APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE PREDICTION AND EARLY DIAGNOSIS OF CHRONIC DISEASES
EMPHASIS ON DIABETES AND CARDIOVASCULAR DISEASES
DOI:
https://doi.org/10.65013/rms.v1i1.11Keywords:
Artificial Intelligence, Early Diagnosis, Diabetes Mellitus, Cardiovascular DiseasesAbstract
This study addresses the application of artificial intelligence (AI) in the prediction and early diagnosis of chronic diseases, focusing on diabetes and cardiovascular diseases (CVDs). The objective is to investigate how AI is used in the early prediction and diagnosis of diabetes and CVDs, contributing to improved health outcomes and resource optimization in public health. Through a systematic review, scientific articles were analyzed from the Virtual Health Library (VHL) and PUBMED databases, using inclusion criteria such as year of publication and suitability in English. The analysis included 20 relevant studies, highlighting AI's potential in identifying risk factors and personalizing treatments, despite challenges associated with infrastructure and clinical acceptance. It is concluded that integrating AI into public health requires collaboration among professionals, managers, and researchers to overcome technical barriers and leverage the benefits of this technology for diagnosing and treating chronic diseases.
References
AUNG, Yuri; WONG, David; TING, Daniel. The promise of artificial intelligence: a review of the
opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, v. 139, n.
1, p. 4–15, 2021. Disponível em: https://pubmed.ncbi.nlm.nih.gov/34405854/. Acesso em: 05 de
novembro de 2024;
BARRETT, Matthew; BOYNE, Josiane; BRANDTS, Julia et al. Artificial intelligence supported patient
self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised
care. The EPMA journal, v. 10, n. 4, p. 445– 464, 2019. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/31832118/. Acesso em: 05 de novembro de 2024;
BUCCHERI, Enrico; DELL’AQUILA, Daniele; RUSSO, Marco. Artificial intelligence in health data
analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale
screening tool for prediabetes and type 2 diabetes.
Diabetes Research and Clinical Practice, v. 174, n. 1, p. 108722–108722, 2021. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/33647331/. Acesso em: 05 de novembro de 2024;
ELSAYED, Nuha; ALEPPO, Grazia; ARODA, Vanita et al. Standards of medical care in diabetes - 2023.
Diabetes Care, v. 46, n. S1, p. S1-S326, 2023. Disponível em:
https://diabetesjournals.org/care/article/46/Supplement_1/S1/Standards-of-Medical- Care-in-Diabetes-
2023. Acesso em: 04 novembro 2024.
FENG, Yinhe; WANG, Yubin; ZENG, Chunfang et al. Artificial Intelligence and Machine Learning in
Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease. International
Journal of Medical Sciences, v. 18,
n. 13, p. 2871–2889, 2021. Disponível em: https://pubmed.ncbi.nlm.nih.gov/34220314/. Acesso em: 05
de novembro de 2024;
GADHIA, Vansh; LOYAL, Jaspreet. Review of Genetic and Artificial Intelligence approaches to
improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa. The Yale
journal of biology and medicine, v. 97, n. 1, p. 67– 72, 2024. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/38559462/. Acesso em: 05 de novembro de 2024;
GUAN, Zhouyu; LI, Huating; LIU, Ruhan et al. Artificial intelligence in diabetes management:
Advancements, opportunities, and challenges. Cell Reports Medicine: Cell Press, v. 4, n. 10, p. 1-15,
2021. Disponível em: https://www.sciencedirect.com/science/article/pii/S0168822721001695. Acesso
em: 04 novembro 2024.
HOYOS, William; HOYOS, Kenia; RUIZ-PÉREZ, Rander. Modelo de inteligencia artificial para la
detección temprana de diabetes. Biomedica, v. 43, n. Sp. 3, p. 110– 121, 2023. Disponível em:https://pubmed.ncbi.nlm.nih.gov/38207148/. Acesso em: 05 de novembro de 2024;
HUANG, Jingtong; YEUNG, Andrea; ARMSTRONG, David et al. Artificial Intelligence for Predicting and
Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology, v. 17, n. 1, p.
224–238, 2022. Disponível em: https://pubmed.ncbi.nlm.nih.gov/36121302/. Acesso em: 05 de
novembro de 2024;
HUANG, Xuan; WANG, Hui; SHE, Chongyang et al. Artificial intelligence promotes the diagnosis and
screening of diabetic retinopathy. Frontiers in Endocrinology, v. 13, n. 1, p. 1-17, 2022. Disponível
em: https://pubmed.ncbi.nlm.nih.gov/36246896/. Acesso em: 05 de novembro de 2024;
ISAKSEN, Jonas; BAUMERT, Mathias; HERMANS, Astrid et al. Artificial intelligence for the detection,
prediction, and management of atrial fibrillation.
Herzschrittmachertherapie + Elektrophysiologie, v. 33, n. 1, p. 34–41, 2022. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/35147766/. Acesso em: 05 de novembro de 2024;
KITTLESON, Michelle; PANJRATH, Gurusher; AMANCHERLA, Kaushik et al. 2023 ACC Expert
Consensus Decision Pathway on the Management of Cardiovascular Disease in Patients with Diabetes.
Journal American of Cardiology, v. 81, n. 18, p. 1835-1878, 2023. Disponível em:
https://www.acc.org/guidelines. Acesso em: 04
novembro 2024.
KUMAR, Yogesh; KOUL, Apeksha; SINGLA, Ruchi et al. Artificial Intelligence in Disease diagnosis: a
Systematic Literature review, Synthesizing Framework and Future Research Agenda. Journal of
Ambient Intelligence and Humanized Computing, v. 14, n. 7, p. 8459–8486, 2022. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/35039756/. Acesso em: 05 de novembro de 2024;
KURNIAWAN, Moh; HANDIYANI, Hanny; NURAINI, Tuti et al. A systematic review of artificial
intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Journal of
Medicine, v. 56, n. 1, p. 1-14, 2024. Disponível em: https://pubmed.ncbi.nlm.nih.gov/38466897/. Acesso
em: 05 de novembro de 2024;
MARX, Nikolaus; FEDERICI, Massino; SCHUTT, Katharina et al. 2023 ESC Guidelines for the
management of cardiovascular diseases in diabetes. European Heart Journal, v, 44, n. 39, p. 4043-
4140, 2023. Disponível em: https://academic.oup.com/eurheartj/article/44/39/4043/7238227. Acesso
em: 04 novembro 2024.
NOMURA, Akihiro; NOGUCHI, Masahiro; KOMETANI, Mitsuhiro et al. Artificial Intelligence in Current
Diabetes Management and Prediction. Current Diabetes Reports, v. 21, n. 12, p. 3-9, 2021. Disponível
em: https://pmc.ncbi.nlm.nih.gov/articles/PMC8668843/. Acesso em: 05 de novembro de 2024;
ORGANIZAÇÃO MUNDIAL DA SAÚDE. World health statistics 2021. 2021. Disponível em:
https://www.who.int/data/gho/publications/world-health-statistics. Acesso em: 04 novembro 2024.
PELLY, Melissa; FATEHI, Farhad LIEW, Danny et al. Artificial intelligence for secondary prevention of
myocardial infarction: A qualitative study of patient and health professional perspectives. International
Journal of Medical Informatics, v. 173, n. 1, p. 105041, 2023. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/36934609/. Acesso em: 05 de novembro de 2024;
REDDY, Charitha; EYNDE, Jef; KUTTY, Shelby. Artificial intelligence in perinatal diagnosis and
management of congenital heart disease. Seminars in Perinatology,
v. 46, n. 4, p. 151588, 2022. Disponível em: https://pubmed.ncbi.nlm.nih.gov/35396036/. Acesso em:
05 de novembro de 2024;
SRINIVASU, Naga; AHMED, Shakeel; HASSABALLAH, Mahmoud et al. An explainable Artificial
Intelligence software system for predicting diabetes. Heliyon, p. e36112–e36112, 2024. Disponível em:
https://pesquisa.bvsalud.org/portal/resource/pt/mdl-39253141. Acesso em: 05 de novembro de 2024;
SUBRAMANIAN, Murugan; WOJTUSCISZYN, Anne; FAVRE, Lucie et al. Precision medicine in the era
of artificial intelligence: implications in chronic disease management. Journal of Translational Medicine, v. 18, n. 1, p. 1-9, 2020.
Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC7725219/#. Acesso em: 05 de novembro de
2024;
TRUSLOW, James; GOTO, Shinichi; HOMILIUS, Max. et al. Cardiovascular Risk Assessment Using
Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors. Circulation:
Cardiovascular Quality and Outcomes, v. 15, n. 6, jun. 2022. Disponível em:
https://pubmed.ncbi.nlm.nih.gov/35477255/. Acesso em: 05 de novembro de 2024;
YASMIN, Farah; SHAH, Syed; NAEEM, Aisha et al. Artificial intelligence in the diagnosis and detection
of heart failure: the past, present, and future. Reviews in Cardiovascular Medicine, v. 22, n. 4, p.
1095, 2021. Disponível em: https://pubmed.ncbi.nlm.nih.gov/34957756/. Acesso em: 05 de novembro
de 2024.
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