Scholarlink

<p>This article presents the design, implementation, and evaluation of MarIA, a GPT-3.5-powered virtual assistant integrated into a messaging platform to support patients with type 2 diabetes mellitus (DM). MarIA employs a multi-agent architecture that enables varying dialogue styles and degrees of personalization. In a 3-month longitudinal study involving 35 participants, personalized interactions increased engagement by 26%, while message length more than quadrupled—yielding a richer understanding of patient context. This deeper contextualization enabled MarIA to initiate more relevant, meaningful conversations, fostering a positive cycle of sustained engagement. Safety was critically assessed. While MarIA did not generate factual hallucinations, some general health suggestions—though accurate in isolation—could be inappropriate for users with specific clinical constraints. This underscores the need not only for comprehensive patient profiling but also for an embedded safety layer capable of detecting potentially unsuitable recommendations before or even after delivery. The multi-agent architecture proved essential in enabling proactive behaviors, nuanced context detection, and dialogue adaptability, ultimately enhancing both engagement and user safety in AI-supported chronic care.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing the User Experience of an LLM-Based Conversational Assistant in Diabetes Mellitus Care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月1日

查看全文
  • Vasco Furtado,
  • Jorge Araujo,
  • Elizabeth S. Furtado,
  • Rafael Bomfim,
  • Vitória Silva,
  • Nickolas Mororó,
  • Lucas Vasconcelos,
  • Cecília Carvalho,
  • Marcos Severo,
  • Juliana Torres,
  • Juliana Oliveira,
  • Antonio Bruno Freitas

摘要

This article presents the design, implementation, and evaluation of MarIA, a GPT-3.5-powered virtual assistant integrated into a messaging platform to support patients with type 2 diabetes mellitus (DM). MarIA employs a multi-agent architecture that enables varying dialogue styles and degrees of personalization. In a 3-month longitudinal study involving 35 participants, personalized interactions increased engagement by 26%, while message length more than quadrupled—yielding a richer understanding of patient context. This deeper contextualization enabled MarIA to initiate more relevant, meaningful conversations, fostering a positive cycle of sustained engagement. Safety was critically assessed. While MarIA did not generate factual hallucinations, some general health suggestions—though accurate in isolation—could be inappropriate for users with specific clinical constraints. This underscores the need not only for comprehensive patient profiling but also for an embedded safety layer capable of detecting potentially unsuitable recommendations before or even after delivery. The multi-agent architecture proved essential in enabling proactive behaviors, nuanced context detection, and dialogue adaptability, ultimately enhancing both engagement and user safety in AI-supported chronic care.