MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

TL;DR

MEDCOD is a novel medical dialogue system that combines medical accuracy, emotional empathy, diversity, and controllability, leveraging a hybrid approach of modular knowledge integration and deep learning for natural language generation.

Contribution

It introduces a unique natural language generator that produces empathetic, diverse, and medically consistent responses, advancing human-like medical dialogue systems.

Findings

Effective in generating empathetic and diverse medical dialogues

Maintains medical accuracy and consistency in responses

Demonstrates improved human-likeness in medical conversations

Abstract

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD