APLICAÇÕES DE BANCO DE DADOS, TELEMEDICINA E INTELIGÊNCIA ARTIFICIAL NA SAÚDE DE LOCALIDADES REMOTAS: UMA REVISÃO SISTEMÁTICA

Abstract

The growing demand for equitable access to healthcare, especially in remote regions, drives the integration of telemedicine and artificial intelligence (AI). This study addresses the gap in a comprehensive synthesis on the synergy between robust databases, telemedicine, and AI to optimize healthcare service delivery in challenging contexts. We conducted a systematic review following the PRISMA guidelines, consulting Semantic Scholar, CrossRef, PubMed, and arXiv with specific keywords. The selection resulted in 10 articles exploring aspects such as data security, remote monitoring, outcome assessment, and innovative applications. The findings reveal that the security and integrity of medical data are paramount, with proposals for watermarking and encryption. AI and IoT enhance diagnosis and monitoring, while the assessment of patient-reported outcomes (PROMs/PREMs) validates the effectiveness of interventions. We conclude that the effective integration of secure databases, telemedicine, and AI is fundamental to overcoming geographical barriers and improving the quality of healthcare in remote areas, but challenges such as interoperability and privacy persist. Future research should focus on scalable implementation models and large-scale clinical validation.

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