Foundation Model–Powered Medical Dialogue Agents with Causality-Aware Workflow Reasoning

Abstract

By facilitating human-AI responses through conversational medical assistants, foundation models have fulfilled great progress by tackling concerns related to bi-directional dialogues and adaptation. Nevertheless, the majority of current medical dialogue systems are still correlation-based, without clear causal reasoning, workflow understanding, and explainability, which are a fatal threat in the context of an assistive medical dialogue where safety, trust, and clinical reliability are of utmost importance. To establish a medical dialogue agent that helps resolve these issues, this paper suggests a foundation model-based medical dialogue agent that also has a workflow reasoning grounded in causality. The suggested paradigm integrates an extensive language model with a causal reasoning system and a guideline-based clinical workflow regulator to secure medically-grounded, clear, and secure dialogue interactions. Causal graphs are used to explicitly describe the connection between symptoms, diagnosis, risk factors, and interventions to allow the system to reason over more than just surface language patterns. Workflows of clinical activities are based on some medical guidelines that restrain the development of dialogue, adopting the standard decision-making habits. The approach focuses on the explainability of response generation in which causal inference and workflow-driven justification will be used to support every system recommendation. Experimental assessment is assessed based on simulated dialogues between patients and agents and benchmarking medical question-answering data. Findings indicate greater clinical consistency, reduced number of hallucinated medical advice and increased score on user trust than when using conventional foundation-model based medical chatbots. The results imply that causal reasoning and workflow constraints improve the medical dialogue systems in terms of safety and reliability significantly. The article has been developing intelligent assistive communication technologies, as it shows how foundation models can be extended with causal transparency and structured medical thought to facilitate reliable healthcare communication.