Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization

TL;DR

This paper introduces a multi-modal, knowledge-infused framework for clinical conversation summarization that incorporates visual data and medical knowledge to improve accuracy and relevance in telemedicine report generation.

Contribution

It proposes a novel multi-modal, multi-task framework with knowledge infusion and visual features, along with a new annotated dataset for clinical conversation summarization.

Findings

Visual information significantly improves summary quality.

Knowledge infusion enhances medical entity preservation.

Medical department identification correlates with summary accuracy.

Abstract

With the advancement of telemedicine, both researchers and medical practitioners are working hand-in-hand to develop various techniques to automate various medical operations, such as diagnosis report generation. In this paper, we first present a multi-modal clinical conversation summary generation task that takes a clinician-patient interaction (both textual and visual information) and generates a succinct synopsis of the conversation. We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework. It leverages an adapter to infuse knowledge and visual features and unify the fused feature vector using a gated mechanism. Furthermore, we developed a multi-modal, multi-intent clinical conversation summarization corpus annotated with intent, symptom, and summary. The extensive set of experiments, both quantitatively and qualitatively, led to the following findings: (a) critical significance of visuals, (b) more precise and medical entity preserving summary with additional knowledge infusion, and (c) a correlation between medical department identification and clinical synopsis generation. Furthermore, the dataset and source code are available at https://github.com/NLP-RL/MM-CliConSummation.