Multimodal Medical Answer Generation using Large Language Models: Results from the WangLab Submission to MEDIQA-M3G 2024

Alapfogalmak

The WangLab team explored two standalone solutions for the MEDIQA-M3G 2024 Multilingual and Multimodal Medical Answer Generation shared task, achieving 1st and 2nd place in the English category. The first solution involved two consecutive API calls to the Claude 3 Opus model, while the second trained a joint image-disease label embedding model using CLIP. Both solutions demonstrated the potential of large language models and multimodal approaches for medical visual question answering, but also highlighted the significant challenges in this domain.

Kivonat

The MEDIQA-M3G 2024 shared task focused on the problem of clinical dermatology multimodal query response generation. Participants were required to generate responses to cases consisting of text providing clinical context and queries, as well as associated images, resembling those made by medical professionals.
The WangLab team explored two standalone solutions for the English category of this task:

Claude 3 Opus API solution:

This solution involved two successive API calls to the Claude 3 Opus model from Anthropic.
The first API call generated possible differential diagnoses based on the provided images alone.
The second API call then reformatted the response to output only the name of the most likely skin condition.
This multi-stage approach outperformed a single-stage API call, likely due to the model's difficulty in simultaneously reasoning about the images and adhering to the required output format.

CLIP image classification solution:

This solution trained a joint image-disease label embedding model in the style of CLIP.
The training data included the medical discussions provided as part of the task, which were used to extract the most likely disease label for each case.
The resulting image-disease label pairs were used to train a ResNet50 image encoder and text projection layers.
During inference, the test images were classified within the learned joint embedding space, and the predicted disease label was post-processed into the required format.
Experiments highlighted the importance of batch size and retrieval method for effective CLIP training and inference in this low-data setting.

Both solutions achieved significantly higher scores than the next best submission on the competition leaderboard, demonstrating the potential of large language models and multimodal approaches for medical visual question answering. However, the overall performance of the solutions was still limited, highlighting the substantial challenges in this domain.
Key limitations include:

The low absolute deltaBLEU scores, even for the top-performing solutions, indicating significant room for improvement.
Inconsistencies and instability in the solutions, with the Claude API subject to randomness and the CLIP solution exhibiting retrieval inconsistencies.
The solutions being optimized for the specific competition metric, which favored short responses and did not fully capture the complexity of the task.
The WangLab team's work provides valuable insights into promising directions for future research, such as further investigation of multi-stage LLM systems and the importance of evaluation metrics in benchmarking the clinical efficacy of developed systems. The MEDIQA-M3G 2024 shared task represents an important step towards the goal of automatically generating clinical responses for multimodal medical queries.

Statisztikák

The training dataset consisted of 842 cases, with each case containing one or more images of skin conditions, accompanying text providing clinical context and queries, and multiple responses from medical professionals.
The validation and test sets contained 56 and 100 cases, respectively.
The query text and target responses were provided in Chinese, English, and Spanish, with the training set potentially containing some automatically translated content.

Idézetek

"The higher scoring of the two methods consists of two successive API calls to Claude 3 Opus (Anthropic). For each case in the test set, the first API call generates possible differential diagnosis for the given images, and the second API call further processes the response into the name of the most likely disease only, which is then returned."
"We observe that the disease diagnosis given by Claude 3 Opus was poorer quality when the prompt constrains the output format upon manual review. This was further confirmed by the inferior performance of the 1-call result. Therefore, we let the API generate differential responses only with the provided images alone without any constraints on format, and use a second API call to reformat the response into the desired form, which is just the name of the skin condition without any abbreviations."

Főbb Kivonatok

WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

by Ronald Xie,S... : arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14567.pdf

Mélyebb kérdések

How can the evaluation metrics be improved to better capture the complexity and clinical relevance of the medical visual question answering task

To enhance the evaluation metrics for medical visual question answering tasks, it is crucial to incorporate a more comprehensive assessment that goes beyond simple word matching. Here are some ways to improve the evaluation metrics:

Semantic Evaluation: Introduce metrics that assess the semantic similarity between the generated responses and the ground truth. This can involve leveraging pre-trained language models to evaluate the contextual relevance of the answers.

Clinical Accuracy: Develop metrics that consider the clinical accuracy of the responses. This could involve consulting medical professionals to validate the correctness of the generated answers in a clinical context.

Multi-Modal Evaluation: Since medicine is inherently multimodal, metrics should account for the integration of text and image modalities. Metrics that evaluate the alignment and coherence between textual descriptions and visual content can provide a more holistic assessment.

Task-Specific Metrics: Tailor evaluation metrics to the specific requirements of medical visual question answering tasks. This could involve considering factors like differential diagnoses, treatment recommendations, and patient-specific information in the evaluation process.

Human-in-the-Loop Evaluation: Incorporate human annotators or clinicians in the evaluation process to provide qualitative feedback on the relevance and accuracy of the generated responses.

By incorporating these enhancements, the evaluation metrics can better capture the complexity and clinical relevance of medical visual question answering tasks.

What other multimodal architectures and training strategies could be explored to further improve the performance of large language model-based solutions for this task

To further improve the performance of large language model-based solutions for medical visual question answering, exploring advanced multimodal architectures and training strategies is essential. Here are some approaches that could be considered:

Transformer Variants: Experiment with transformer variants optimized for multimodal tasks, such as Vision Transformers (ViTs) or variants that incorporate both text and image embeddings effectively.

Attention Mechanisms: Develop attention mechanisms that can dynamically focus on relevant parts of the text and image inputs to improve the model's understanding of multimodal data.

Fine-Tuning Strategies: Explore fine-tuning strategies that leverage both pre-trained language models and domain-specific medical data to enhance the model's performance on medical visual question answering tasks.

Generative Adversarial Networks (GANs): Investigate the use of GANs to generate realistic medical images that can be integrated into the training process to improve the model's ability to interpret visual data accurately.

Self-Supervised Learning: Implement self-supervised learning techniques that can leverage the inherent structure of medical data to improve the model's representation learning capabilities across modalities.

By exploring these multimodal architectures and training strategies, it is possible to enhance the performance and robustness of large language model-based solutions for medical visual question answering tasks.

Given the challenges highlighted in this work, what are the key technical and practical barriers that need to be addressed before deploying such systems in real-world clinical settings

Deploying systems for medical visual question answering in real-world clinical settings poses several technical and practical challenges that need to be addressed:

Interpretability and Explainability: Ensuring that the AI models provide transparent and interpretable results is crucial for gaining trust from healthcare professionals. Developing methods to explain the model's reasoning and decision-making processes is essential.

Data Privacy and Security: Handling sensitive medical data requires robust privacy and security measures to protect patient information. Compliance with regulations like HIPAA is essential for deploying AI systems in clinical settings.

Ethical Considerations: Addressing ethical concerns related to bias, fairness, and accountability in AI algorithms is paramount. Ensuring that the models do not perpetuate existing healthcare disparities is critical for ethical deployment.

Integration with Clinical Workflows: Seamless integration of AI systems into existing clinical workflows is essential for adoption. The systems should complement healthcare professionals' tasks and provide actionable insights in real-time.

Validation and Regulatory Approval: Conducting rigorous validation studies and obtaining regulatory approval are necessary steps before deploying AI systems in clinical practice. Ensuring the safety and efficacy of the systems is paramount.

By addressing these technical and practical barriers, healthcare organizations can pave the way for the successful deployment of medical visual question answering systems in real-world clinical settings.