Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future...

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Primary Care Research

Background:

• Artificial intelligence (AI) shows promise for enhancing clinical decision-making and patient outcomes in primary care.
• The current extent of AI integration into outpatient primary care settings is not well-defined.
• This review investigates AI's role, trials, and integration within non-urgent primary care.

Purpose of the Study:

• To conduct a scoping review on the implementation and application of artificial intelligence in outpatient primary care settings.
• To understand the current landscape of AI research and real-world use in primary care.
• To identify gaps in AI application and implementation within primary care.

Main Methods:

• A comprehensive scoping review following Joanna Briggs Institute methodology and PRISMA-ScR guidelines.
• Searches of MEDLINE, CINAHL, Scopus, and clinicaltrials.gov databases for peer-reviewed articles (Jan 2019-Nov 2024).
• Thematic analysis to synthesize findings on AI application domains, research stage, and implementation status.

Main Results:

• 61 studies met eligibility criteria from 3203 screened manuscripts.
• Most studies focused on AI model development (43%), with few reporting clinical trial results (8%).
• AI applications identified included provider support and radiological diagnosis; minimal real-world implementations (11 studies) were noted, primarily in ambient scribing and decision support.

Conclusions:

• Artificial intelligence in primary care is predominantly in the developmental phase.
• Real-world applications of AI in primary care are currently limited.
• Further research and development are needed to realize AI's full potential in primary care settings.