Research on the integration of artificial intelligence into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. 

The authors conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute scoping review framework and reported the findings according to PRISMA-ScR reporting guidelines. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met inclusion criteria. Machine learning, natural language processing, and expert systems were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks demonstrated the highest accuracy, considering the given database for the given clinical task. 

The authors observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings