CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915692080529408 |
|---|---|
| author | Zhang, Yuting Bunting, Karina V. Champsi, Asgher Wang, Xiaoxia Lu, Wenqi Thorley, Alexander Hothi, Sandeep S Qiu, Zhaowen Buyukates, Baturalp Kotecha, Dipak Duan, Jinming |
| author_facet | Zhang, Yuting Bunting, Karina V. Champsi, Asgher Wang, Xiaoxia Lu, Wenqi Thorley, Alexander Hothi, Sandeep S Qiu, Zhaowen Buyukates, Baturalp Kotecha, Dipak Duan, Jinming |
| contents | Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support Zhang, Yuting Bunting, Karina V. Champsi, Asgher Wang, Xiaoxia Lu, Wenqi Thorley, Alexander Hothi, Sandeep S Qiu, Zhaowen Buyukates, Baturalp Kotecha, Dipak Duan, Jinming Artificial Intelligence Computers and Society Multiagent Systems Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems. |
| title | CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support |
| topic | Artificial Intelligence Computers and Society Multiagent Systems |
| url | https://arxiv.org/abs/2508.13256 |