Application Of Artificial Intelligence In Community-Based Primary Health Care: Systematic Review

Abstract

Background: As artificial intelligence (AI) continues to reshape healthcare, its potential within community-based primary health care settings remains a subject of growing interest. This study seeks to comprehensively explore the impact of AI technology on patient outcomes, resource allocation, and healthcare delivery efficiency in diverse populations receiving care in such settings.

Aim: The aim of this study is to investigate how the application of AI technology compares to traditional methods in community-based primary health care, with a focus on patient outcomes and healthcare delivery.

Method: A systematic approach was employed to identify and select relevant studies from key databases. Inclusion criteria ensured the consideration of studies published within the last 10 years that explored AI's role in community-based primary health care. Data extraction and analysis were conducted to synthesize findings from 11 selected studies.

Results: The synthesis revealed a spectrum of AI integration approaches, from bias mitigation protocols to evaluative studies of disease screening and self-management interventions. These studies showcased the potential of AI to enhance healthcare equity, accuracy, and efficiency. However, limitations including study selection bias were acknowledged.

Conclusion: The study concludes that AI holds significant promise in transforming community-based primary health care, offering opportunities to address bias, improve disease management, and enhance patient self-management. The findings underscore the need for evidence-based decisions and inclusive research to guide strategic AI implementation.