AI vs Manual Quality Control in Epidemiological Surveys: The Nicheng Cohort Study

This observational study investigates the efficacy of AI-assisted quality control versus traditional manual methods in the context of the Shanghai Nicheng Cohort Study. With an estimated enrollment of 900 participants, the research aims to evaluate how artificial intelligence can enhance the accuracy and efficiency of data validation in large-scale epidemiological surveys.

The study divides quality control personnel into two groups: an experimental group utilizing an AI system that transcribes recordings, extracts keywords, analyzes logical consistency, and generates quality control prompts; and a control group relying solely on manual review of questionnaire recordings without technological assistance.

Key aspects of the research include:

• Comparison of error detection rates between AI-assisted and manual approaches
• Assessment of time efficiency in quality control processes
• Evaluation of data completeness and consistency improvements
• Analysis of user adaptation to AI-assisted tools

Participants must meet specific criteria, including computer proficiency, dialect recognition ability (particularly Shanghai Nanhui dialect), and openness to using AI tools. The study excludes individuals unfamiliar with AI technology or unable to commit to the full research duration.

Primary outcomes focus on changes in quality control accuracy over an 8-week period, while secondary outcomes examine improvements in data integrity through automated keyword extraction and logical consistency checks. The Shanghai 6th People's Hospital leads this innovative research, which runs from May 1 to June 15, 2025.

This study represents a significant step toward modernizing epidemiological research methods, potentially setting new standards for data quality assurance in population health studies.