Bioethical considerations in the use of artificial intelligence in mastology
Keywords:
BREAST NEOPLASMS, MAMMOGRAPHIC SCREENING, BIG DATA, BIOETHICS, ARTIFICIAL INTELLIGENCE, CONFIDENTIALITY, PRIVACY
Abstract
Mammographic screening has helped to identify breast cancer in its earliest stages, when treatment is most effective. The use of Artificial Intelligence in the analysis of mammograms has proved to be able to excel the human eye in detecting lesions in the breast that may be suspicious for cancer. The objective of this study is to make a reflective contribution on the advancement of digital technology and in particular, Artificial Intelligence in mammographic screening, from the technical and bioethical points of view. Advantages and limitations of Artificial Intelligence are analyzed explaining how machine learning occurs. A bioethical debate is proposed on issues such as privacy, credibility, accountability and continuous education. The importance of establishing channels of dialogue between all stakeholders in the incorporation of new technologies in medicine is highlighted.
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2) Kyono T, Gilbert F, van der Schaar M. Improving workflow efficiency for mammography using machine learning. J Am Coll Radiol 2020; 17(1 Pt A):56-63.
3) Geras KJ, Mann RM, Moy L. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology 2019; 293(2):246-59. doi: 10.1148/radiol.2019182627.
4) Paci E, Broeders M, Hofvind S, Puliti D, Duffy S. European breast cancer service screening outcomes: a first balance sheet of the benefits and harms. Cancer Epidemiol Biomarkers Prev 2014; 23(7):1159-63.
5) Tabár L, Vitak B, Chen T, Yen A, Cohen A, Tot T, et al. Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology 2011; 260(3):658-63.
6) Lehman C, Arao R, Sprague B, Lee J, Buist D, Kerlikowske K, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 2017; 283(1):49-58. doi: 10.1148/radiol.2016161174.
7) McKinney S, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature 2020; 577(7788):89-94. doi: 10.1038/s41586-019-1799-6.
8) Copeland B. Artificial intelligence. En: Encyclopedia Britannica. London: Encyclopedia Britannica, 2021. Disponible en: www.britannica.com/technology/artificial-intelligence [Consulta: 4 marzo 2021].
9) McCarthy J. What is artificial intelligence? 2007. Disponible en: http://jmc.stanford.edu/articles/whatisai/whatisai.pdf [Consulta: 12 abril 2021].
10) Guinovart X. Fundamentos de Lingüística Computacional: bases teóricas, líneas de investigación y aplicaciones. Bibliodoc: anuari de biblioteconomia, documentació i informació 1998; 135-46. Disponible en: http://www.raco.cat/index.php/Bibliodoc/article/view/56629/66051 [Consulta: 16 febrero 2021].
11) Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61:85-117.
12) Alpaydin E. Introduction to machine learning. 3rd ed. The MIT Press, 2014.
13) Uruguay. UdelaR. Facultad de Ingeniería. Aprendizaje automático aplicado al análisis de textos. Disponible en: https://www.fing.edu.uy/cpap/cursos/aprendizaje-automatico-aplicado-al-analisis-de-textos [Consulta: 4 marzo 2021].
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15) Deng L, Yu D. Deep learning: methods and applications. Found Trends Signal Process 2013; 7(3-4):197-387.
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18) Tabár L, Fagerberg C, Gad A, Baldetorp L, Holmberg L, Gröntoft O, et al. Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. Lancet 1985; 1(8433):829-32.
19) Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art. Semin Cancer Biol 2021; 72:214-25. doi: 10.1016/j.semcancer.2020.06.002.
20) Abbasi J. Artificial intelligence improves breast cancer screening in study. JAMA 2020; 323(6):499. doi: 10.1001/jama.2020.0370.
21) Mendelson E. Artificial intelligence in breast imaging: potentials and limitations. AJR Am J Roentgenol 2019; 212(2):293-9.
22) Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J American Coll Radiol 2018; 15(3 Pt B):504-8. doi: 10.1016/j.jacr.2017.12.026.
23) European Group on Ethics in Science and New Technologies. Statement of artificial intelligence, robotics and “autonomous” systems. Brussels, 2018. doi: 10.2777/786515.
24) Vayena E, Haeusermann T, Adjekum A, Blasimme A. Digital health: meeting ethical and policy challenges. En: Stuckelberger C, Duggal P, eds. Cyber Ethics 4.0: Serving Humanity with values. Geneva: Stuckelberger-Duggal, 2018:229-58.
25) Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017; 37(7):2113-31. doi: 10.1148/rg.2017170077.
26) Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol 2016; 13(12 Pt A):1415-20. doi: 10.1016/j.jacr.2016.07.010.
27) Nabi J. How bioethics can shape artificial intelligence and machine learning? J Hastings Cent Rep 2018; 48(5):10-13. doi: 10.1002/hast.895.
28) Elenko E, Speier A, Zohar D. A regulatory framework emerges for digital medicine. Nat Biotechnol 2015; 33(7):697-702.
29) Rigby M. Ethical dimensions of using artificial intelligence in health care. AMA J Ethics 2019; 21(2):E121-4. doi: 10.1001/amajethics.2019.121.
30) Schaffter T, Buist D, Lee C, Nikulin Y, Ribli D, Guan Y, et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 2020; 3(3):e200265. doi: 10.1001/jamanetworkopen.2020.0265.
31) Abdala MB, Lacroix Eussler S, Soubie S. La política de la Inteligencia Artificial: sus usos en el sector público y sus implicancias regulatorias. (Documento de Trabajo N°185). Buenos Aires: CIPPEC, 2019. 25 p.
32) Arieno A, Chan A, Destounis S. A Review of the role of augmented intelligence in breast imaging: from automated breast density assessment to risk stratification. AJR Am J Roentgenol 2019; 212(2):259-70.
Published
2021-11-08
How to Cite
1.
Actis AM. Bioethical considerations in the use of artificial intelligence in mastology. Rev. Méd. Urug. [Internet]. 2021Nov.8 [cited 2024Dec.18];37(4):e37413. Available from: https://www2.rmu.org.uy/ojsrmu311/index.php/rmu/article/view/759
Section
Opinion articles
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