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Harnessing Large Language Models in Medical Research and Scientific Writing: A Closer Look to The Future: LLMs in Medical Research and Scientific Writing
Open accessAbstract
Large Language Models (LLMs), a form of artificial intelligence generating natural language responses based on user input, have demonstrated potential across various applications such as entertainment, education, and customer service. This review comprehensively highlights their current research status and potential applications within the medical domain, addressing the challenges and opportunities for future development and implementation. Key aspects covered include diverse data sources for training and testing, such as electronic health records and clinical trials; ethical considerations, including privacy and consent; evaluation techniques focusing on accuracy and coherence; and clinical applications ranging from diagnosis to patient education. The review concludes that LLMs hold significant promise for enhancing the quality and efficiency of medical research and scientific writing but also emphasize the need for careful design and regulation to ensure safety and reliability.
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“Prior studies have mostly discussed potential applications and ethical implications of LLMs for scientific writing in medicine overall and – when performance was measured – analyzed often only one (typically proprietary) LLM.12–17”
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 [14]
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Qadhi, S. M., Alduais, A., Chaaban, Y., & Khraisheh, M. (2024) · Information · doi:10.3390/info15060325 · Cited by 106
Eminovic, S., Levita, B., Schmidt, R., Lindholz, M., Desser, D., Penzkofer, T., Wattjes, M. P., & Nawabi, J. (2026) · DIGITAL HEALTH · doi:10.1177/20552076261464745
Metadata
- Authors
- Abu-Jeyyab, M., Alrosan, S., & Alkhawaldeh, I.
- Affiliations
- University of Kansas Medical Center (US)
- Mutah University (JO)
- Venue
- High Yield Medical Reviews
- Publisher
- High Yield Medicine
- Published
- 2023-12-09
- Volume
- 1
- Issue
- 2
- ISSN
- 2960-1630 (ISSN-L)
- OpenAlex
- W4389508559
- Cited by
- 17
- References
- 7
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 6
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- High Yield Medical Reviews (publishedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- large language models, medical research, scientific writing, electronic health records, clinical trials, privacy, informed consent, LLM evaluation, diagnosis, patient education, AI regulation, medical ethics
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