Restructuring the health professions and perspectives for the future in the Artificial Intelligence era

Authors

DOI:

https://doi.org/10.51723/ccs.v32i03.1060

Keywords:

Artificial Intelligence, Biomedical Technology Assessment, Medical Education, Deep Learning, Computer Security

Abstract

The application of artificial intelligence devices and algorithms in health has become a reality in many areas. However, as in the case of other technologies already used, in order for these tools to effectively result in improving the quality of care, health professionals need to know how to critically assess their positive and negative aspects, as well as its own limitations. Thus, it is extremely important to adopt a posture of collaboration, and not of confrontation, with these devices. This essay seeks to do an overview about artificial intelligence, and it's use in health, in addition to elucidate points and raise questions on risks, benefits and the dilemma of dehumanization in medical settings. Furthermore, it seeks to reflect about what is the profile that the “professional of the future” should have and the paths that educational institutions could take for their training.

Downloads

Download data is not yet available.

Author Biographies

  • Julival Fagundes Ribeiro, Hospital de Base do Distrito Federal - HBDF

    Médico. Doutorado em Medicina Tropical pela Universidade de Brasília. Hospital de Base do Distrito Federal (HBDF). Brasília, DF, Brasil. 

  • Nelson Silvestre Garcia Chaves, Escola Superior de Ciências da Saúde - ESCS

    Acadêmico de Medicina. Escola Superior de Ciências da Saúde (ESCS).

  • Derek Chaves Lopes, Escola Superior de Ciências da Saúde - ESCS

    Acadêmico de Medicina. Escola Superior de Ciências da Saúde.

  • Gabriel Elias de Macedo , Escola Superior de Ciências da Saúde - ESCS

    Acadêmico de Medicina. Escola Superior de Ciências da Saúde

References

Patin K. From mythology to machine learning, a history of artificial intelligence. Coda Story 2020. https://www.codastory.com/authoritarian-tech/history-artificial-intelligence/ (accessed March 30, 2021).

Bussler F. A History of Artificial Intelligence — From the Beginning. Medium 2020. https://towardsdatascience.com/a-history-of-artificial-intelligence-from-the-beginning-10be5b99c5f4 (accessed March 30, 2021).

Rico DF, Sayani HH, Field RF. History of Computers, Electronic Commerce and Agile Methods. Adv. Comput., vol. 73, Elsevier; 2008, p. 1–55. https://doi.org/10.1016/S0065-2458(08)00401-4 .

Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92:807–12. https://doi.org/10.1016/j.gie.2020.06.040.

Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69:S36–40. https://doi.org/10.1016/j.metabol.2017.01.01.

Rigby MJ. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics 2019;21:121–4. https://doi.org/10.1001/amajethics.2019.121.

Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019;7. https://doi.org/10.7717/peerj.7702.

Lobo LC. Inteligência artificial, o Futuro da Medicina e a Educação Médica. Rev Bras Educ Médica 2018;42:3–8. https://doi.org/10.1590/1981-52712015v42n3rb20180115editorial1.

Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform 2019;28:016–26. https://doi.org/10.1055/s-0039-1677908.

Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019;28:73–81. https://doi.org/10.1080/13645706.2019.1575882.

Haleem A, Javaid M, Khan IH. Current status and applications of Artificial Intelligence (AI) in medical field: An overview. Curr Med Res Pract 2019;9:231–7. https://doi.org/10.1016/j.cmrp.2019.11.005.

Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to Machine Learning, Neural Networks, and Deep Learning. Neural Netw n.d.:12.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44. https://doi.org/10.1038/nature14539.

Wiestler B, Menze B. Deep learning for medical image analysis: a brief introduction. Neuro-Oncol Adv 2020;2:iv35–41. https://doi.org/10.1093/noajnl/vdaa092.

Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychol Rev 1958;65:386–408. https://doi.org/10.1037/h0042519.

Panesar A. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Berkeley, CA: Apress; 2019. https://doi.org/10.1007/978-1-4842-3799-1.

Wang L, Alexander CA. Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol 2020;44:267–83. https://doi.org/10.1080/03091902.2020.1769758.

Kalil AJ, Dias VM de CH, Rocha C da C, Morales HMP, Fressatto JL, Faria RA de. Sepsis risk assessment: a retrospective analysis after a cognitive risk management robot (Robot Laura®) implementation in a clinical-surgical unit. Res Biomed Eng 2018;34:310–6. https://doi.org/10.1590/2446-4740.180021.

Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med 2020;7:27. https://doi.org/10.3389/fmed.2020.00027.

Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020;8:e18599. https://doi.org/10.2196/18599.

Cordeiro JV. Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges. Front Med 2021;8:647897. https://doi.org/10.3389/fmed.2021.647897.

England JR, Cheng PM. Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. AJR Am J Roentgenol 2019;212:513–9. https://doi.org/10.2214/AJR.18.20490.

Martinez-Martin N. What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care? AMA J Ethics 2019;21:180–7. https://doi.org/10.1001/amajethics.2019.180.

Christian G. Assessing Methods and Tools to improve reporting, increase transparency, and reduce failures in Machine Learning Applications in Healthcare. MS. Florida Atlantic University, n.d.

Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 2020:l6927. https://doi.org/10.1136/bmj.l6927.

Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int 2018;121:1027–31. https://doi.org/10.1016/j.envint.2018.07.015.

Ministério da Saúde, Secretaria de Ciência, Tecnologia e Insumos Estratégicos, Departamento de Ciência e Tecnologia. Diretrizes Metodológicas: Elaboração de revisão sistemática e metanálise de estudos observacionais comparativos sobre fatores de risco e prognóstico. Brasília, Distrito Federal: 2014.

Leslie D, Mazumder A, Peppin A, Wolters MK, Hagerty A. Does “AI” stand for augmenting inequality in the era of covid-19 healthcare? BMJ 2021;372:n304. https://doi.org/10.1136/bmj.n304.

Hlávka JP. Chapter 10.Security, privacy, and information-sharing aspects of healthcare artificial intelligence. In: Bohr A, Memarzadeh K, editors. Artif. Intell. Healthc., Academic Press; 2020: 235–70. https://doi.org/10.1016/B978-0-12-818438-7.00010-1.

Luxton DD. Recommendations for the ethical use and design of artificial intelligent care providers. Artif Intell Med 2014;62:1–10. https://doi.org/10.1016/j.artmed.2014.06.004.

Hlavin G, Koenig F, Male C, Posch M, Bauer P. Evidence, eminence and extrapolation. Stat Med 2016;35:2117–32. https://doi.org/10.1002/sim.6865.

West SM, Whittaker M, Crawford K. Discriminating Systems: Gender, Power and Race in AI. AI Now Institute; n.d. Retrieved from https://ainowinstitute.org/ discriminatingsystems.html

Park SY, Park JE, Kim H, Park SH. Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches). Korean J Radiol 2021;22:e88. https://doi.org/10.3348/kjr.2021.0223.

Vaughn J, Baral A, Vadari M, Boag W. Dataset Bias in Diagnostic AI systems: Guidelines for Dataset Collection and Usage 2020:9. http: //juliev42.github.io/files/CHIL_paper_bias.pdf

Panch T, Mattie H, Atun R. Artificial intelligence, and algorithmic bias: implications for health systems. J Glob Health n.d.;9:020318. https://doi.org/10.7189/jogh.09.020318.

Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020:m1328. https://doi.org/10.1136/bmj.m1328.

Minossi JG, Silva AL da. Medicina defensiva: uma prática necessária? Rev Colégio Bras Cir 2013;40:494–501. https://doi.org/10.1590/S0100-69912013000600013.

Shortliffe EH. Dehumanization of Patient Care--Are Computers the Problem or the Solution? J Am Med Inform Assoc 1994;1:76–8. https://doi.org/10.1136/jamia.1994.95236139.

Haque OS, Waytz A. Dehumanization in Medicine: Causes, Solutions, and Functions. Perspect Psychol Sci 2012;7:176–86. https://doi.org/10.1177/1745691611429706.

Densen P. Challenges and Opportunities Facing Medical Education. Trans Am Clin Climatol Assoc 2011;122:42–58. PMCID: PMC3116346

McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence? Npj Digit Med 2020;3:86. https://doi.org/10.1038/s41746-020-0294-7.

Published

2021-09-24

Issue

Section

Saúde Coletiva

How to Cite

1.
Restructuring the health professions and perspectives for the future in the Artificial Intelligence era. Com. Ciências Saúde [Internet]. 2021 Sep. 24 [cited 2024 Nov. 20];32(03). Available from: https://revistaccs.espdf.fepecs.edu.br/index.php/comunicacaoemcienciasdasaude/article/view/1060

Similar Articles

1-10 of 372

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)