Multi-agent Self-triage System with Medical Flowcharts
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911609883983872 |
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| author | Liu, Yujia Yu, Sophia Jin, Hongyue Wen, Jessica Qian, Alexander Lee, Terrence Ramsis, Mattheus Choi, Gi Won Qin, Lianhui Liu, Xin Wang, Edward J. |
| author_facet | Liu, Yujia Yu, Sophia Jin, Hongyue Wen, Jessica Qian, Alexander Lee, Terrence Ramsis, Mattheus Choi, Gi Won Qin, Lianhui Liu, Xin Wang, Edward J. |
| contents | Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-agent Self-triage System with Medical Flowcharts Liu, Yujia Yu, Sophia Jin, Hongyue Wen, Jessica Qian, Alexander Lee, Terrence Ramsis, Mattheus Choi, Gi Won Qin, Lianhui Liu, Xin Wang, Edward J. Artificial Intelligence Multiagent Systems Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization. |
| title | Multi-agent Self-triage System with Medical Flowcharts |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2511.12439 |