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Standardizing and Scaffolding Health Care AI-Chatbot Evaluation: Systematic Review.

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  • 1Technology and Operations Management, Harvard Business School, Cambridge, MA, United States.

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Summary
This summary is machine-generated.

A new framework, HAICEF, standardizes health care AI chatbot evaluation. It addresses safety, privacy, fairness, trustworthiness, and operational effectiveness for responsible AI implementation.

Keywords:
AI chatbotsartificial intelligenceevaluation frameworkgenerative AIlarge language model

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Medical Ethics

Background:

  • The rapid proliferation of health care chatbots is outpacing current evaluation standards for artificial intelligence (AI).
  • There is a critical need for systematic methods to assess the safety, efficacy, and ethical implications of these AI-driven tools.
  • Existing evaluation approaches lack standardization, hindering responsible development and deployment in clinical settings.

Purpose of the Study:

  • To develop a structured, stakeholder-informed framework for the standardized evaluation of health care chatbots.
  • To create a comprehensive tool that addresses key concerns in AI healthcare applications.
  • To facilitate the responsible implementation of AI in patient care and health system operations.

Main Methods:

  • Systematic literature searches guided by PRISMA methodology identified relevant evaluation frameworks.
  • Extracted and refined evaluation questions, mapping them to established AI constructs.
  • Iterative input from diverse stakeholders including clinicians, patients, developers, and policymakers ensured a comprehensive and practical framework.

Main Results:

  • The Health Care AI Chatbot Evaluation Framework (HAICEF) was developed, featuring a hierarchical structure with three priority domains: safety, privacy, and fairness; trustworthiness and usefulness; and design and operational effectiveness.
  • The framework comprises 18 second-level and 60 third-level constructs, covering 271 detailed questions.
  • Key areas of focus include data provenance, harm control, HIPAA/GDPR-aligned privacy, bias management, reliability, transparency, and workflow integration, applicable to both patient-facing and back-office use cases.

Conclusions:

  • HAICEF offers an adaptable scaffold for the standardized evaluation and responsible implementation of health care AI chatbots.
  • The framework promotes a systematic approach to assessing AI tools in healthcare, enhancing safety and effectiveness.
  • Future work includes prospective validation and a Delphi consensus to further extend accountability and accessibility assurances for AI in healthcare.