Multimodal Medical Diagnosis and Chat

Multimodal Medical Diagnosis and Chat

1. Dataset Preparation:

Use datasets containing medical images (e.g., fundus, X-rays) and corresponding text reports.
Utilize the IU X-Ray dataset (https://paperswithcode.com/dataset/iu-x-ray) for training and evaluation, as it includes paired chest X-ray images and radiology reports.

2. Model Development:

Develop multimodal AI models that integrate image analysis and text understanding.
Image Analysis: Utilize Vision Transformer (ViT) for medical image processing and feature extraction.
Text Understanding: Use BioBERT, ClinicalBERT, or GPT-4 (OpenAI API) for interpreting medical reports and handling textual queries.
Implement multimodal fusion techniques to combine insights from images and text.

3. Medical Chat Application:

Build an interactive medical chat system that leverages multimodal inputs (images and text) to provide diagnostic assistance and respond to medical queries.

4. Evaluation:

Assess model and chat system performance using metrics like accuracy, precision, recall, and response relevance.

5. Implementation Platform:

The project will be developed and executed on Google Colab.