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Developing a Quality Evaluation Index System for Health Conversational Artificial Intelligence: Mixed Methods Study.

Weizhen Liao1, Meng Li2, Chengyu Ma1

  • 1School of Public Health, Capital Medical University, Beijing, China.

Journal of Medical Internet Research
|January 19, 2026
PubMed
Summary

A new quality evaluation index system for Health Conversational Artificial Intelligence (HCAI) was developed. This system, based on expert consensus, prioritizes ethics, consultation capability, and user experience for HCAI assessment.

Keywords:
Delphi methodanalytic hierarchy processconversational AIevaluation index systemhealth consultation

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Digital Health Evaluation

Background:

  • Growing demand for healthcare services and workforce shortages necessitate innovative solutions.
  • Health Conversational Artificial Intelligence (HCAI) offers potential for accessible and personalized care.
  • Lack of standardized evaluation frameworks hinders assessment of HCAI safety, reliability, and clinical applicability.

Purpose of the Study:

  • To establish a scientific, systematic quality evaluation index system for HCAI.
  • To provide a theoretical basis and practical tool for HCAI assessment and optimization.
  • To address the need for rigorous quality assessment in the rapidly evolving field of HCAI.

Main Methods:

  • Literature review, industry standards analysis, and expert group discussions informed the initial framework.
  • Two rounds of Delphi expert consultations ensured comprehensive input and consensus.
  • Analytic Hierarchy Process (AHP) was used to determine indicator weights and finalize the index system structure.

Main Results:

  • High expert consensus achieved with 100% response rates and significant authority coefficients.
  • The final HCAI quality evaluation index system comprises 3 primary, 7 secondary, and 28 tertiary indicators.
  • Weighted primary indicators: ethics and compliance (0.4781), health consultation capability (0.4112), and user experience (0.1107).

Conclusions:

  • The developed index system is scientifically valid and practically applicable for HCAI quality assessment.
  • This framework serves as a valuable reference for optimizing HCAI models and guiding regulatory oversight.
  • The study provides a robust tool to ensure the safety, reliability, and effectiveness of HCAI in healthcare settings.