โก Quick Summary
The study introduces LEADS, an AI foundation model designed for medical literature mining, trained on a vast dataset of 633,759 samples. LEADS outperformed existing large language models (LLMs) in key tasks, achieving a 0.81 recall in study selection and a 0.85 accuracy in data extraction, while saving significant time for clinicians.
๐ Key Details
- ๐ Dataset: 633,759 samples from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries
- โ๏ธ Technology: LEADS AI foundation model
- ๐ Performance: 0.81 recall in study selection, 0.85 accuracy in data extraction
- โณ Time savings: 20.8% in study selection, 26.9% in data extraction
๐ Key Takeaways
- ๐ค LEADS is a specialized AI model for enhancing evidence-based medicine.
- ๐ Performance improvements were noted over four leading LLMs across six literature mining tasks.
- ๐ฉโโ๏ธ User study involved 16 clinicians and researchers from 14 institutions.
- ๐ก Recall and accuracy metrics indicate LEADS’ effectiveness in real-world applications.
- โฐ Significant time savings can enhance productivity in literature reviews.
- ๐ Encourages future research on specialized LLMs using high-quality domain data.
๐ Background
The integration of artificial intelligence in systematic literature reviews holds immense promise for improving evidence-based medicine. However, the effectiveness of AI in this domain has been hampered by a lack of sufficient training and evaluation. The development of specialized models like LEADS aims to bridge this gap, providing a tailored solution for literature mining.
๐๏ธ Study
This study focused on creating LEADS, an AI foundation model trained on a comprehensive dataset comprising systematic reviews and clinical trial publications. The researchers conducted experiments to evaluate LEADS against existing LLMs, assessing its performance in tasks such as study search, screening, and data extraction.
๐ Results
LEADS demonstrated remarkable performance, achieving a 0.81 recall in study selection compared to 0.78 for traditional methods, resulting in a 20.8% time savings. In data extraction, LEADS reached an accuracy of 0.85, surpassing the 0.80 accuracy of conventional approaches, with a time savings of 26.9%. These results highlight the model’s potential to enhance expert productivity significantly.
๐ Impact and Implications
The findings from this study could transform the landscape of medical literature mining. By leveraging AI models like LEADS, healthcare professionals can achieve more efficient and accurate literature reviews, ultimately leading to better-informed clinical decisions. The implications extend beyond individual practices, potentially influencing the broader field of evidence-based medicine and research methodologies.
๐ฎ Conclusion
The introduction of LEADS marks a significant advancement in the application of AI for medical literature mining. With its impressive performance metrics and time-saving capabilities, LEADS paves the way for future research into specialized AI models that can further enhance expert productivity. The future of AI in healthcare looks promising, and continued exploration in this area is highly encouraged!
๐ฌ Your comments
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A foundation model for human-AI collaboration in medical literature mining.
Abstract
Applying artificial intelligence (AI) for systematic literature review holds great potential for enhancing evidence-based medicine, yet has been limited by insufficient training and evaluation. Here, we present LEADS, an AI foundation model trained on 633,759 samples curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. In experiments, LEADS demonstrates consistent improvements over four cutting-edge large language models (LLMs) on six literature mining tasks, e.g., study search, screening, and data extraction. We conduct a user study with 16 clinicians and researchers from 14 institutions to assess the utility of LEADS integrated into the expert workflow. In study selection, experts using LEADS achieve 0.81 recall vs. 0.78 without, saving 20.8% time. For data extraction, accuracy reached 0.85 vs. 0.80, with 26.9% time savings. These findings encourage future work on leveraging high-quality domain data to build specialized LLMs that outperform generic models and enhance expert productivity in literature mining.
Author: [‘Wang Z’, ‘Cao L’, ‘Jin Q’, ‘Chan J’, ‘Wan N’, ‘Afzali B’, ‘Cho HJ’, ‘Choi CI’, ‘Emamverdi M’, ‘Gill MK’, ‘Kim SH’, ‘Li Y’, ‘Liu Y’, ‘Luo Y’, ‘Ong H’, ‘Rousseau JF’, ‘Sheikh I’, ‘Wei JJ’, ‘Xu Z’, ‘Zallek CM’, ‘Kim K’, ‘Peng Y’, ‘Lu Z’, ‘Sun J’]
Journal: Nat Commun
Citation: Wang Z, et al. A foundation model for human-AI collaboration in medical literature mining. A foundation model for human-AI collaboration in medical literature mining. 2025; 16:8361. doi: 10.1038/s41467-025-62058-5