CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Authors: Wei Dai, Peilin Chen, Malinda Lu, Daniel A Li, Haowen Wei, Hejie Cui, Paul Pu Liang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 singletask learning. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology 2Athinoula A. Martinos Center for Biomedical Imaging 3Harvard Medical School 4Stanford University. Correspondence to: Wei Dai <EMAIL>. |
| Pseudocode | No | The paper describes the CLIMB framework and experimental procedures but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at this link. |
| Open Datasets | Yes | CLIMB unifies diverse public clinical datasets into a unified benchmark designed specifically for developing and evaluating multimodal medical AI systems. |
| Dataset Splits | Yes | Split: For multitask training, we use the Bench MD split, which includes label remapping to 7 diagnostic categories. This split consists of 17,476 records in the training set and 4,361 records in the test set, totaling 21,837 records. |
| Hardware Specification | Yes | All experiments are ran on a GPU server with 8x H200 141GB GPUs. |
| Software Dependencies | No | All experiments were conducted using the Py Torch framework. |
| Experiment Setup | Yes | Depending on the model sizes, we use a parameter search to identify the optimal learning rate from 1e-5 to 1e-3 for all experiments. The weight decay was set to 1e-3. |