PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis
Open accessAbstract
The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers for vision-language alignment and diagnosis in a dialogic manner. Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data. In our work, we innovate by eliminating the text branch and substituting the dialogue head with a classification head. This approach presents a more effective method for harnessing LLMs in diagnosis with fewer learnable parameters. Furthermore, to balance the retention of detailed image information with progression towards accurate diagnosis, we introduce the contextual multi-token engine. This engine is specialized in adaptively generating diagnostic tokens. Additionally, we propose the information emitter module, which unidirectionally emits information from image tokens to diagnosis tokens. Comprehensive experiments validate the superiority of our methods.
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Studies have demonstrated that fine-tuning enhances the diagnostic precision of models through task-specific data and domain-specific knowledge.
“Studies have demonstrated that fine-tuning enhances the diagnostic precision of models through task-specific data and domain-specific knowledge34,35.”
From Enhancing diagnostic capability with multi-agents conversational large language models (2025) · cites this paper as [34]
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Large Language Models in Medical Image Analysis: A Systematic Survey and Future Directions
Urooj, B., Fayaz, M., Ali, S., Dang, L. M., & Kim, K. W. (2025) · Bioengineering · doi:10.3390/bioengineering12080818 · Cited by 22 · see citing works
Multimodal Large Models are Effective Action Anticipators
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Self-citationHaixian, L., Shu, P., Zhao, L., Chunfeng, L., & Lun, L. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1576461 · Cited by 19 · see citing works
Assessing the Accuracy of Diagnostic Capabilities of Large Language Models
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Self-citationThe Screening and Diagnosis Technologies Towards Pneumoconiosis: From Imaging Analysis to E-Noses
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DVG-Diffusion: Dual-View-Guided Diffusion Model for CT Reconstruction From X-Rays
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Self-citationIntelligent Diagnostic Framework for Pneumoconiosis
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Self-citationProACT: prototype-guided anchor-based test-time adaptation for pneumoconiosis diagnosis
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Self-citation
- Authors
- Song, M., Wang, J., Yu, Z., Wang, J., Yang, L., Lu, Y., Li, B., Wang, X., Wang, X., Huang, Q., Li, Z., Kanellakis, N. I., Liu, J., Wang, J., Wang, B., & Yang, J.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Northwestern Polytechnical University (CN)
- Harbin Medical University (CN)
- Tongji University (CN)
- Churchill Hospital (GB)
- Corresponding author
- Wang, Jiarui
- Wang, Xue
- Wang, Xiaoxu
- Liu, Jiangfeng
- Wang, Jing
- Wang, Binglu
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 97
- Pages
- 103248
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4399864536
- Cited by
- 31
- References
- 84
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 1
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Basic and Applied Basic Research Foundation of Guangdong Province
- Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- large language models, vision-language models, occupational diseases, parameter-efficient fine-tuning, vision-language alignment, chest CT
- MeSH terms
- Algorithms, Humans, Pneumoconiosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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