Machine Learning in Primary Health Care: The Research Landscape

Area of Science:

• Health Informatics
• Artificial Intelligence
• Machine Learning

Background:

• Digital transformation in healthcare relies on AI and machine learning.
• These technologies aim to enhance primary health system efficiency, effectiveness, equity, and responsiveness.
• The study focuses on the role of AI and ML in primary healthcare services.

Purpose of the Study:

• To identify leading countries, institutions, and funding sponsors in AI and ML for primary healthcare.
• To analyze publication trends and key research themes.
• To understand the application of AI and ML in addressing diagnostic challenges and improving patient care.

Main Methods:

• Synthetic knowledge synthesis.
• Bibliometric analysis.
• Thematic analysis.

Main Results:

• The United States and the United Kingdom are the most productive countries.
• Key journals include Plos One and BJM Open; leading funders are the National Institutes of Health (USA) and the National Natural Science Foundation of China.
• Publication trends show positive, exponential growth, with main themes including natural language processing for clinical decision-making, primary care optimization, social determinants of health, and chatbot applications.

Conclusions:

• Machine learning in primary care seeks to reduce missed diagnostic opportunities.
• AI applications aim to minimize adverse patient effects.
• The research highlights the growing impact of AI and ML on primary healthcare delivery and patient outcomes.