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Revista Médica Sinergia

2020, Number 08

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Revista Médica Sinergia 2020; 5 (08)

Artificial intelligence and machine learning in medicine

Álvarez VM, Quirós MLM, Cortés BMV

Language: Spanish
References: 23
Page: 1-11
PDF size: 154.96 Kb.


ABSTRACT

Machine learning is a powerful branch of Artificial Intelligence that has been used successfully in different industries. In the last few years with the increasing availability of clinical data stored in electronic format, the medical field has become an ideal environment for the development and application of these technologies. Machine learning has the potential to improve healthcare system by analyzing millions of clinical data to create prognostic, screening and diagnostic models. However, even though it is evident that the use of these algorithms can improve the quality of healthcare systems and patient’s life, an appropriate validation process is needed in order to implement these technologies into the clinical practice.


REFERENCES

  1. Obermeyer Z, Lee T. Lost in Thought — The Limits of the Human Mind and the Future of Medicine. New England Journal of Medicine. 2017;377(13):1209-1211. https://doi.org/10.1056/NEJMp1705348

  2. Gui C, Chan V. Machine learning in medicine. University of Western Ontario Medical Journal. 2017;86(2):76-78. https://doi.org/10.5206/uwomj.v86i2.2060

  3. Char D, Shah N, Magnus D. Implementing Machine Learning in Health Care — Addressing Ethical Challenges. New England Journal of Medicine. 2018;378(11):981-983. https://doi.org/10.1056/NEJMp1714229

  4. Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology. 2019;293(1). https://doi.org/10.1148/radiol.2019182908

  5. Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H et al. Dermatologist-level classificaction of skin cancer with deep neural networks. Nature. 2017;542: 115-118. https://doi.org/10.1038/nature21056

  6. Ghorbani A, Ouyang D, Abid A, He B, Chen J, Harrington R et al. Deep learning interpretation of echocardiograms. Npj Digit Med. 2020;3(10). https://doi.org/10.1038/s41746-019-0216-8

  7. Núñez Reiz A, Armengol de la Hoz M, Sánchez García M. Big Data Analysis y Machine Learning en medicina intensiva. Medicina Intensiva [Internet]. 2019 [Citado 25 febrero 2020];43(7):416-426. Disponible en: https://www.medintensiva.org/es-big-data-analysis-machine-learning-articulo-S0210569118303139

  8. Chadha B. Clinical Oracle: Machine Learning in Medicine. Berkeley Scientific Journal;23(2). https://escholarship.org/uc/item/1kt5029r

  9. Adamson A, Welch H. Machine Learning and the Cancer-Diagnosis Problem — No Gold Standard. New England Journal of Medicine. 2019;381(24):2285-2287. https://doi.org/10.1056/NEJMp1907407

  10. Koenigkam M, Ferrei J, Tadao D, Magalhães A, Nogueira M, Mazzoncini de Azevedo P. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras. 2019;52(6):387-396. https://doi.org/10.1590/0100-3984.2019.0049

  11. Camacho D, Collins K, Powers R, Costello J, Collins J. Next- Generation Machine Learning for Biological Networks. Cell. 2018;173 (7): 1581-1592. https://doi.org/10.1016/j.cell.2018.05.015

  12. Sidey-Gibbons J, Sidey-Gibbons C. Machine learning in medicine: a practical introduction. BMC Med Res Metodol. 2019;19(64). https://doi.org/10.1186/s12874-019-0681-4

  13. Choy G, Khalilzadeh O, Michalski M, DO S, Samir A, Pianykh O et al. Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 2018;288(2):318-328. https://doi.org/10.1148/radiol.2018171820

  14. Deo R, Machine Learning in Medicine. Circulation. 2015;132(20):1920-1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593

  15. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. New England Journal of Medicine. 2019;380(14):1347-1358. https://doi.org/10.1056/NEJMra1814259

  16. Erickson B, Korfiatis P, Akkus Z, Kline T. Machine Learning for Medical Imaging. RadioGraphics. 2017;37(2):505-515. https://doi.org/10.1148/rg.2017160130

  17. Kourou K, Exarchos T, Exarchos K, Karamouzis M, Fotiadis D. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2015;13:8-17. https://doi.org/10.1016/j.csbj.2014.11.005

  18. Xu Y, Ju L, Tong J, Zhou C, Yang J. Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection. Sci Rep. 2020;10(2519). https://doi.org/10.1038/s41598-020- 59115-y

  19. Campanella G, Hanna M, Geneslaw L, Miraflor A, Krauss V, Busam K et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25:1301-1309. https://doi.org/10.1038/s41591-019-0508-1

  20. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463-477. https://doi.org/10.1038/s41573-019-0024-5

  21. Rifaioglu A, Atas H, Martin M, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Briefings in Bioinformatics. 2018;20(5):1878-1912. https://doi.org/10.1093/bib/bby061

  22. Vayena E, Blasimme A, Cohen I. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018; 15(11): e1002689. https://doi.org/10.1371/journal.pmed.1002689

  23. Greene J, Lea A. Digital Futures Past — The Long Arc of Big Data in Medicine. New England Journal of Medicine. 2019;381(5):480-485. https://doi.org//10.1056/NEJMms1817674




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