Abstract
AbstractThe rapid rise of healthcare chatbots, valued at $787.1 million in 2022 and projected to grow at 23.9% annually through 2030, underscores the need for robust evaluation frameworks. Despite their potential, the absence of standardized evaluation criteria and rapid AI advancements complicate assessments. This study addresses these challenges by developing the first comprehensive evaluation framework inspired by health app regulations and integrating insights from diverse stakeholders. Following PRISMA guidelines, we reviewed 11 existing frameworks, refining 271 questions into a structured framework encompassing three priority constructs, 18 second-level constructs, and 60 third-level constructs. Our framework emphasizes safety, privacy, trustworthiness, and usefulness, aligning with recent concerns about AI in healthcare. This adaptable framework aims to serve as the initial step in facilitating the responsible integration of chatbots into healthcare settings.
Publisher
Cold Spring Harbor Laboratory
Reference31 articles.
1. Grand View Research. Healthcare Chatbots Market Size, Share & Trends Analysis Report By Component (Software, Services), By Application (Appointment Scheduling, Symptom Checking), By Deployment, By End-user, And Segment Forecasts, 2023 - 2030. San Francisco, CA: Grand View Research, Inc., 2024.
2. Bach D. How international health care organizations are using bots to help fight COVID-19. Microsoft 2020; published online April. https://news.microsoft.com/transform/how-international-health-care-organizations-are-using-bots-to-help-fight-covid-19/.
3. GPT versus Resident Physicians — A Benchmark Based on Official Board Scores;NEJM AI,2024
4. Meaney C , Huang RS , Lu K , Fischer AW , Leung FH , Kulasegaram K , Tzanetos K , Punnett A. Comparing the performance of ChatGPT and GPT-4 versus a cohort of medical students on an official University of Toronto undergraduate medical education progress test. medRxiv. 2023 Sep 14:2023–09.
5. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models;PLoS digital health,2023