Aspectos bioéticos del uso de sistemas de inteligencia artificial en el campo de la salud: un estudio exploratorio
DOI:
https://doi.org/10.1344/rbd2023.57.35146Palabras clave:
bioética, inteligência artificial, cuidado em saúdeResumen
Objetivo: analizar la percepción de los usuarios de las redes sociales sobre el uso de sistemas de IA en el campo de la salud y aspectos bioéticos asociados a este uso. Método: método mixto, del tipo descriptivo-exploratorio. El camino metodológico se dividió en dos etapas: (1) recopilación de información sobre los principales aspectos bioéticos involucrados en el uso de la IA y (2) elaboración de escenarios para la toma de decisiones. Los datos cuantitativos se analizaron mediante estadística descriptiva para caracterizar la muestra desde el punto de vista sociodemográfico, así como para caracterizar el perfil de toma de decisiones de la muestra con respecto a cuestiones bioéticas asociadas al uso de sistemas de IA. El análisis de datos cualitativos se realizó utilizando el análisis de contenido de Bardin. Resultados: en el perfil sociodemográfico se observa una muestra de mujeres adultas con título universitario. En cuanto a las preocupaciones éticas asociadas a los escenarios aplicados, las principales preocupaciones fueron en primer lugar la privacidad y confidencialidad de los datos, seguidas de las preocupaciones relacionadas con la responsabilidad asociada al uso de estas tecnologías, así como el consentimiento informado. Conclusión: De esta forma, se destaca la importancia de nuevos estudios empíricos exploratorios como este, que evalúen la percepción, actitudes y opiniones de públicos especializados, como profesionales de la salud, derecho, humanidades, con el fin de obtener evidencias concretas para el desarrollo. de programas de gestión y gobernanza de los sistemas de IA, especialmente en el escenario brasileño, donde los recursos son escasos.
Citas
Astromskė, K., Peičius, E., & Astromskis, P. (2020). Ethical and legal challenges of informed consent applying artificial intelligence in medical diagnostic consultations. AI & SOCIETY. https://doi.org/10.1007/s00146-020-01008-9
Ayoub, A., & Pulijala, Y. (2019). The application of virtual reality and augmented reality in Oral & Maxillofacial Surgery. BMC Oral Health, 19, 1–8. https://doi.org/10.1186/s12903-019-0937-8
Baig, M. A., Almuhaizea, M. A., Alshehri, J., Bazarbashi, M. S., & Al-Shagathrh, F. (2020). Urgent Need for Developing a Framework for the Governance of AI in Healthcare. Studies in Health Technology and Informatics, 272, 253–256. https://doi.org/10.3233/SHTI200542
Bandman, E. L., & Bandman, B. (1984). Bioethics and Human Rights: A Reader for Health Professionals. Univ Pr of Amer.
Bardin, L. (2011). Análise de Conteúdo (2nd ed.). Edições 70. https://books.google.com.br/books?id=AFpxPgAACAAJ
Bazeley, P. (2013). QUALITATIVE DATA ANALYSIS: PRACTICAL STRATEGIES. SAGE Publications, Inc.
Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with Nvivo. SAGE Publications, Inc.
Beauchamp, T. (2010). Standing on principles. Oxford University Press.
Beauchamp, T. L., & Childress, J. F. (2013). Principles of Biomedical Ethics (7th ed.). Oxford University Press.
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & Desroches, C. M. (2019). Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. Journal of Medical Internet Research, 21(3). https://doi.org/10.2196/12802
Brasil. (2018). Lei Geral de Proteção de Dados Pessoais (LGPD). DOU, 14 de Agosto de 2018. http://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528. https://doi.org/10.1007/s11948-017-9901-7
Chianca, V., Cuocolo, R., Gitto, S., Albano, D., Merli, I., Badalyan, J., Cortese, M. C., Messina, C., Luzzati, A., Parafioriti, A., Galbusera, F., Brunetti, A., & Sconfienza, L. M. (2021). Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study. European Journal of Radiology, 137, 109586. https://doi.org/10.1016/j.ejrad.2021.109586
Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, 26(4), 2051–2068. https://doi.org/10.1007/s11948-019-00146-8
Creswell, J. ., & Clark, V. L. P. (2015). Pesquisa de Métodos Mistos (2a). Penso Editora.
Cui, M., & Zhang, D. Y. (2021). Artificial intelligence and computational pathology. Laboratory Investigation, 101(4), 412–422. https://doi.org/10.1038/s41374-020-00514-0
Do, H. M., Spear, L. G., Nikpanah, M., Mirmomen, S. M., Machado, L. B., Toscano, A. P., Turkbey, B., Bagheri, M. H., Gulley, J. L., & Folio, L. R. (2020). Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence. Academic Radiology, 27(1), 96–105. https://doi.org/10.1016/j.acra.2019.09.014
Ekmekci, P. E., & Arda, B. (2020). History of Artificial Intelligence (pp. 1–15). https://doi.org/10.1007/978-3-030-52448-7_1
Emanuel, E. J., & Wachter, R. M. (2019). Artificial Intelligence in Health Care. JAMA, 321(23), 2281. https://doi.org/10.1001/jama.2019.4914
Faleiros, M. C., Nogueira-Barbosa, M. H., Dalto, V. F., Júnior, J. R. F., Tenório, A. P. M., Luppino-Assad, R., Louzada-Junior, P., Rangayyan, R. M., & de Azevedo-Marques, P. M. (2020). Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging. Advances in Rheumatology, 60(1), 25. https://doi.org/10.1186/s42358-020-00126-8
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Goldim, J. R. (2006). Bioética: Origens e complexidade. Rev HCPA, 26(2).
Goldim, J. R. (2009). [Complex bioethics: a comprehensive approach to decision making process]. Revista AMRIGS, 53(1), 58–63.
Goldim, J. R. (2016). Bioethics after Levinas. In eLS (pp. 1–4). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470015902.a0026694
Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Husnayain, A., Shim, E., Fuad, A., & Su, E. C.-Y. (2020). Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study. Journal of Medical Internet Research, 22(9), e19788. https://doi.org/10.2196/19788
Johnson, S. L. J. (2019). AI, Machine Learning, and Ethics in Health Care. The Journal of Legal Medicine, 39(4), 427–441. https://doi.org/10.1080/01947648.2019.1690604
Jonas, H. (2006). O principio responsabilidade: ensaio de uma etica para a civilização tecnologica. Contraponto.
Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 92(4), 807–812. https://doi.org/10.1016/j.gie.2020.06.040
Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H., & Langlotz, C. P. (2020). Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework. Radiology, 192536. https://doi.org/10.1148/radiol.2020192536
Lin, S. Y., Mahoney, M. R., & Sinsky, C. A. (2019). Ten Ways Artificial Intelligence Will Transform Primary Care. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-019-05035-1
Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A., & de Lusignan, S. (2019a). Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of Medical Informatics. https://doi.org/10.1055/s-0039-1677901
Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S. (2019b). Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of Medical Informatics, 28(01), 041–046. https://doi.org/10.1055/s-0039-1677901
Lopez-Jimenez, F., Attia, Z., Arruda-Olson, A. M., Carter, R., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C., Medina-Inojosa, J. R., Noseworthy, P. A., Pellikka, P. A., Redfield, M. M., Roger, V. L., Sandhu, G. S., Senecal, C., & Friedman, P. A. (2020). Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings, 95(5), 1015–1039. https://doi.org/10.1016/j.mayocp.2020.01.038
Marquis, P., Papini, O., & Prade, H. (2020). Elements for a History of Artificial Intelligence. In A Guided Tour of Artificial Intelligence Research (pp. 1–43). Springer International Publishing. https://doi.org/10.1007/978-3-030-06164-7_1
Martin, A., Nateqi, J., Gruarin, S., Munsch, N., Abdarahmane, I., Zobel, M., & Knapp, B. (2020). An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Scientific Reports, 10(1), 19012. https://doi.org/10.1038/s41598-020-75912-x
Morley, J., Machado V, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. SOCIAL SCIENCE & MEDICINE, 260. https://doi.org/10.1016/j.socscimed.2020.113172
Oren, O., Gersh, B. J., & Bhatt, D. L. (2020). Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. The Lancet Digital Health, 2(9), e486–e488. https://doi.org/10.1016/S2589-7500(20)30160-6
Price, W. N. 2nd, & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7
Racine, E., Boehlen, W., & Sample, M. (2019). Healthcare uses of artificial intelligence: Challenges and opportunities for growth. Healthcare Management Forum, 32(5), 272–275. https://doi.org/10.1177/0840470419843831
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491–497. https://doi.org/10.1093/jamia/ocz192
Risling, T. L., & Low, C. (2019). Advocating for Safe, Quality and Just Care: What Nursing Leaders Need to Know about Artificial Intelligence in Healthcare Delivery. Nursing Leadership (Toronto, Ont.), 32(2), 31–45. https://doi.org/10.12927/cjnl.2019.25963
Soltan, A. A. S., Kouchaki, S., Zhu, T., Kiyasseh, D., Taylor, T., Hussain, Z. B., Peto, T., Brent, A. J., Eyre, D. W., & Clifton, D. A. (2021). Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test. The Lancet Digital Health, 3(2), e78–e87. https://doi.org/10.1016/S2589-7500(20)30274-0
Tang, X. (2020). The role of artificial intelligence in medical imaging research. BJR|Open, 2(1), 20190031. https://doi.org/10.1259/bjro.20190031
Tsiknakis, N., Trivizakis, E., Vassalou, E., Papadakis, G., Spandidos, D., Tsatsakis, A., Sánchez‑García, J., López‑González, R., Papanikolaou, N., Karantanas, A., & Marias, K. (2020). Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays. Experimental and Therapeutic Medicine, 20(2), 727–735. https://doi.org/10.3892/etm.2020.8797
UNESCO. (2009). The UNESCO Universal Declaration on Bioethics and Human Rights Background , principles and application (H. A. M. J. ten Have & M. S. Jean (eds.)). United Nations Educational. http://www.unesco-chair-bioethics.org/wp-content/uploads/2015/08/The-UNESCO-Universal-Declaration-on-Bioethics-and-Human-Rights-Background-Principles-and-Application.pdf
Uusitalo, S., Tuominen, J., & Arstila, V. (2020). Mapping out the philosophical questions of AI and clinical practice in diagnosing and treating mental disorders. Journal of Evaluation in Clinical Practice, jep.13485. https://doi.org/10.1111/jep.13485
Visweswaran, S., Colditz, J. B., O’Halloran, P., Han, N.-R., Taneja, S. B., Welling, J., Chu, K.-H., Sidani, J. E., & Primack, B. A. (2020). Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study. Journal of Medical Internet Research, 22(8), e17478. https://doi.org/10.2196/17478
Weikert, T., Winkel, D. J., Bremerich, J., Stieltjes, B., Parmar, V., Sauter, A. W., & Sommer, G. (2020). Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm. European Radiology, 30(12), 6545–6553. https://doi.org/10.1007/s00330-020-06998-0
Woo, H., Cho, Y., Shim, E., Lee, J.-K., Lee, C.-G., & Kim, S. H. (2016). Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea. Journal of Medical Internet Research, 18(7), e177. https://doi.org/10.2196/jmir.4955
Yetisen, A. K., Martinez-Hurtado, J. L., Ünal, B., Khademhosseini, A., & Butt, H. (2018). Wearables in Medicine. Advanced Materials, 30(33), 1706910. https://doi.org/10.1002/adma.201706910
Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint. Journal of Medical Internet Research, 22(9), e22845. https://doi.org/10.2196/22845
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Derechos de autor 2023 Leonardo de Oliveira, Lucas Garcia, Evelise Duarte, Fagner de Moura
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