CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models (2503.07667v2)

Abstract: Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduce Clinical Large-Scale Integrative Multimodal Benchmark (CLIMB), a comprehensive clinical benchmark unifying diverse clinical data across imaging, language, temporal, and graph modalities. CLIMB comprises 4.51 million patient samples totaling 19.01 terabytes distributed across 2D imaging, 3D video, time series, graphs, and multimodal data. Through extensive empirical evaluation, we demonstrate that multitask pretraining significantly improves performance on understudied domains, achieving up to 29% improvement in ultrasound and 23% in ECG analysis over single-task learning. Pretraining on CLIMB also effectively improves models' generalization capability to new tasks, and strong unimodal encoder performance translates well to multimodal performance when paired with task-appropriate fusion strategies. Our findings provide a foundation for new architecture designs and pretraining strategies to advance clinical AI research. Code is released at https://github.com/DDVD233/climb.

• The paper presents CLIMB, a novel framework and dataset comprising 19.01TB from 4.51 million patients across diverse modalities, addressing the need for multimodal clinical AI benchmarks.
• Empirical evaluations show significant performance gains through multitask pretraining and improved generalization in few-shot learning settings when models are trained using CLIMB.
• CLIMB serves as a foundational resource to accelerate the development of more integrated and reliable clinical foundation models capable of holistic patient data analysis.

Data Foundations for Large-Scale Multimodal Clinical Foundation Models: An Overview

The paper under discussion presents a comprehensive paper on the development of Clinical Large-scale Integrative Multimodal Benchmark (CLIMB), a novel framework meant to enhance the architecture and training strategies for multimodal clinical AI models. The research addresses a critical gap in current clinical AI benchmarks, which are often restricted to single-domain or single-modality analysis, thereby limiting the holistic assessment of a patient's condition that real-world healthcare demands.

Problem Context and Motivation

Recent advancements in clinical AI have predominantly focused on unimodal systems, such as image classification using X-rays, NLP of clinical notes, and predictive models for patient outcomes using time-series data. This siloed approach is insufficient for holistic patient assessment and the integrated interpretation of various medical data types — including imaging, language, temporal, and graph data — crucial for improving diagnostic and prognostic performance. The paper presents CLIMB as a solution, offering a multimodal clinical benchmark that consolidates data from diverse sources to train and evaluate clinical foundation models.

Composition of CLIMB

CLIMB is an extensive dataset aggregating 4.51 million patient samples to a total of 19.01 terabytes, sourced from a diverse range of modalities. These include 2D imaging (e.g., X-rays), 3D video (e.g., CT scans), time series (e.g., ECGs), graphs (e.g., brain networks), and multimodal data combinations. Through a rigorous data standardization process across 33 institutions, the dataset maintains consistency in format while preserving the innate patterns of missing data — a common challenge in electronic health records.

Experimental Framework and Key Findings

The empirical evaluation is grounded on three primary inquiries: the effectiveness of multitask learning across varied clinical tasks, the transferability of models to new tasks with limited data, and the efficacy of multimodal fusion strategies. The findings indicate substantial improvements in model performance attributed to:

1. Multitask Pretraining: Pretraining models on CLIMB yields marked improvements (up to 32.54% AUC in specific domains) when compared to single-task learning. This benefit is most pronounced in previously underexplored areas, emphasizing the value of diverse data integration.
2. Few-shot Transfer: CLIMB-trained models demonstrate significant adaptability in few-shot learning settings across diverse clinical tasks, highlighting their ability to generalize well even with limited novel task data. Improvements were noted across various modalities, achieving up to 29% in some tasks.
3. Multimodal Fusion: The research explores various fusion strategies for integrating clinical data. The results show that models pretrained on unimodal data from CLIMB can effectively enhance multimodal learning, fostering better task performance when appropriate fusion methodologies are adopted.

Implications and Future Directions

The introduction of CLIMB sets a new precedent for clinical AI research, offering a structured framework for holistic AI model development. The paper suggests that future developments could leverage CLIMB to explore novel architectures better suited for multimodal data integration and improve model robustness and interpretability in clinical settings.

From a practical standpoint, the findings underscore the potential of CLIMB to serve as a foundational dataset for training AI models that better mimic the integrative analysis conducted in clinical practice. Theoretically, it opens pathways for exploring new machine learning paradigms and algorithmic innovations that prioritize data diversity and integration.

In conclusion, this research does not merely contribute a dataset but paves the way for a paradigm shift in how clinical data is utilized, calling attention to the necessity of multimodal approaches to truly capture the complexity of human health. The release of CLIMB, along with detailed guidelines for its use, aims to stimulate further research in clinical AI, encouraging the development of more comprehensive and reliable models that can contribute significantly to patient care and clinical decision-making.