Smarter Together: Combining Large Language Models and Small Models for Physiological Signals Visual Inspection
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909695399165952 |
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| author | Li, Huayu He, Zhengxiao Chen, Xiwen Zhang, Ci Quan, Stuart F. Killgore, William D. S. Wung, Shu-Fen Chen, Chen X. Yuan, Geng Lu, Jin Li, Ao |
| author_facet | Li, Huayu He, Zhengxiao Chen, Xiwen Zhang, Ci Quan, Stuart F. Killgore, William D. S. Wung, Shu-Fen Chen, Chen X. Yuan, Geng Lu, Jin Li, Ao |
| contents | Large language models (LLMs) have shown promising capabilities in visually interpreting medical time-series data. However, their general-purpose design can limit domain-specific precision, and the proprietary nature of many models poses challenges for fine-tuning on specialized clinical datasets. Conversely, small specialized models (SSMs) offer strong performance on focused tasks but lack the broader reasoning needed for complex medical decision-making. To address these complementary limitations, we introduce \ConMIL{} (Conformalized Multiple Instance Learning), a novel decision-support framework distinctively synergizes three key components: (1) a new Multiple Instance Learning (MIL) mechanism, QTrans-Pooling, designed for per-class interpretability in identifying clinically relevant physiological signal segments; (2) conformal prediction, integrated with MIL to generate calibrated, set-valued outputs with statistical reliability guarantees; and (3) a structured approach for these interpretable and uncertainty-quantified SSM outputs to enhance the visual inspection capabilities of LLMs. Our experiments on arrhythmia detection and sleep stage classification demonstrate that \ConMIL{} can enhance the accuracy of LLMs such as ChatGPT4.0, Qwen2-VL-7B, and MiMo-VL-7B-RL. For example, \ConMIL{}-supported Qwen2-VL-7B and MiMo-VL-7B-RL both achieves 94.92% and 96.82% precision on confident samples and (70.61% and 78.02%)/(78.10% and 71.98%) on uncertain samples for the two tasks, compared to 46.13% and 13.16% using the LLM alone. These results suggest that integrating task-specific models with LLMs may offer a promising pathway toward more interpretable and trustworthy AI-driven clinical decision support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smarter Together: Combining Large Language Models and Small Models for Physiological Signals Visual Inspection Li, Huayu He, Zhengxiao Chen, Xiwen Zhang, Ci Quan, Stuart F. Killgore, William D. S. Wung, Shu-Fen Chen, Chen X. Yuan, Geng Lu, Jin Li, Ao Artificial Intelligence Machine Learning Signal Processing Large language models (LLMs) have shown promising capabilities in visually interpreting medical time-series data. However, their general-purpose design can limit domain-specific precision, and the proprietary nature of many models poses challenges for fine-tuning on specialized clinical datasets. Conversely, small specialized models (SSMs) offer strong performance on focused tasks but lack the broader reasoning needed for complex medical decision-making. To address these complementary limitations, we introduce \ConMIL{} (Conformalized Multiple Instance Learning), a novel decision-support framework distinctively synergizes three key components: (1) a new Multiple Instance Learning (MIL) mechanism, QTrans-Pooling, designed for per-class interpretability in identifying clinically relevant physiological signal segments; (2) conformal prediction, integrated with MIL to generate calibrated, set-valued outputs with statistical reliability guarantees; and (3) a structured approach for these interpretable and uncertainty-quantified SSM outputs to enhance the visual inspection capabilities of LLMs. Our experiments on arrhythmia detection and sleep stage classification demonstrate that \ConMIL{} can enhance the accuracy of LLMs such as ChatGPT4.0, Qwen2-VL-7B, and MiMo-VL-7B-RL. For example, \ConMIL{}-supported Qwen2-VL-7B and MiMo-VL-7B-RL both achieves 94.92% and 96.82% precision on confident samples and (70.61% and 78.02%)/(78.10% and 71.98%) on uncertain samples for the two tasks, compared to 46.13% and 13.16% using the LLM alone. These results suggest that integrating task-specific models with LLMs may offer a promising pathway toward more interpretable and trustworthy AI-driven clinical decision support. |
| title | Smarter Together: Combining Large Language Models and Small Models for Physiological Signals Visual Inspection |
| topic | Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.16215 |