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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.16255 |
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| _version_ | 1866916585606742016 |
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| author | Wang, Zifeng Cao, Lang Jin, Qiao Chan, Joey Wan, Nicholas Afzali, Behdad Cho, Hyun-Jin Choi, Chang-In Emamverdi, Mehdi Gill, Manjot K. Kim, Sun-Hyung Li, Yijia Liu, Yi Ong, Hanley Rousseau, Justin Sheikh, Irfan Wei, Jenny J. Xu, Ziyang Zallek, Christopher M. Kim, Kyungsang Peng, Yifan Lu, Zhiyong Sun, Jimeng |
| author_facet | Wang, Zifeng Cao, Lang Jin, Qiao Chan, Joey Wan, Nicholas Afzali, Behdad Cho, Hyun-Jin Choi, Chang-In Emamverdi, Mehdi Gill, Manjot K. Kim, Sun-Hyung Li, Yijia Liu, Yi Ong, Hanley Rousseau, Justin Sheikh, Irfan Wei, Jenny J. Xu, Ziyang Zallek, Christopher M. Kim, Kyungsang Peng, Yifan Lu, Zhiyong Sun, Jimeng |
| contents | Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16255 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A foundation model for human-AI collaboration in medical literature mining Wang, Zifeng Cao, Lang Jin, Qiao Chan, Joey Wan, Nicholas Afzali, Behdad Cho, Hyun-Jin Choi, Chang-In Emamverdi, Mehdi Gill, Manjot K. Kim, Sun-Hyung Li, Yijia Liu, Yi Ong, Hanley Rousseau, Justin Sheikh, Irfan Wei, Jenny J. Xu, Ziyang Zallek, Christopher M. Kim, Kyungsang Peng, Yifan Lu, Zhiyong Sun, Jimeng Computation and Language Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining. |
| title | A foundation model for human-AI collaboration in medical literature mining |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.16255 |