Introducing Multimodal Healthcare AI Systems Free Course

Healthcare AI is developing rapidly, and multimodal data is at the core of this development. Deep Lake 4.0 allows you to integrate multimodal biomedical datasets, consisting of images, text, literature, et cetera, with powerful generative AI tools, yielding research benefits. We’ve partnered with Bayer Radiology, Intel Corporation, and Amazon Web Services to bring you this course, which covers:

1. Standardizing multimodal data in healthcare AI with Croissant and Deep Lake

The Croissant file format is a metadata-rich standard, particularly valuable for managing biomedical datasets that combine images with clinical or experimental context. In this chapter, you’ll learn how to load biomedical data in Croissant, store them in Deep Lake, and apply version control through branching and merging, ensuring reproducibility and collaborative progress in biomedical research workflows.

2. Powerful AI search on a natural language corpus of adverse drug effects with Deep Lake

Pharmaceutical datasets are complex, with unstructured clinical notes, social media posts, and scientific literature. In this chapter, you’ll learn how to process the ADE Corpus V2 into a query-ready format using Deep Lake, including loading the corpus, converting it into a Deep Lake dataset, and training a TinyLlama LoRA model, enabling powerful AI search across diverse biomedical text.

3. Automated LLM-powered labeling of radiology image datasets

Radiology datasets are often underutilized due to limited descriptive metadata. In this chapter, you’ll learn how to use LLMs with Deep Lake to generate natural language labels for images, transforming them into multimodal, searchable datasets.

4. AI-powered biomedical literature review

The growing volume of biomedical literature makes comprehensive analysis difficult and time-consuming. In this chapter, you’ll learn how to leverage Activeloop’s L0 reasoning model to perform AI-powered literature reviews on multimodal scientific papers. This approach enables researchers to extract insights and answer complex questions efficiently.

5. Unified multimodal search for rapid drug discovery

Drug discovery is often slowed by siloed, multimodal datasets spanning literature, assays, sequences, and imaging. In this chapter, you’ll learn how to unify and index diverse biomedical data on AWS Sagemaker Lakehouse with Deep Lake, enabling AI Search across textual, numerical, and visual information. Researchers can rapidly shortlist promising drug candidates using lexical, semantic, and visual queries, dramatically accelerating the path from data to actionable insights.

Authored by Darsh Mandera (Activeloop) and Steffen Vogler (Bayer Radiology).

Special thanks to Vitor Freitas (AWS), Gitika Vijh (AWS), Susan Marquez (Intel Corporation) and Arijit Bandyopadhyay (Intel Corporation).

Big Upgrade to All Courses

Additionally, we’ve also upgraded all of our previously released courses on learn.activeloop.ai to run on Deep Lake 4.0, the latest version of Deep Lake, as well as the latest versions of key artificial intelligence libraries like LlamaIndex and LangChain, providing a modernized learning experience.