Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns

TL;DR

This paper introduces a novel human-AI interaction method for chest X-ray analysis by integrating eye gaze data into vision-language models, significantly improving diagnostic accuracy and enhancing AI-radiologist collaboration.

Contribution

It presents a new approach that combines eye gaze heatmaps with vision-language models to improve medical image analysis and radiologist-AI interaction.

Findings

Eye gaze data improves analysis accuracy across tasks.

Fine-tuning with gaze data outperforms other models.

Enhanced human-AI collaboration in medical diagnosis.

Abstract

Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between these models and radiologists has been primarily limited to input images. This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists' attention by incorporating eye gaze data alongside textual prompts. Our approach leverages heatmaps generated from eye gaze data, overlaying them onto medical images to highlight areas of intense radiologist's focus during chest X-ray evaluation. We evaluate this methodology in tasks such as visual question answering, chest X-ray report automation, error detection, and differential diagnosis. Our results demonstrate the inclusion of eye gaze information significantly enhances the accuracy of chest X-ray analysis. Also, the impact of eye gaze on fine-tuning was confirmed as it outperformed other medical VLMs in all tasks except visual question answering. This work marks the potential of leveraging both the VLM's capabilities and the radiologist's domain knowledge to improve the capabilities of AI models in medical imaging, paving a novel way for Computer Assisted Diagnosis with a human-centred AI.