Magnol.AI Copilot: Multimodal LLMs for Conversational Insight Generation

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Authors: Association for Artificial Intelligence 2026, Guangchen Ruan, Hui Xiao, Hui Zhang
Cited by: 0

Abstract

We present Magnol.AI Copilot, an extension of the Magnol.AI digital biomarker platform that integrates multimodal large language models (LLMs) to transform digital health technology (DHT) trial dashboards into conversational systems. Copilot augments the platform with a multi-agent orchestration layer and vision-enabled LLMs that interpret visualizations, tabular summaries, and textual metadata. The system enables natural language queries and automatic generation of contextual insights, allowing researchers to interact with wearable data through dialogue rather than static inspection. A case study with an actigraphy device demonstrates Copilot’s ability to identify nightly compliance gaps and provide contextual explanations, reducing cognitive load compared to manual dashboard review. This work presents a novel integration of IoMT infrastructure with multimodal LLMs, advancing digital biomarker research toward conversational and accessible DHT trial platforms.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Context-Aware Activity Recognition Systems Multimodal Machine Learning Applications

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN