Fatores que influenciam a aceitação de Tecnologias de Inteligência Artificial na Saúde

Autores

DOI:

https://doi.org/10.26512/gs.v13i01.41552

Palavras-chave:

Inteligência Artificial, Sistemas de Apoio a Decisões Clínicas, Aplicações da Informática Médica, Revisão Sistemática

Resumo

Desde 2010, a utilização de tecnologias de Inteligência Artificial na saúde e promoção da qualidade de vida apresenta um progresso significativo na medicina. Entretanto, há muitas barreiras e resistência quanto a sua implementação seja por parte da gestão do hospital, paciente, profissional de saúde, conselho e sociedade de forma geral. O objetivo desta pesquisa é identificar os fatores que influenciam a aceitação da Inteligência Artificial na área da saúde por meio de uma revisão sistemática dos estudos que avaliaram empiricamente o uso dessa tecnologia. Para composição do arcabouço literário, foi realizada uma revisão sistemática da literatura na base de periódicos Web of Science com amostra final de 50 artigos. Como principais resultados, foram identificados 11 fatores: aspectos clínicos, aspectos humanos, aspectos organizacionais, aspectos regulatórios, experiência do usuário, grau de instrução para desenvolvimento de tecnologia, grau de instrução para uso da tecnologia, infraestrutura tecnológica, implantação tecnológica, percepção de potencial e resistência à inovação.

Downloads

Não há dados estatísticos.

Referências

Abedi, V., Khan, A., Chaudhary, D., Misra, D., Avula, V., Mathrawala, D., Kraus, C., Marshall, K. A., Chaudhary, N., Li, X., Schirmer, C. M., Scalzo, F., Li, J. & Zand, R. (2020). Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework [PMID: 32922515]. Therapeutic Advances in Neurological Disorders, 13, 1756286420938962. https://doi.org/10.1177/1756286420938962

Ahmadi, M., Mehrabi, N., Sheikhtaheri, A. & Sadeghi, M. (2017). Acceptability of picture archiving and communication system (PACS) among hospital healthcare personnel based on a unified theory of acceptance and use of technology. Electronic Physician, 9(9), 5325–5330. https: //doi.org/10.19082/5325.

Andersson, J., Nyholm, T., Ceberg, C., Almén, A., Bernhardt, P., Fransson, A. & Olsson, L. E. (2021).

Artificial intelligence and the medical physics profession - A Swedish perspective. Physica

Medica, 88, 218–225. https://doi.org/https://doi.org/10.1016/j.ejmp.2021.07.009

Azimova, N. D., Ashirbaev, S. P. & Vikhrov, I. P. (2020). THE FIRST STEPS IN ARTIFICIAL

INTELLIGENCE DEVELOPMENT IN MEDICINE IN UZBEKISTAN. Health Problems of

Civilization, 14(4), 314–319. https://doi.org/10.5114/hpc.2020.98086

Bergier, H., Duron, L., Sordet, C., Kawka, L., Schlencker, A., Chasset, F. & Arnaud, L. (2021). Digital

health, big data and smart technologies for the care of patients with systemic autoimmune

diseases: Where do we stand? Autoimmunity Reviews, 20(8), 102864.

https://doi.org/10.1016/j. autrev.2021.102864

Berre, C., Sandborn, W., Aridhi, S., Devignes, M.-D., Fournier, L., Smail, M., Danese, S. & PeyrinBiroulet, L. (2019). Application of Artificial Intelligence to Gastroenterology and Hepatology.

Gastroenterology, 158. https://doi.org/10.1053/j.gastro.2019.08.058

Beyar, R., Davies, J. E., Cook, C., Dudek, D., Cummins, P. A. & Bruining, N. (2021). Robotics,

imaging, and artificial intelligence in the catheterisation laboratory. EuroIntervention, 17,

–549. https://doi.org/10.4244/EIJ-D-21-00145

Boon-itt, S. (2019). Quality of health websites and their influence on perceived usefulness, trust and

intention to use: an analysis from Thailand. Journal of Innovation and Entrepreneurship, 8(1).

https://doi.org/10.1186/s13731-018-0100-9

Briganti, G. & Le Moine, O. (2020). Artificial Intelligence in Medicine: Today and Tomorrow. Frontiers

in Medicine, 7, 27. https://doi.org/10.3389/fmed.2020.00027

Brito, J. V. d. C. S. & Ramos, A. S. M. (2019). Limitações dos Modelos de Aceitação da Tecnologia: um

Ensaio sob uma Perspectiva Crítica. Edição Especial: VIII Simpósio Brasileiro de Tecnologia

da Informação, 17(EE), 210–220. https://doi.org/10.21714/1679-18272019v17esp.p210-220

Cao, H., Zhang, Z., Evans, R. D., Dai, W., Bi, Q., Zhu, Z., Xu, J. & Shen, L. (2021). Barriers and

Enablers to the Implementation of Intelligent Guidance Systems for Patients in Chinese Tertiary

Transfer Hospitals: Usability Evaluation. IEEE Transactions on Engineering Management,

–10. https://doi.org/10.1109/TEM.2021.3066564

Chen, C.-Y., Lin, W.-C. & Yang, h.-y. (2020). Diagnosis of ventilator-associated pneumonia using

electronic nose sensor array signals: solutions to improve the application of machine learning

in respiratory research. Respiratory Research, 21. https://doi.org/10.1186/s12931-020-1285-6.

Cheng, J., Abel, J., Balis, U., McClintock, D. & Pantanowitz, L. (2020). Challenges in the Development,

Deployment Regulation of Artificial Intelligence (AI) in Anatomical Pathology. The American

Journal of Pathology, 191. https://doi.org/10.1016/j.ajpath.2020.10.018

Davenport, T. & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future

Hospital Journal, 6, 94–98.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information

Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Diprose, W. K., Buist, N. S., Hua, N., Thurier, Q., Shand, G. & Robinson, R. (2020). Physician

understanding, explainability, and trust in a hypothetical machine learning risk calculator.

Journal of the American Medical Informatics Association: JAMIA.

Esmaeilzadeh, P., Mirzaei, T. & Dharanikota, S. (2021). Patients’ Perceptions Toward Human–Artificial

Intelligence Interaction in Health Care: Experimental Study. Journal of Medical Internet

Research, 23, e25856. https://doi.org/10.2196/25856

Fabbri, S., Silva, C., Hernandes, E., Octaviano, F., Di Thommazo, A. & Belgamo, A. (2016). Improvements in the StArt tool to better support the systematic review process. Proceedings of

the 20th International Conference on Evaluation and Assessment in Software Engineering.

https://doi.org/10.1145/2915970.2916013

Field, M., Vinod, S., Aherne, N., Carolan, M., Dekker, A., Delaney, G., Greenham, S., Hau, E.,

Lehmann, J., Ludbrook, J., Miller, A., Rezo, A., Selvaraj, J., Sykes, J., Holloway, L. & Thwaites,

D. (2021). Implementation of the Australian Computer-Assisted Theragnostics (AusCAT)

network for radiation oncology data extraction, reporting and distributed learning. Journal of

Medical Imaging and Radiation Oncology, 65. https://doi.org/10.1111/1754-9485.13287

Foreman, B. (2020). Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James)

Integrating and Using Big Data in Neurocritical Care. Neurotherapeutics, 17. https://doi.org/10.

/s13311-020-00846-1

Gomolin, A., Netchiporouk, E., Gniadecki, R. & Litvinov, I. (2020). Artificial Intelligence Applications

in Dermatology: Where Do We Stand? Frontiers in Medicine, 7. https://doi.org/10.3389/fmed.

00100

Gopal, G., Suter-Crazzolara, C., Toldo, L. & Eberhardt, W. (2018). Digital transformation in healthcare - Architectures of present and future information technologies. Clinical Chemistry and

Laboratory Medicine (CCLM), 57. https://doi.org/10.1515/cclm-2018-0658

Gu, D., Zhao, W., Xie, Y., Wang, X., Su, K. & Zolotarev, O. V. (2021). A Personalized Medical Decision

Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data

from the Real World. Diagnostics, 11(9). https://doi.org/10.3390/diagnostics11091677

Gubatan, J., Levitte, S., Patel, A., Balabanis, T., Wei, M. & Sinha, S. (2021). Artificial intelligence

applications in inflammatory bowel disease: Emerging technologies and future directions.

World journal of gastroenterology, 27, 1920–1935. https://doi.org/10.3748/wjg.v27.i17.1920

Holden, R. J. & Karsh, B. (2010). The Technology Acceptance Model: Its past and its future in health

care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.

Hu, Y., Jacob, J., Parker, G., Hawkes, D., Hurst, J. & Stoyanov, D. (2020). The challenges of deploying

artificial intelligence models in a rapidly evolving pandemic.

Hughes, K., Zhou, J., Bao, Y., Singh, P., Wang, J. & Yin, K. (2019). Natural language processing to

facilitate breast cancer research and management. The Breast Journal, 26. https://doi.org/10.

/tbj.13718

Juravle, G., Boudouraki, A., Terziyska, M. & Rezlescu, C. (2020). Chapter 14 - Trust in artificial

intelligence for medical diagnoses (B. L. Parkin, Ed.; Vol. 253). Elsevier. https://doi.org/https:

//doi.org/10.1016/bs.pbr.2020.06.006 .

Jutzi, T., Krieghoff-Henning, E., Holland-Letz, T., Utikal, J., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., Maron, R. & Brinker, T. (2020). Artificial

Intelligence in Skin Cancer Diagnostics: The Patients’ Perspective. Frontiers in Medicine, 7.

https://doi.org/10.3389/fmed.2020.00233

Kasperbauer, T. (2020). Conflicting roles for humans in learning health systems and AI-enabled

healthcare. Journal of Evaluation in Clinical Practice, 27. https://doi.org/10.1111/jep.13510

Kealey, E., Leckman-Westin, E. & Finnerty, M. T. (2013). Impact of four training conditions on physician use of a web-based clinical decision support system. Artificial intelligence in medicine,

(1), 39–44. https://doi.org/https://doi.org/10.1016/j.artmed.2013.03.003

Ketikidis, P., Dimitrovski, T., Lazuras, L. & Bath, P. A. (2012). Acceptance of health information

technology in health professionals: An application of the revised technology acceptance model.

Health Informatics Journal, 18(2), 124–134. https://doi.org/10.1177/1460458211435425

Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D.,

Liebig, T., Jirstrand, M., Urbann, O., Hedman, M., Lipponen, J. A., Bicciato, S., Radan, A.-P.,

Valdivieso, B., Thronicke, W., Gunopulos, D. & Delgado-Gonzalo, R. (2021). Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European

Hospitals. Healthcare, 9(8). https://doi.org/10.3390/healthcare9080961

Lai, P. (2017). The Literature Review of Technology Adoption models and theories for the novelty

technology. Journal of Information Systems and Technology Management, 14(1). https://doi.

org/10.4301/s1807-17752017000100002

Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N. & Pinto dos Santos,

D. (2021). Use and Control of Artificial Intelligence in Patients Across the Medical Workflow:

Single-Center Questionnaire Study of Patient Perspectives. J Med Internet Res, 23(2), e24221.

https://doi.org/10.2196/24221

Loncaric, F., Camara, O., Piella, G. & Bijnens, B. (2020). Integration of artificial intelligence into

clinical patient management: focus on cardiac imaging. Revista Española de Cardiología

(English Edition), 74. https://doi.org/10.1016/j.rec.2020.07.003

McParland, A. & Grant, K. (2019). Applications of artificial intelligence in emergency medicine.

University of Toronto medical journal, 96.

Mendelson, E. B. (2019). Artificial Intelligence in Breast Imaging: Potentials and Limitations. American Journal of Roentgenology, 212(2), 293–299. https://doi.org/10.2214/AJR.18.20532

Mohammadzadeh, N., Safdari, R. & Rahimi, A. (2013). Multi-Agent Systems: Effective Approach for

Cancer Care Information Management. Asian Pacific journal of cancer prevention : APJCP,

, 7757–9. https://doi.org/10.7314/APJCP.2013.14.12.7757

Mysona, D., Kapp, D., Rohatgi, A., Lee, D., Mann, A., Tran, P., Tran, L., She, J. & Chan, J. (2021).

Applying artificial intelligence to gynecologic oncology: A review [Funding Information: Dr

Chan discloses that he is a recipient of grant/research funding from Acerta, Aravive, Biodesix,

Clovis, Johnson Johnson, Oxigen, Genentech, Tesaro, AstraZeneca, Eisai, and Merck. This

project was supported by Denise Hale Chair and Fisher Family Fund from Dr John Chan. The

remaining authors, faculty, and staff in a position to control the content of this CME activity

have disclosed that they have no financial relationships with, or financial interests in, any

commercial organizations relevant to this educational activity. Publisher Copyright: © 2021

Lippincott Williams and Wilkins. All rights reserved.]. Obstetrical and Gynecological Survey,

(5), 292–301. https://doi.org/10.1097/ogx.0000000000000902

Nadarzynski, T., Miles, O., Cowie, A. & Ridge, D. (2019). Acceptability of artificial intelligence

(AI)-led chatbot services in healthcare: A mixed-methods study. DIGITAL HEALTH, 5,

https://doi.org/10.1177/2055207619871808

Nizam, V. & Aslekar, A. (2021). Challenges of Applying AI in Healthcare in India. Journal of

Pharmaceutical Research International, 33(36B), 203–209. https://doi.org/10.9734/jpri/2021/

v33i36B31969.

Padmanabhan, M., Yuan, P., Chada, G. & Nguyen, H. V. (2019). Physician-Friendly Machine Learning:

A Case Study with Cardiovascular Disease Risk Prediction. Journal of Clinical Medicine, 8(7).

https://doi.org/10.3390/jcm8071050

Palma, E. M., Santos, T. A. & Klein, A. (2021). Fatores que influenciam a aceitação de telemedicina

por médicos no Brasil. Revista Alcance Eletrônica, 28(1), 118–138. https://doi.org/https:

//doi.org/10.14210/alcance.v28n1(jan/abr).p118-138

Passos, R. P., Vilela Junior, G. & Barros, V. (2018). INTELIGÊNCIA ARTIFICIAL NAS CIÊNCIAS

DA SAÚDE. CPAQV Journal, 10, 1. https://doi.org/10.36692/cpaqv-v10n1-1

Peixoto, M. R., Ferreira, J. B. & Oliveira, L. (2022). Drivers for Teleconsultation Acceptance in Brazil:

Patients’ Perspective during the COVID-19 Pandemic. Revista de Administração Contemporânea, 26(2).

Poon, A. & Sung, J. (2021). Opening the black box of AI-Medicine. Journal of Gastroenterology and

Hepatology, 36, 581–584. https://doi.org/10.1111/jgh.15384

Prakash, A. V. & Das, S. (2021). Medical practitioner’s adoption of intelligent clinical diagnostic

decision support systems: A mixed-methods study. Information Management, 58(7), 103524.

https://doi.org/https://doi.org/10.1016/j.im.2021.103524

Rahimi, B., Nadri, H., Afshar, H. L. & Timpka, T. (2018). A Systematic Review of the Technology

Acceptance Model in Health Informatics. Applied Clinical Informatics, 09(03), 604–634.

https://doi.org/10.1055/s-0038-1668091

Scherer, R., Siddiq, F. & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic

structural equation modeling approach to explaining teachers’ adoption of digital technology

in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.

Schwartz III, J., Gao, M., Geng, E., Mody, K., Mikhail, C. & Cho, S. (2019). Applications of Machine

Learning Using Electronic Medical Records in Spine Surgery. Neurospine, 16, 643–653.

https://doi.org/10.14245/ns.1938386.193

Shinners, L., Aggar, C., Grace, S. & Smith, S. (2019). Exploring healthcare professionals’ understanding and experiences of artificial intelligence technology use in the delivery of healthcare: An

integrative review. Health Informatics Journal, 26, 146045821987464. https://doi.org/10.1177/

Spänig, S., Emberger-Klein, A., Sowa, J.-P., Canbay, A., Menrad, K. & Heider, D. (2019). The

Virtual Doctor: An Interactive Clinical-Decision-Support System based on Deep Learning

for Non-Invasive Prediction of Diabetes. Artificial Intelligence in Medicine, 100, 101706.

https://doi.org/10.1016/j.artmed.2019.101706

Tarakji, K., Silva, J., Chen, L., Turakhia, M., Perez, M., Attia, Z., Passman, R., Boissy, A., Cho, D.,

Majmudar, M., Mehta, N., Wan, E. & Chung, M. (2020). Digital Health and the Care of the

Arrhythmia Patient; What Every Electrophysiologist Needs to Know. Circulation. Arrhythmia

and electrophysiology, 13. https://doi.org/10.1161/CIRCEP.120.007953

The Lancet Digital Health. (2021). Artificial intelligence for COVID-19: saviour or saboteur? The

Lancet Digital Health, 3(1), e1. https://doi.org/10.1016/s2589-7500(20)30295-8

Ting, D., Pasquale, L., Peng, L., Campbell, J., Lee, A., Raman, R., Tan, G., Schmetterer, L., Keane,

P. & Wong, T. Y. (2018). Artificial intelligence and deep learning in ophthalmology. British

Journal of Ophthalmology, 103, bjophthalmol–2018. https://doi.org/10.1136/bjophthalmol2018-313173

Torous, J., Bucci, S., Bell, I., Kessing, L., Faurholt-Jepsen, M., Whelan, P., Carvalho, A., Keshavan, M.

& Firth, J. (2021). The growing field of digital psychiatry: current evidence and the future of

apps, social media, chatbots, and virtual reality. World Psychiatry, 20. https://doi.org/10.1002/

wps.20883.

Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T.,

Nguyen, S. H., Tran, B. X., Latkin, C. A., Ho, R. C. M. & Ho, C. S. H. (2021). Determinants of

Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective

Physicians. Frontiers in Public Health, 9, 1752. https://doi.org/10.3389/fpubh.2021.755644

Tziortziotis, I., Laskaratos, F.-M. & Coda, S. (2021). Role of Artificial Intelligence in Video Capsule

Endoscopy. Diagnostics, 11. https://doi.org/10.3390/diagnostics11071192

Venugopal, P., Priya, S. A., Manupati, V. K., Varela, M. L. R., Machado, J. & Putnik, G. D. (2018).

Impact of UTAUT Predictors on the Intention and Usage of Electronic Health Records and

Telemedicine from the Perspective of Clinical Staffs. Innovation, Engineering and Entrepreneurship (pp. 172–177). Springer International Publishing. https://doi.org/10.1007/978-3-319-

-6_24

Vourgidis, I., Mafuma, S. J., Wilson, P., Carter, J. & Cosma, G. (2019). Medical Expert Systems –

A Study of Trust and Acceptance by Healthcare Stakeholders. Em A. Lotfi, H. Bouchachia,

A. Gegov, C. Langensiepen & M. McGinnity (Ed.), Advances in Computational Intelligence

Systems (pp. 108–119). Springer International Publishing.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health. https:

//www.who.int/publications/i/item/9789240029200 {ISBN} 9789240029200

Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., Beaty, K., Dehan, E. & Parikh,

B. (2019). Translating cancer genomics into precision medicine with artificial intelligence:

applications, challenges and future perspectives. Human Genetics, 138, 1–16. https://doi.org/

1007/s00439-019-01970-5

Yamada, K. & Mori, S. (2019). The day when computers read between lines. Japanese journal of

radiology, 37, 3. https://doi.org/10.1007/s11604-019-00833-3

Yang, Y. J. & Bang, C. S. (2019). Application of artificial intelligence in gastroenterology. World

journal of gastroenterology, 25(14), 1666–1683. https://doi.org/https://doi.org/10.3748/wjg.

v25.i14.1666

Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B. & Cheng, Y. (2019). Psychosocial Factors Affecting

Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study. J Med Internet

Res, 21(10), e14316. https://doi.org/10.2196/14316

Zhai, H., Yang, X., Xue, J., Lavender, C., Ye, T., Li, J.-B., Xu, L., Lin, L., Cao, W. & Sun, Y. (2021).

Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring

Technology: Model Development and Questionnaire Study. J Med Internet Res, 23(9), e27122.

Publicado

30-04-2022

Como Citar

1.
Susiane dos Santos Pereira K, Reis Armond de Melo D, Chaves Vilela Junior D, Goncalves Rodrigues L. Fatores que influenciam a aceitação de Tecnologias de Inteligência Artificial na Saúde. Rev. G&S [Internet]. 30º de abril de 2022 [citado 20º de abril de 2024];13(01):02-20. Disponível em: https://periodicos.unb.br/index.php/rgs/article/view/41552

Edição

Seção

Artigos de Revisão