Towards Health-I: Personalized Healthcare Intelligence Based on Generative AI with Cross-modal Learning and Long-term Adaptation

Abstract

Health-I is envisioned as a cyber-enabled smart companion that continuously aggregates multimodal health-related data to support personalized care and coordinated actions with clinicians and AI systems. While recent advances in generative AI (GenAI) and large language models (LLMs) have enabled multimodal reasoning across clinical text, wearable signals, and medical images, realizing Health-I requires long-term, stable, and personalized health modeling. This paper proposes a GenAI–driven framework comprising multimodal data acquisition, a GenAI processing layer, and User-Aware Responses. To evaluate feasibility, we conduct two case studies using ECG data: individual identification for personalization and emotion recognition for mental health. We assess three mainstream GenAI models (i.e., GPT-4o, DeepSeek-V3, and Gemini-2.5-Pro) under zero-shot and one-shot prompting. The findings highlight key challenges in unified multimodal representations, joint multi-task reasoning, and sustained personalization, and outline a roadmap toward practical, reliable, and ethically aligned Health-I systems.

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