Artificial intelligence-based clinical decision support: scoping review

Authors

DOI:

https://doi.org/10.59681/2175-4411.v17.2025.1457

Keywords:

Clinical Decision Support Systems, Artifical Intelligence, Machine Learning

Abstract

Artificial intelligence (AI) seeks to replicate human cognitive functions and its application in healthcare is constantly evolving. This scoping review analyzed the use of AI-based Clinical Decision Support Systems (CDSS). The search was conducted in the PubMed, SciELO and LILACS databases, considering articles from the last three years. Among the 77 articles found, 10 met the inclusion criteria. The results highlight the advancement of AI-based CDSS, showing superior performance to humans in many cases. Countries around the world are already using AI to support clinical diagnoses, with promising results that indicate improvements in the quality, efficiency and effectiveness of healthcare decisions. The increasing adoption of AI in clinical practice suggests its potential to transform the sector, offering more accurate diagnoses, personalized treatments and better care management, consolidating it as an essential tool for the future of healthcare.

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Author Biography

Antonio Valério Netto, Universidade Federal de São Paulo

Atualmente é professor afiliado junto ao departamento de Informática em Saúde da Escola Paulista de Medicina (EPM/UNIFESP). E professor de pós-graduação nos cursos de especialização em ciência de dados para saúde, gestão e liderança em enfermagem, além do MBA em saúde digital. Desde 2011 é pesquisador bolsista do CNPq em Desenvolvimento Tecnológico e Extensão Inovadora (DT). É Pós-doutor na área de biotelemetria e telemonitoramento pelo Instituto de Ensino e Pesquisa do Hospital Sírio-Libanês. Doutor em computação e matemática computacional pela USP. Especialista em informática em saúde pela UNIFESP. Possui MBA em marketing pela FUNDACE (FEA-RP/USP). É técnico em informática industrial pela ETEP, bacharel em ciência da computação pela Universidade Federal de São Carlos (UFSCar) e mestre em engenharia na área de simulação virtual pela USP. Em 2001 foi pesquisador visitante na School of Optometry at Indiana University (EUA). Entre 2019 e 2021 foi professor visitante em health data science e telemedicina na Escola Paulista de Medicina (EPM/UNIFESP). Trabalhou com inovação e tecnologia no IAMSPE (Instituto de Assistência Médica ao Servidor Público Estadual), HCFMUSP (Hospital da Clínicas da Faculdade de Medicina da USP), Opto Eletrônica e na T-Systems. Em 2003, fundou a Cientistas Desenvolvimento Tecnológico, empresa focada no desenvolvimento de sistemas computacionais que em 2009 foi considerada pelo Sebrae SP uma das pequenas empresas mais inovadoras do estado de São Paulo. Em 2007, fundou a XBot, primeira empresa de robótica móvel do país para as áreas de educação, pesquisa e edutainment, que em 2011 foi uma das vencedoras do prêmio nacional de empreendedorismo e em 2012 recebeu o Prêmio MPE Brasil Estadual São Paulo de destaque em boas práticas de responsabilidade social. É avaliador ad-hoc do CNPq, da Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE), da Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB) e Assessor Científico do Fundo Mackenzie de Pesquisa. Possui mais de 100 publicações entre livros, capítulos de livros, revistas e congressos internacionais e nacionais nas áreas de computação e engenharia. Possui oito pedidos de patentes e seis registros de marcas. Coordenou em torno de 35 projetos tecnológicos financiados pela FINEP, CNPq, FAPESP, ABDI e empresas privadas. Recebeu diversos prêmios e menções honrosas, como a do Society of Automotive Engineer (SAE) Brasil 2001 - melhor artigo na categoria Projetos e de melhor aluno do MBA em Marketing da FUNDACE em 2006. Em 2008 foi finalista do prêmio Empreendedor de Sucesso promovido pela revista PEGN e FGV. Em 2009 tornou-se professor honorário da Universidad Abierta Interamericana (Buenos Aires/ARG). Em 2013, ganhou o Prêmio Alexandrino Garcia do Grupo Algar na categoria Empreendedorismo pelo trabalho realizado na área de tecnologia educacional. Em 2016 recebeu o prêmio ABSEG da Associação Brasileira de Profissionais de Segurança. Em 2019 foi vencedor do Concurso de Tecnologias Policiais (StartPol). Em 2023, obteve primeiro lugar no prêmio UNIDAS (União Nacional das Instituições de Autogestão em Saúde).

References

Toh C, P. Brody J. Applications of Machine Learning in Healthcare. In: Smart Manufacturing - When Artificial Intelligence Meets the Internet of Things. IntechOpen; 2021. Disponível em: https://www.intechopen.com/chapters/72044. Acesso em: 22 out. 2024

Khosravi M, Zare Z, Mojtabaeian SM, Izadi R. Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews. Vol. 11, Health Services Research and Managerial Epidemiology. SAGE Publications Inc.; 2024. Disponível em: https://journals.sagepub.com/doi/full/10.1177/23333928241234863. Acesso em: 22 out. 2024

Miller RA. Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc [Internet]. 1994. Disponível em: https://academic.oup.com/jamia/article-abstract/1/1/8/849986?redirectedFrom=fulltext. Acesso em: 22 out. 2024

Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med. 2021 Jun 11;2(6):642–65. Disponível em: https://www.cell.com/med/fulltext/S2666-6340(21)00155-0. Acesso em: 22 out. 2024

Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA [Internet]. 2017 Dec 12. Disponível em: https://jamanetwork.com/journals/jama/fullarticle/2665775. Acesso em: 18 nov. 2024

Osheroff JA, Pifer EA, Teich JM, Sittig DF, Jenders RA, Society. HI and MS. Improving outcomes with clinical decision support : an implementer’s guide. Chicago, IL : HIMSS,; 2005. xii, 136 pages. Disponível em: https://doi.org/10.4324/9780367806125HIMSS. Acesso em: 18 nov. 2024

Lobo LC. Inteligência Artificial e Medicina. Rev Bras Educ Med. 2017 Jun;41(2):185–93. Disponível em: https://www.scielo.br/j/rbem/a/f3kqKJjVQJxB4985fDMVb8b/?lang=pt. Acesso em: Acesso em: 22 out. 2024

Wang L, Chen X, Zhang L, Li L, Huang Y, Sun Y, et al. Artificial intelligence in clinical decision support systems for oncology. Int J Med Sci [Internet]. 2023. Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC9812798/. Acesso em: 15 de nov. 2024

Syrowatka A, Song W, Amato MG, Foer D, Edrees H, Co Z, et al. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health [Internet]. 2022 Feb 1;4(2):e137–48. Disponível em: https://www.thelancet.com/journals/landig/article/PIIS25897500(21)00229-6/fulltext. Acesso em: 18 nov. 2024

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Vol. 169, Annals of Internal Medicine. American College of Physicians; 2018. p. 467–73. Disponível em: https://www.acpjournals.org/doi/10.7326/M18-0850. Acesso em: 22 out. 2024

Liu H, Chen Y, Zhang Y, Wang L, Luo R, Wu H, et al. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur Radiol. 2021 Aug 1;31(8):5902–12. Disponível em: https://link.springer.com/article/10.1007/s00330-020-07659-y. Acesso em: 10 de out. 2024

Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, et al. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. British Journal of Dermatology. 2024 Jul 1;191(1):125–33. Disponível em: https://academic.oup.com/bjd/article/191/1/125/7564904?login=false. Acesso em: 10 de out. 2024

Schwab K, Nguyen D, Ungab G, Feld G, Maisel AS, Than M, et al. Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non-valvular atrial fibrillation in the emergency department. J Am Coll Emerg Physicians Open [Internet]. 2021 Aug 1;2(4):e12534. Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC8353018/. Acesso em: 10 de out. 2024

Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021 May 1;27(5):815–9. Disponível em: https://www.nature.com/articles/s41591-021-01335-4. Acesso em: 10 de out. 2024

Lee HW, Jin KN, Oh S, Kang SY, Lee SM, Jeong IB, et al. Artificial Intelligence Solution for Chest Radiographs in Respiratory Outpatient Clinics: Multicenter Prospective Randomized Clinical Trial. Ann Am Thorac Soc. 2023 May 1;20(5):660–7. Disponível em: https://www.atsjournals.org/doi/full/10.1513/AnnalsATS.202206-481OC. Acesso em: 10 de out. 2024

Seol HY, Shrestha P, Muth JF, Wi C Il, Sohn S, Ryu E, et al. Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. PLoS One. 2021 Aug 1;16(8 August). Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255261. Acesso em: 10 de out. 2024

Jian MJ, Lin TH, Chung HY, Chang CK, Perng CL, Chang FY, et al. Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning. Infect Drug Resist [Internet]. 2024;17:2899–912. Disponível em: https://pmc.ncbi.nlm.nih.gov/articles/PMC11246630/. Acesso em: 10 de out. 2024

Szlejf C, Batista AFM, Bertola L, Lotufo PA, Benseñor IM, Chiavegatto Filho ADP, et al. Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study. Brazilian Journal of Medical and Biological Research. 2023;56. Disponível em: https://www.scielo.br/j/bjmbr/a/r9zmCWchBzPTmZTGMC8qwpR/#. Acesso em: 10 de out. 2024

Gong EJ, Bang CS, Lee JJ, Baik GH, Lim H, Jeong JH, et al. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: Development and validation study. Endoscopy. 2022 Sep 3;55(8):701–8. Disponível em: https://www.thiemeconnect.com/products/ejournals/abstract/10.1055/a-2031-0691. Acesso em: 10 de out. 2024

Meinikheim M, Mendel R, Palm C, Probst A, Muzalyova A, Scheppach MW, et al. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial. Endoscopy. 2024 Sep 1;56(9):641–9. Disponivel em: https://www.thiemeconnect.com/products/ejournals/abstract/10.1055/a-2296-569. Acesso em: 10 de out. 2024

Shortliffe EH, Sepúlveda MJ. Clinical Decision Support in the Era of Artificial Intelligence. JAMA [Internet]. 2018 Dec 4;320(21):2199–200. Disponível em: https://jamanetwork.com/journals/jama/article-abstract/2713901. Acesso em: 18 de nov. 2024

Sadeghi Z, Alizadehsani R, CIFCI MA, Kausar S, Rehman R, Mahanta P, et al. A review of Explainable Artificial Intelligence in healthcare. Computers and Electrical Engineering. 2024 Aug 1;118:109370. Disponível em: https://www.sciencedirect.com/science/article/pii/S0045790624002982?via%3Dihub. Acesso em: 14 de dez. 2024

Tenório JM, Sousa FS, Pisa IT. Classificação de intervenções, aplicações e serviços em saúde digital para o contexto brasileiro (CDHI.br). São Paulo: 2023. Licença: CC BY-NC-AS 4.0 DEED. Disponível em https://cdhi.saude360.app.br Acesso em: 14 out. 2024.

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Published

2025-12-16

How to Cite

Semedo Correia, E. A., & Valério Netto, A. (2025). Artificial intelligence-based clinical decision support: scoping review. Journal of Health Informatics, 17(1). https://doi.org/10.59681/2175-4411.v17.2025.1457

Issue

Vol. 17 (2025)

Section

Review

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Copyright (c) 2025 Engels Avelino Semedo Correia, Antonio Valério Netto

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