DeepSeek is open-access and the next AI disrupter for radiology
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Open-source models like DeepSeek-R1 offer transparency, local deployment, and the ability to tailor the model to sensitive healthcare data, though they demand more in-house technical expertise.
“Open-source models like DeepSeek-R1 offer transparency, local deployment, and the ability to tailor the model to sensitive healthcare data, though they demand more in-house technical expertise.”
From Open-source versus proprietary large language models for academic writing in medicine: A controlled evaluation of DeepSeek-R1 and ChatGPT-o1 in neuroradiology (2026) · cites this paper as [8]
The Future challenges section says DeepSeek’s open weights and low resource requirements make local deployment and in-house AI training feasible for large healthcare organizations. It also flags missing training code as a transparency concern. The paper does not establish that the model can be tailored to sensitive healthcare data or that doing so demands more in-house technical expertise. (LLM, conf=0.85)
Experimental — accuracy not yet measuredOpen-source development fosters reproducibility, community-driven improvements, and equitable access.
“Moreover, open-source development fosters reproducibility, community-driven improvements, and equitable access.7,8”
Proprietary models demonstrate strong general performance, but their use is hindered by limitations in transparency, customizability, data privacy, and reproducibility.
“While proprietary models demonstrate strong general performance, their use is hindered by limitations in transparency, customizability, data privacy, and reproducibility.7,8”
Both DeepSeek-R1 and ChatGPT-o1 employ chain-of-thought reasoning.
“Both LLMs employ “chain-of-thought”-reasoning, that guides to models to follow and “deep think” a reasoning process when dealing with complex problems.8,10”
Open-source LLMs offer full access to weights and architecture, enabling local deployment, fine-tuning on institutional data, and alignment with privacy and governance requirements.
“These models offer full access to weights and architecture, enabling local deployment, fine-tuning on institutional data, and alignment with privacy and governance requirements.”
DeepSeek-R1's public weights and modest hardware needs enable institutions to run and refine the model on-site, but undisclosed training data and code leave questions about provenance, reproducibility, and governance.
“Its public weights and modest hardware needs enable institutions to run and refine the model on-site, but undisclosed training data and code leave questions about provenance, reproducibility, and governance. 8”
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cites
No references could be resolved yet.
Research works printed but not linked: 4
- [1]DeepSeek AI, Guo D, Yang D, et al DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. arXiv [csCL]. Published online January 22, 2025. http://arxiv.org/abs/2501.12948, preprint: not peer reviewed. · arXiv
- [4]DeepSeek AI, Liu A, Feng B, et al DeepSeek-V3 technical report. arXiv [csCL]. Published online December 26, 2024. http://arxiv.org/abs/2412.19437, preprint: not peer reviewed. · arXiv
- [5]Peng H, Wu K, Wei Y, et al FP8-LM: training FP8 large language models. arXiv [csLG]. Published online October 27, 2023. http://arxiv.org/abs/2310.18313, preprint: not peer reviewed. · arXiv
- [10]Tam TYC, Sivarajkumar S, Kapoor S, et al A framework for human evaluation of large language models in healthcare derived from literature review. NPJ Digit Med. 2024; 7(1): 258. 10.1038/s41746-024-01258-7 39333376 PMC11437138 · DOI · Paper page
Cited by
Sorted by citations
Eminovic, S., Levita, B., Schmidt, R., Lindholz, M., Desser, D., Penzkofer, T., Wattjes, M. P., & Nawabi, J. (2026) · DIGITAL HEALTH · doi:10.1177/20552076261464745
- Authors
- Peng, Y., Chen, Q., & Shih, G.
- Affiliations
- Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10022,
- Department of Biomedical Informatics and Data Science, Yale School of Medicine , New Haven, CT 06510,
- Department of Radiology, Weill Cornell Medicine , New York, NY 10065,
- ORCID
- Peng, Yifan
- Venue
- Radiology Advances
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-01
- Volume
- 2
- Issue
- 1
- ISSN
- 2976-9337
- PMC
- PMC12363671
- Cited by
- 16
- References
- 0
- Language
- en
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