EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval

Abstract:We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: this https URL

Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)

arXiv:2609.07194 [cs.AI]

(or arXiv:2609.07194v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.07194 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Workshop SECURE AI4H, AAAI 2026, https://link.springer.com/book/9789819239238

https://doi.org/10.1007/978-3-032-37657-2_32 Focus to learn more DOI(s) linking to related resources

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