Training Community Health Agents (CHA) with Artificial Intelligence (AI)

Abstract

Artificial Intelligence (AI) has been used in the training of community health agents (CHAs) to optimize professional development and improve the quality of care and data collection in primary healthcare. This systematic review, with a qualitative approach, aimed to assess the impacts of this technology on CHA education and practice. The research was conducted in the PubMed and Scopus databases using the PICo strategy and DeCS/MeSH descriptors. After applying inclusion and exclusion criteria, 1,202 articles were selected for screening, resulting in the analysis of four full studies that met the selection criteria. The results indicated that AI enabled more dynamic and personalized training, facilitating the collection of a larger volume of data, reducing clinical errors, and optimizing consultation time. In middle- and low-income countries, AI-mediated learning platforms allowed large-scale training, adapting to regional needs. Additionally, the implementation of AI-assisted teleconsultations reduced unnecessary referrals and improved CHA access to remote communities.These findings align with previous studies highlighting AI’s potential to transform healthcare education and enhance service effectiveness. Strengthening these technologies and addressing their barriers may contribute to building a healthcare system with greater equity, transparency, and efficiency.