Ethical concerns and algorithmic fairness of artificial intelligence applications in primary care: a bibliometric and co-word analysis
1
Peking University Shenzhen Hospital, Shenzhen, China
2
The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
3
Shenzhen Nanshan People's Hospital, Shenzhen, China
4
The Third People's Hospital of Shenzhen, Shenzhen, China
5
Shenzhen Second People's Hospital, Shenzhen, China
6
Shenzhen People's Hospital, Shenzhen, China
7
Shenzhen Health Capacity Building and Continuing Education Center, Shenzhen, China
Popul. Med. 2026;8(Supplement Supplement 1):A913
ABSTRACT
INTRODUCTION:
Artificial Intelligence (AI) is reshaping primary care, yet it raises significant concerns regarding algorithmic bias and the digital divide. Understanding the evolving academic focus on AI ethics is crucial for ensuring inclusive technology deployment.
METHODS:
To ensure comprehensive coverage, we searched seven databases: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and CINAHL. The search covered literature from January 2015 to December 2025 regarding AI in primary care. After removing duplicates and screening for relevance to primary care settings, we used VOSviewer to conduct a co-word analysis. We constructed strategic diagrams to classify themes and tracked the frequency evolution of ethical keywords such as "Bias," "Equity," and "Privacy."
RESULTS:
A total of 2,150 unique articles were included. Technical performance metrics (e.g., accuracy, sensitivity) consistently appeared as core themes. A post-2022 shift was observed where "Algorithmic Fairness" and "Explainable AI (XAI)" moved from peripheral to central themes, driven largely by contributions from computer science and social science fields. However, empirical studies specifically validating data representativeness for marginalized populations (e.g., migrants, ethnic minorities) remained scarce, accounting for only 5.2% of the total output.
CONCLUSIONS:
While academic interest in AI ethics is rising across disciplines, there remains a critical paucity of empirical research on eliminating systemic bias in primary care models. Future digital transformation must prioritize equity over mere technical efficiency to prevent technology from exacerbating existing health inequalities.
Artificial Intelligence (AI) is reshaping primary care, yet it raises significant concerns regarding algorithmic bias and the digital divide. Understanding the evolving academic focus on AI ethics is crucial for ensuring inclusive technology deployment.
METHODS:
To ensure comprehensive coverage, we searched seven databases: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and CINAHL. The search covered literature from January 2015 to December 2025 regarding AI in primary care. After removing duplicates and screening for relevance to primary care settings, we used VOSviewer to conduct a co-word analysis. We constructed strategic diagrams to classify themes and tracked the frequency evolution of ethical keywords such as "Bias," "Equity," and "Privacy."
RESULTS:
A total of 2,150 unique articles were included. Technical performance metrics (e.g., accuracy, sensitivity) consistently appeared as core themes. A post-2022 shift was observed where "Algorithmic Fairness" and "Explainable AI (XAI)" moved from peripheral to central themes, driven largely by contributions from computer science and social science fields. However, empirical studies specifically validating data representativeness for marginalized populations (e.g., migrants, ethnic minorities) remained scarce, accounting for only 5.2% of the total output.
CONCLUSIONS:
While academic interest in AI ethics is rising across disciplines, there remains a critical paucity of empirical research on eliminating systemic bias in primary care models. Future digital transformation must prioritize equity over mere technical efficiency to prevent technology from exacerbating existing health inequalities.
| eISSN: | 2654-1459 |
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