Google Scholar | Citations: 16429| h-index: 32 | i10-index: 482
The use of Artificial Intelligence in Medicine: the benefits and challenges of the human-technology partnership in health
PDF (Portuguese (Brazil))

Keywords

Artificial Intelligence
Intelligent systems
Medical computer science
Health Informatics
Computer-aided diagnosis

How to Cite

The use of Artificial Intelligence in Medicine: the benefits and challenges of the human-technology partnership in health. (2024). Electronic Journal Collection Health, 24(12), e18374. https://doi.org/10.25248/reas.e18374.2024

Copyright © | All rights reserved.

The journal holds the exclusive copyright for the publication of this article under the terms of Brazilian law 9610/98.

Partial reproduction
The use of parts of the texts, figures and questionnaire of the article is free, being mandatory the citation of the authors and journal.

Total reproduction
It is expressly prohibited and must be authorized by the journal.

Abstract

Objective: Describe the ways in which Artificial Intelligence (AI) tools influence Medicine, their benefits and challenges, showcasing existing tools in the field and broadly addressing both patient care and the daily life of healthcare professionals. Bibliographic review: The benefits of AI are significant, predominantly in the areas of early diagnosis through image analysis, personalized treatment planning, prognostic prediction, health management, and contributions to a more effective Medicine, always in partnership with human professionals. Additionally, various tools, both in development and already available for popular use, were presented, as well as the companies responsible for each and their respective functions, to exemplify the existing technological advances. However, there are still ethical, legal, and operational difficulties that need to be overcome for the effective and widespread integration of AI-based technologies in practice. Final considerations: The impact of AI on Medicine is undeniable, making it essential to conduct more clinical studies that prove and deepen the understanding of the influence of this new technology, ensuring that its benefits are maximized and its challenges adequately addressed.

PDF (Portuguese (Brazil))

References

ARAHAM SB, et al. Improved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive Alerts. Journal of Diabetes Science and Technology, 2019; 15(1): 193229681985933.

ABRÀMOFF MD, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 2018; 1(1).

AUNG YYM, et al. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 2021; 139(1): 4–15.

BEARD JR e BLOOM DE. Towards a comprehensive public health response to population ageing. The Lancet, 2015; 385(9968): 658–661.

BELK RW, et al. Key concepts in artificial intelligence and technologies 4.0 in services. Service Business, 2023; 17(1): 1–9.

BRIGANTI G e MOINE OL. Artificial Intelligence in Medicine: Today and Tomorrow. Frontiers in Medicine, 2020; 7(27).

BUMGARNER JM, et al. Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. Journal of the American College of Cardiology, 2018; 71(21): 2381–2388.

CASSIANO B. Alagoas recebe mais R$ 2 milhões para informatização de equipes de saúde. Agência Saúde. 2020. Disponível em: https ://www.gov.br/saude/pt-br/assuntos/noticias/2020/fevereiro/alagoas-recebe-mais-r-2-milhoes-para-informatizacao-de-equipes-de-saude. Acessado em: 20 de junho de 2024.

CHAKRABORTY J, et al. Bias in machine learning software: why? how? what to do? Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021.

CHENG JZ, et al. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports, 2016; 6(1).

CIELO AC, et al. Implantação da Estratégia e-SUS Atenção Básica: uma análise fundamentada em dados oficiais. Revista de Saúde Pública, 2022; 56: 5.

DAVE M e PATEL N. Artificial intelligence in healthcare and education. British Dental Journal, 2023; 234(10): 761–764.

DERCLE L, et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. Journal for ImmunoTherapy of Cancer, 2022; 10(9): 5292.

DILSIZIAN SE e SIEGEL EL. Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment. Current Cardiology Reports, 2013; 16(1).

DOURADO DA e AITH FMA. A regulação da inteligência artificial na saúde no Brasil começa com a Lei Geral de Proteção de Dados Pessoais. Revista de Saúde Pública, 2022; 56(80).

GULSHAN V, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 2016; 316(22): 2402.

GUPTA R, et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity, 2021; 25(3): 1–46.

HARDESTY L. Study finds gender and skin-type bias in commercial artificial-intelligence systems. Revista MIT News, 2018. Disponível em https ://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212. Acessado em: 23 de fevereiro de 2024.

HOLLON TC, et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine, 2020; 26(1): 52–58.

JOHNSON KB, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 2020; 14(1): 86–93.

KANG DY, et al. Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 2020; 28(1).

KAUL V, et al. History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 2020; 92(4): 807–812.

LEMES MM e LEMOS ANLE. O uso da inteligência artificial na saúde pela Administração Pública brasileira. Cadernos Ibero-Americanos de Direito Sanitário, 2020; 9(3): 166–182.

LIYANAGE H, et al. Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of Medical Informatics, 2019; 28(1): 41–46.

MORAIS DMG, et al. O conceito de inteligência artificial usado no mercado de softwares, da educação tecnológica e na literatura científica. Educação Profissional e Tecnológica em Revista, 2020; 4(2): 98–109.

NUNES HC, et al. Desafios bioéticos do uso da inteligência artificial em hospitais. Revista Bioética, 2022; 30: 82–93.

PI-SSEx. PAINEL DE INDICADORES DO SISTEMA DE SAÚDE DO EXÉRCITO. Brasília, 2019. Disponível em: http ://www.centraldeservicos.dsau.eb.mil.br/index.php/pt-br/painel-de-indicadores. Acessado em: 20 de junho de 2024.28. POWLES J e HODSON H. Google DeepMind and healthcare in an age of algorithms. Health and Technology, 2017; 7(4): 351–367.

PUPIC N, et al. An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS digital health, 2023; 2(11): 255.

RACITI P, et al. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology, 2020; 33(10): 2058–2066.

RAGHUNATH S, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature Medicine, 2020; 26(6): 886–891.

REGALIA G, et al. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Research, 2019; 153: 79–82.

RIBEIRO AH, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communications, 2020; 11(1).

SOARES RA, et al. O uso da inteligência artificial na medicina: aplicações e benefícios. Research, Society and Development, 2023; 12(4): 5012440856.

THIAGARAJAN JJ, et al. DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms. Scientific Reports, 2020; 10(1).

WANG X, et al. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

ZAPAROLLI D. A inteligência artificial chega à saúde. Revista Pesquisa FAPESP. Disponível em: https ://revistapesquisa.fapesp.br/a-inteligencia-artificial-chega-a-saude/. Acessado em: 23 de fevereiro de 2024. 2022; 332.

ZUCOLOTTO TE, et al. A inteligência artificial na medicina: aplicações atuais e potenciais. Brazilian Journal of Health Review, 2023; 6(6): 31237–31247.