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AI in Medical Questionnaires: Scoping Review
Xuexing Luo1, Yiyuan Li1, Jing Xu1
1Faculty of Humanities and Arts, Macau University of Science and Technology, Macau, China.
Journal of Medical Internet Research
|June 23, 2025
Summary
Artificial intelligence (AI) shows promise in enhancing medical questionnaires for assessment, development, and prediction, despite current research limitations. Future work must address interpretability, validation, and ethics for clinical integration.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Psychometric Assessment
Background:
- The global mental health burden is significant, with limitations in current diagnostic questionnaires leading to inaccurate diagnoses.
- The COVID-19 pandemic has increased healthcare challenges, highlighting the need for advanced diagnostic tools.
- Artificial Intelligence (AI) offers potential solutions for improving diagnostic accuracy and clinical decision-making in healthcare.
Purpose of the Study:
- To systematically review the applications, benefits, and challenges of AI in medical questionnaires.
- To focus on AI's role in assessment, development, and prediction functions within medical questionnaires.
- To evaluate the value of AI in addressing limitations of traditional diagnostic tools.
Main Methods:
- Systematic review of 5 databases (PubMed, Embase, Cochrane Library, Web of Science, CNKI) from inception to September 2024.
- Inclusion criteria focused on peer-reviewed studies applying AI to medical, psychological, or physiological questionnaires with measurable outcomes.
- Data extraction, quality appraisal using Joanna Briggs Institute tools, and narrative synthesis were performed by three independent reviewers.
Main Results:
- 14 studies met inclusion criteria, identifying 24 AI technologies (e.g., random forest, ChatGPT) applied to questionnaires.
- AI demonstrated advantages in assessment (e.g., 92.18% accuracy in distinguishing conditions), development (e.g., ChatGPT for culturally competent scales), and prediction (AUC 0.790 for cataract surgery risk).
- Most studies (79%) remain in the exploratory phase, with moderate methodological quality and limitations such as lack of control groups and inadequate validation.
Conclusions:
- Integrated AI application in medical questionnaires holds significant potential for improving diagnostic efficiency, accelerating scale development, and promoting early intervention.
- Further research is crucial to enhance model interpretability, system compatibility, validation standardization, and ethical governance.
- Addressing challenges like data privacy, clinical integration, and transparency is essential for the effective implementation of AI in medical questionnaires.
Keywords:
AIartificial intelligencediagnostic accuracymedical questionnairesquestionnaire developmentquestionnaire-based predictionMore Related Videos
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