ArticleBMC medical informatics and decision making2026
An intention-oriented multi-agent dialogue system for patient-centered decision-making.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConversational agents are increasingly used in healthcare, but most systems are designed for clinicians and insufficiently support patient-centered decision-making. Limited patient understanding of medical decisions may reduce adherence and lead to doctor-patient conflicts. Existing dialogue systems either lack interpretability or suffer from rigid architectures and limited scalability. This study aims to develop an interpretable, patient-centered medical dialogue system to improve communication and decision support.
methodsWe propose an intention-oriented multi-agent dialogue system consisting of an intention understanding agent, an argument reasoning agent, and a template-based interaction agent. A medical knowledge ontology was constructed using a hybrid expert-data-driven approach, and a knowledge base was built from recent biomedical literature. The system performs intent recognition, entity extraction, and argument-based reasoning using weighted clinical evidence. A Graves' eye disease dialogue platform was implemented to evaluate system performance through objective metrics and clinician-based user studies.
resultsThe system achieved an average F1 score of 85% for intent recognition and over 95% response accuracy in evaluations by physicians. User experience assessments showed performance comparable to physician-led online systems and superior to traditional medical Q&A systems. In comparative experiments, the proposed system significantly outperformed ChatGPT in personalized reasoning and interpretability across multiple evaluation dimensions.
conclusionsThe proposed multi-agent dialogue system improves patient-centered communication by providing interpretable and personalized decision support. It demonstrates strong potential to enhance healthcare dialogue systems and assist clinical decision-making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.