Overview

Faculty at the Center for Translational Data Science (CTDS), the Department of Medicine, the Department of Computer Science, and the Data Science Institute at the University of Chicago have started an initiative called Meshes of Midscale Models (M3) to address some of the current challenges in applying AI to the fields of biology, medicine, and healthcare.

Central to this initiative are:

1. Growing the number of organizations that can develop and operate data commons containing high-quality biomedical data;
2. Developing AI algorithms, methods, and techniques for building machine learning, large language models (LLM), and large quantitative models (LQM) over the data in these commons (“AI Commons”); and
3. Developing technology to support distributed and federated AI over multiple AI Commons (“AI Meshes”).

To support these aims, CTDS is leading a collaboration to develop open-source software for 1), 2), and 3).

Challenges in Biomedical AI

The incorporation of AI in biomedical domains is hindered by several key obstacles:

1. Data Limitations: The scarcity and inaccessibility of high-quality labeled biomedical data impede the training of Large Language Models (LLMs), Generative AI (GenAI) models, and Large Quantitative Models (LQM). Much of this data is secured behind organizational firewalls due to privacy and compliance issues.
2. Limited understanding of embeddings for biomedical data: While embedding techniques have advanced for text and image data, translating these methods to complex biomedical data, such as those involving genes, proteins, receptors, etc., remains an active research area without equivalent success.
3. Economics of Data: Developing and maintaining biomedical data systems is expensive and demands a deeper understanding of the economics related to data value creation and sustainable data marketplaces.
4. Resource Constraints: Recruiting skilled personnel in this domain is challenging, slowing progress despite the urgent need for advancement.