. Diagnostic errors in the intensive care unit: a systematic review of autopsy studies. BMJ Qual Saf. 2012;21:894–902.
Smith SS, Morris W, Voorheis RW. The new international Webster’s comprehensive dictionary of the English language: encyclopedic edition. Naples: Trident Press International; 1998.
•• Patel VL, Shortliffe EH, Stefanelli M, et al. The coming age of artificial intelligence in medicine. Artif Intell Med. 2009;46:5–17. This paper provides an overview of a panel discussion at the Artificial Intelligence in Medicine Europe conference in July 2007 focusing on predictions of the coming of age of AI in the 1990’s that did not materialize and research and experience during the subsequent years. Topics included clinical decision support, dealing with uncertainty, systems integration, and modeling of expertise.
Pearson T. IBM Watson. How to build your own “Watson Jr.” in your basement. IBM Developer Works blog. February 18, 2011. Available at: https://www.ibm.com/developerworks/mydeveloperworks/blogs/InsideSystemStorage/entry/ibm_watson_how_to_build_your_own_watson_jr_in_your_basement7?lang=en. Accessed September 30, 2013.
Dugdale DC, Epstein R, Pantilat SZ. Time and the patient-physician relationship. J Gen Intern Med. 1999;14 Suppl 1:S34–40.
Fornell D. An introduction to clinical decision support for cardiology. Diagn Intervent Cardiol. 2013. Available at: www.dicardiology.com/article/introduction-clinical-decision-support-cardiology. Accessed September 29, 2013.
Saifi S, Taylor AJ, Allen H, Hendel R. The use of a learning community and online evaluation of utilization for SPECT myocardial perfusion imaging. J Am Coll Cardiol Imaging. 2013;6:823–9.
DePuey GE, Garcia EV, Ezquerra NF. Three-dimensional techniques and artificial intelligence in thallium-201 cardiac imaging cardiac imaging. Am J Roentgenol. 1989;152:1161–8.
• Itchhaporia D, Snow PB, Almassy RJ, Oetgen WJ. Artificial neural networks: current status in cardiovascular medicine. J Am Coll Cardiol. 1996;28:515–21. The authors define artificial neural networks as a way to find unexpected relations in data sets based on input from previous data and describe research performed in the 1980’s and 1990’s in cardiovascular medical research.
Clayton RH, Murray A, Campbell RWF. Recognition of ventricular fibrillation using neural networks. Med Biol Eng Comput. 1994;32:217–20.
DeGroff CG, Bhatikar S, Hertzberg J, Shandas R, Valdes-Cruz L, Mahajan RL. Artificial neural network–based method of screening heart murmurs in children. Circulation. 2001;103:2711–6.
Dilsizian V, Taillefer R. Journey in evolution of nuclear cardiology: will there be another quantum leap with the F-18 labeled myocardial perfusion tracers? JACC Cardiovasc Imaging. 2012;5:1269–84.
Artificial Intelligence in Medicine. (n.d.). Retrieved September 22, 2013, from Cedars Sinai: http://www.cedars-sinai.edu/Patients/Programs-and-Services/Medicine-Department/Artificial-Intelligence-in-Medicine-AIM/.
Dilsizian V, Bacharach SL, Beanlands RS, et al. ASNC imaging guidelines for nuclear cardiology procedures: PET myocardial perfusion and metabolism clinical imaging. J Nucl Cardiol. 2009;16: published online as supplementary material. Available at: http://link.springer.com/content/esm/art:10.1007/s12350-009-9094-9/file/MediaObjects/12350_2009_9094_MOESM1_ESM.pdf. Accessed September 30, 2013.
Levit L, Balogh E, Nass S, Ganz PA, editors. Delivering high-quality cancer care: charting a new course for a system in crisis. Institute of Medicine. Washington, DC: Institute of Medicine; 2013.
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The authors wish to acknowledge and thank Nancy Knight for her tremendous assistance in the editing of this manuscript and Stephen Siegel for his assistance with the graphics.
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Steven E. Dilsizian declares that he has no conflict of interest. Eliot L. Siegel has received PI funding for a grant from IBM to help bring the Jeopardy! Deep Q/A software to the medical domain.
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This article does not contain any studies with human or animal subjects performed by any of the authors.
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This article is part of the Topical Collection on Nuclear Cardiology
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Dilsizian, S.E., Siegel, E.L. Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment. Curr Cardiol Rep 16, 441 (2014). https://doi.org/10.1007/s11886-013-0441-8
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DOI: https://doi.org/10.1007/s11886-013-0441-8