Diagnostic and Screening AI Tools in Brazil's Resource-Limited Settings: Systematic Review

Abstract

Background: Artificial intelligence (AI) has the potential to transform global health care, with extensive application in Brazil, particularly for diagnosis and screening.

Objective: This study aimed to conduct a systematic review to understand AI applications in Brazilian health care, especially focusing on the resource-constrained environments.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Brazil; PRISMA; artificial intelligence; diagnosis; screening.

Figure 1.. PRISMA study selection flowchart. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Figure 2.. Forest plot sensitivity.

Figure 3.. Forest plot specificity.

Figure 4.. Forest plot for area under the receiver operating characteristic (ROC) curve.