The integration of laboratory and radiology data: A holistic approach to patient diagnostics
Keywords:
Artificial intelligence, Radiology, Medical imaging, Machine learning, Deep learningAbstract
Background _ Artificial intelligence (AI), particularly deep learning, has the potential to significantly transform the field of radiology. While there has been considerable attention in popular literature on the possible applications of AI, such as autonomous vehicles, it is suggested that the healthcare sector would experience the earliest and most significant impact from AI. Aim of Work – This paper aims to provide an overview of the current state of AI in radiology, including its potential benefits and challenges, and to discuss the ethical, technical, and practical considerations for its implementation in clinical practice. Methods – A comprehensive literature review was conducted to identify relevant studies and articles on AI in radiology. The review included scientific publications, conference proceedings, and reports from reputable organizations and institutions. Results – The findings of the literature review suggest that AI has the potential to enhance the efficiency, accuracy, and precision of medical image analysis and interpretation. AI algorithms can assist radiologists in tasks such as image segmentation, lesion detection, and disease classification. However, several challenges need to be addressed before AI can be widely adopted in clinical practice.
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De Spiegeleire, S., Maas, M., & Sweijs, T. (2017). Artificial intelligence and the future of defense: strategic implications for small-and medium-sized force providers. The Hague Centre for Strategic Studies.
Chockley K, Emanuel E (2016) The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol 13(12):1415–1420
Obermeyer Z, Emanuel EJ (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216
Recht, M., & Bryan, R. N. (2017). Artificial intelligence: threat or boon to radiologists?. Journal of the American College of Radiology, 14(11), 1476-1480.
Prainsack B (2017) Personalized medicine: empowered patients in the 21st century? New York University Press 100
Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama, 316(22), 2353-2354.
Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21, 403-418.
Hallevy, G. (2015). Liability for crimes involving artificial intelligence systems (Vol. 257). New York, NY, USA: Springer International Publishing.
Chan, W. V., Pearson, T. A., Bennett, G. C., Cushman, W. C., Gaziano, T. A., Gorman, P. N., ... & Jacobson Vann, J. C. (2017). ACC/AHA special report: clinical practice guideline implementation strategies: a summary of systematic reviews by the NHLBI Implementation Science Work Group: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation, 135(9), e122-e137.
Osoba OA, Welser W IV (2017) An intelligence in our image: The risks of bias and errors in artificial intelligence. https://www.rand.org/pubs/research_reports/RR1744.html. Accessed 4 Oct 2019
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big data & society, 3(1), 2053951715622512.
Sartore, F. (2016). Big data: privacy and intellectual property in a comparative perspective.
Wachter S, Mittelstadt B, Russell C (2017) Counterfactual explanations without opening the black box: automated decisions and the GPDR. Harv JL Tech 31:841
Hofkirchner, W., & Burgin, M. (Eds.). (2017). Future Information Society, The: Social And Technological Problems (Vol. 8). World Scientific.
Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21, 403-418.
Thierer A, O’Sullivan A, Russel R (2017) Artificial intelligence and public policy. Mercatus Research Paper. Available via https://www.mercatus.org/system/files/thierer-artificial-intelligence-policy-mr-mercatus-v1.pdf. Accessed 4 Oct 2019
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577.
Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574-582.
Blindheim, K. (2011). On studying the unstudyable: a quantitative literature review of proposed causes of corruption and how these have been studied (Master's thesis, The University of Bergen).
Di Giamberardino, P., Iacoviello, D., Jorge, R. N., & Tavares, J. M. R. (2014). Computational Modelling of Objects Represented in Images.
Naaktgeboren CA, de Groot JA, van Smeden M, Moons KG, Reitsma JB (2013) Evaluating diagnostic accuracy in the face of multiple reference standards. Ann Intern Med 159(3):195–202
Whiting P, Rutjes AWS, Reitsma JB, Glas AS, Bossuyt PMM, Kleijnen J (2004) Sources of variation and bias in studies of diagnostic accuracy: a systematic review. Ann Intern Med 140(3):189–202
Tricoci P, Allen JM, Kramer JM, Califf RM, Smith SC Jr (2009) Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA 301:831–84128
Leung KY, van der Lijn F, Vrooman HA, Sturkenboom MC, Niessen WJ (2015) IT Infrastructure to support the secondary use of routinely acquired clinical imaging data for research. Neuroinformatics 13(1):65–81
European Society of Radiology (ESR) communications@ myesr. org. (2015). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights into imaging, 6, 141-155.
Limkin, E. J., Sun, R., Dercle, L., Zacharaki, E. I., Robert, C., Reuzé, S., ... & Ferté, C. (2017). Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Annals of Oncology, 28(6), 1191-1206.
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., ... & Alzheimer's Disease Neuroimaging Initiative. (2015). Impact of the Alzheimer's disease neuroimaging initiative, 2004 to 2014. Alzheimer's & Dementia, 11(7), 865-884.
Ashish, N., Bhatt, P., & Toga, A. W. (2016). Global data sharing in Alzheimer disease research. Alzheimer Disease & Associated Disorders, 30(2), 160-168.
Siegel, R., Naishadham, D., & Jemal, A. (2013). Cancer statistics, 2013. CA: a cancer journal for clinicians, 63(1).
Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., ... & Satpathy, M. (2017). Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA oncology, 3(4), 524-548.
Wang, Z. J., Zhang, L. L., Elmariah, S., Han, H. Y., & Zhou, Y. J. (2017, March). Prevalence and prognosis of nonobstructive coronary artery disease in patients undergoing coronary angiography or coronary computed tomography angiography: a meta-analysis. In Mayo Clinic Proceedings (Vol. 92, No. 3, pp. 329-346). Elsevier.
Venema E, Mulder MJHL, Roozenbeek B et al (2017) Selection of patients for intra-arterial treatment for acute ischaemic stroke: development and validation of a clinical decision tool in two randomised trials. BMJ 357:j1710. https://doi.org/10.1136/bmj.j1710
Bittencourt, M. S., Hulten, E., Polonsky, T. S., Hoffman, U., Nasir, K., Abbara, S., ... & Blankstein, R. (2016). European Society of Cardiology–recommended coronary artery disease consortium pretest probability scores more accurately predict obstructive coronary disease and cardiovascular events than the Diamond and Forrester Score: the Partners Registry. Circulation, 134(3), 201-211.
Lee, J. G., Jun, S., Cho, Y. W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep learning in medical imaging: general overview. Korean journal of radiology, 18(4), 570-584.
Achenbach, S., Goroll, T., Seltmann, M., Pflederer, T., Anders, K., Ropers, D., ... & Marwan, M. (2011). Detection of coronary artery stenoses by low-dose, prospectively ECG-triggered, high-pitch spiral coronary CT angiography. JACC: Cardiovascular Imaging, 4(4), 328-337.
Sanchez-Morillo, D., Fernandez-Granero, M. A., & Leon-Jimenez, A. (2016). Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: a systematic review. Chronic respiratory disease, 13(3), 264-283.
Purnama, D. W., Rochman, C. B. A., Darmawan, R., Amaliyah, P., Baqi, A. D., & Zahidin, A. (2011). A Bibliometrics Analysis for Artificial Intelligence Implementation of Employment in Education Institutions.
Kumar, R. (2017). Machine learning and cognition in enterprises: business intelligence transformed. Apress.
Wintermark, M., Sanelli, P. C., Albers, G. W., Bello, J., Derdeyn, C., Hetts, S. W., ... & Meltzer, C. C. (2013). Imaging recommendations for acute stroke and transient ischemic attack patients: a joint statement by the American Society of Neuroradiology, the American College of Radiology, and the Society of NeuroInterventional Surgery. American Journal of Neuroradiology, 34(11), E117-E127.
Stites, M., & Pianykh, O. S. (2016). How secure is your radiology department? Mapping digital radiology adoption and security worldwide. American Journal of Roentgenology, 206(4), 797-804.
Bøhler, H. M. (2017). EU copyright protection of works created by artificial intelligence systems (Master's thesis, The University of Bergen).
Hsu, W., El-Saden, S., & Taira, R. K. (2016). Medical imaging informatics. Translational Biomedical Informatics: A Precision Medicine Perspective, 167-224.
López, J. M., Catena, A., Montes, A., & Castillo, M. E. (2017). Effectiveness of a short voice training program for teachers: a preliminary study. Journal of Voice, 31(6), 697-706.
Asken, B. M., Sullan, M. J., DeKosky, S. T., Jaffee, M. S., & Bauer, R. M. (2017). Research gaps and controversies in chronic traumatic encephalopathy: a review. JAMA neurology, 74(10), 1255-1262.
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