Mancini Leticia Medeiros, Torres Luiz Eduardo Vanderlei, Coelho Jorge Artur P de M, da Fonseca Nichollas Botelho, Cordeiro Pedro Fellipe Dantas, Cavalcante Samara Silva Noronha, Dermeval Diego
Faculty of Medicine, Universidade Federal de Alagoas, Av. Lourival Melo Mota, S/n - Tabuleiro do Martins, Maceió, 57072-900, Brazil, 558232141461.
JMIR AI. 2025 Sep 10;4:e69547. doi: 10.2196/69547.
Artificial intelligence (AI) has the potential to transform global health care, with extensive application in Brazil, particularly for diagnosis and screening.
This study aimed to conduct a systematic review to understand AI applications in Brazilian health care, especially focusing on the resource-constrained environments.
A systematic review was performed. The search strategy included the following databases: PubMed, Cochrane Library, Embase, Web of Science, LILACS, and SciELO. The search covered papers from 1993 to November 2023, with an initial overview of 714 papers found, of which 25 papers were selected for the final sample. Meta-analysis data were evaluated based on three main metrics: area under the receiver operating characteristic curve, sensitivity, and specificity. A random effects model was applied for each metric to address study variability.
Key specialties for AI tools include ophthalmology and infectious disease, with a significant concentration of studies conducted in São Paulo state (13/25, 52%). All papers included testing to evaluate and validate the tools; however, only two conducted secondary testing with a different population. In terms of risk of bias, 10 of 25 (40%) papers had medium risk, 8 of 25 (32%) had low risk, and 7 of 25 (28%) had high risk. Most studies were public initiatives, totaling 17 of 25 (68%), while 5 of 25 (20%) were private. In limited-income countries like Brazil, minimum technological requirements for implementing AI in health care must be carefully considered due to financial limitations and often insufficient technological infrastructure. Of the papers reviewed, 19 of 25 (76%) used computers, and 18 of 25 (72%) required the Windows operating system. The most used AI algorithm was machine learning (11/25, 44%). The combined sensitivity was 0.8113, the combined specificity was 0.7417, and the combined area under the receiver operating characteristic curve was 0.8308, all with P<.001.
There is a relative balance in the use of both diagnostic and screening tools, with widespread application across Brazil in varied contexts. The need for secondary testing highlights opportunities for future research.
人工智能（AI）有潜力改变全球医疗保健状况，在巴西有广泛应用，尤其在诊断和筛查方面。
本研究旨在进行系统综述，以了解人工智能在巴西医疗保健中的应用，特别关注资源有限的环境。
进行了系统综述。检索策略包括以下数据库：PubMed、Cochrane图书馆、Embase、科学引文索引、拉丁美洲和加勒比卫生科学数据库以及科学电子图书馆在线。检索涵盖1993年至2023年11月的论文，初步共找到714篇论文，其中25篇被选入最终样本。基于三个主要指标评估荟萃分析数据：受试者工作特征曲线下面积、敏感性和特异性。对每个指标应用随机效应模型以处理研究变异性。
人工智能工具的关键专业领域包括眼科和传染病，大量研究集中在圣保罗州（13/25，52%）。所有论文都包括测试以评估和验证工具；然而，只有两篇对不同人群进行了二次测试。在偏倚风险方面，25篇论文中有10篇（40%）为中度风险，8篇（32%）为低风险，7篇（28%）为高风险。大多数研究是公共项目，共25篇中的17篇（68%），而25篇中的5篇（2%)是私人项目。在像巴西这样的低收入国家，由于财政限制和技术基础设施往往不足，在医疗保健中实施人工智能的最低技术要求必须仔细考虑。在审查的论文中，25篇中有19篇（76%）使用计算机，25篇中有18篇（72%）需要Windows操作系统。最常用的人工智能算法是机器学习（11/25，44%）。综合敏感性为0.8113，综合特异性为0.7417，受试者工作特征曲线下综合面积为0.8308，所有P值均<0.001。
诊断和筛查工具的使用相对平衡，在巴西各地的不同背景下广泛应用。二次测试的需求凸显了未来研究的机会。
