ReviewJMIR AI2025
Standardizing and Scaffolding Health Care AI-Chatbot Evaluation: Systematic Review.
Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Article
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Iterative Multidisciplinary Development and Evaluation of a Patient-Facing Social Determinants of Health Chatbot Using Synthetic Data Simulation: Mixed Methods Study.JMIR formative research · 2026Article
- Mobile App Rating Scale for Health Care Professionals to Assess the Quality of mHealth Apps: Questionnaire Development and Psychometric Analysis.JMIR mHealth and uHealth · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Metrics Used for the Evaluation of Chatbots Providing Cancer Genetic Risk Assessment and Education: Systematic Review.JMIR AI · 2026Review
- Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era.JMIR mental health · 2026Article
- Review
- Arkangel AI, OpenEvidence, ChatGPT, Medisearch: Are They Objectively up to Medical Standards? A Real-Life Assessment of LLM Chatbots in Health Care.Mayo Clinic proceedings. Digital health · 2026Article
- Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.Frontiers in public health · 2026Article
- Training the public health workforce for generative AI: validation of the Italian Chatbot Usability Questionnaire.Frontiers in public health · 2026Article
- Safety-focused development and evaluation of an LLM sexual well-being chatbot for women: A methods-focused feasibility study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth care chatbots are rapidly proliferating, while generative artificial intelligence (AI) outpaces existing evaluation standards.
objectiveWe aimed to develop a structured, stakeholder-informed framework to standardize evaluation of health care chatbots.
methodsPRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses)-guided searches across multiple databases identified 266 records; 152 were screened, 21 full texts were assessed, and 11 frameworks were included. We extracted 356 questions (refined to 271 by deduplication and relevance review), mapped items to Coalition for Health AI constructs, and organized them with iterative input from clinicians, patients, developers, epidemiologists, and policymakers.
resultsWe developed the Health Care AI Chatbot Evaluation Framework (HAICEF), a hierarchical framework with 3 priority domains (safety, privacy, and fairness; trustworthiness and usefulness; and design and operational effectiveness) and 18 second-level and 60 third-level constructs covering 271 questions. Emphasis includes data provenance and harm control; Health Insurance Portability and Accountability Act/General Data Protection Regulation-aligned privacy and security; bias management; and reliability, transparency, and workflow integration. Question distribution across domains is as follows: design and operational effectiveness, 40%; trustworthiness and usefulness, 39%; and safety, privacy and fairness, 21%. The framework accommodates both patient-facing and back-office use cases.
conclusionsHAICEF provides an adaptable scaffold for standardized evaluation and responsible implementation of health care chatbots. Planned next steps include prospective validation across settings and a Delphi consensus to extend accountability and accessibility assurances.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.