How AI Can Leverage Multimodal Information in Medical Systems
Medical AI systems have the potential to revolutionize healthcare by analyzing data from various sources like medical images, clinical notes, lab tests, and genomics. However, integrating different modalities into AI systems poses a challenge. In this blog post, we explore different approaches to building multimodal AI systems and discuss recent research papers highlighting their feasibility.
Approaches to Building Multimodal AI Systems
1. Tool Use: In this approach, an AI system uses specialized tools to analyze data from different modalities. For example, an AI system can send a chest X-ray image to a radiology AI system for analysis. This approach offers flexibility and allows the use of validated subsystems but can lead to communication issues between subsystems.
2. Model Grafting: This approach involves adapting neural networks specialized in specific domains to plug directly into the AI system. Recent papers from Google Research demonstrate the feasibility of this approach. For instance, a neural network can be trained to interpret spirograms (a breathing assessment) and its output can be adapted to serve as input for the AI system. Model grafting allows the AI system to leverage existing optimized models but requires building new adapters for each specific domain.
3. Generalist Systems: The most ambitious approach is to build an integrated AI system that can handle information from all modalities. Google Research’s recent paper introduces Med-PaLM M, a multimodal AI model that can interpret clinical language, imaging, and genomics. This generalist approach maximizes flexibility and information transfer between modalities but comes with higher computational costs.
The Future of Multimodal Medical AI Systems
Successfully developing multimodal medical AI systems could have a profound impact on professional medicine, medical research, and consumer applications. However, it is crucial to evaluate these technologies in collaboration with the medical community and healthcare ecosystem.
In conclusion, there are different approaches to building multimodal AI systems in healthcare, each with its own advantages and disadvantages. By leveraging the strengths of existing tools, adapting specialized models, or building generalist systems, we can create AI systems that effectively integrate information from diverse modalities and enhance patient care.