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EPEE: towards efficient and effective foundation models in biomedicine
Zaifu Zhan1,2, Shuang Zhou2, Huixue Zhou3
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN USA.
Npj Health Systems
|May 15, 2026
Summary
We developed EPEE (Entropy- and Patience-based Early Exiting), a new method to speed up foundation models in healthcare. EPEE significantly cuts down inference time while keeping accuracy high for real-time clinical applications.
Area of Science:
- Biomedical informatics
- Artificial intelligence in healthcare
- Machine learning for clinical decision support
Background:
- Foundation models (e.g., GPT, CLIP) show promise in biomedical tasks.
- High inference latency and "overthinking" hinder real-time clinical use of these models.
Purpose of the Study:
- To introduce EPEE (Entropy- and Patience-based Early Exiting), a hybrid strategy to enhance foundation model inference efficiency.
- To address the trade-off between efficiency and effectiveness in biomedical foundation models.
Main Methods:
- Developed EPEE, combining entropy-based and patience-based early exiting strategies.
- Evaluated EPEE on classification, relation extraction, and event extraction tasks.
- Tested EPEE across eight foundation models (BERT, ALBERT, GPT-2, ViT, Qwen, GPT-oss, BioMistral, Meditron3) and twelve diverse datasets (clinical notes, medical images).
Main Results:
- EPEE significantly reduced inference time across all tested models and tasks.
- Accuracy was maintained or improved with EPEE compared to standard inference.
- Demonstrated adaptability of EPEE to various biomedical datasets and tasks.
Conclusions:
- EPEE effectively balances efficiency and effectiveness for biomedical foundation models.
- EPEE offers a practical solution for real-time clinical decision-making.
- This approach supports reliable and efficient clinical workflows using advanced AI models.
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