Aug 2026· Journal of Biomedical and Clinical Research· Vol 19, pp. 255-265· 0 citations· 15 references
TL;DR
This study took an article written by the authors of the current study and ran it through the models to help build prompts for the article to be written by the AI, and compared the results of the human-written article and the AI-generated ones.
Abstract
Large language models (LLMs), a type of artificial intelligence (AI), are increasingly popular tools used for everyday and work-related activities and tasks. Their application in medicine is widely researched and has been used recently to help write scientific papers in various fields. LLMs can draft sections of manuscripts or whole papers far more quickly than human writers. However, they need appropriate prompting to draft near-complete and worthwhile papers. In the current study, we used two different LLM models: ChatGPT-4o and Claude 3.5 Sonnet, to test AI’s scientific writing capabilities. We took an article written by the authors of the current study on the topic of fluorescent cholangiograms and ran it through the models to help build prompts for the article to be written by the AI. After that, we loaded the data and references used for the article into both AI tools and prompted them to write sections of the article (or a whole article if possible) on the same topic while allowing for independent choice of statistical analysis. The results of the human-written article and the AI-generated ones were compared, evaluating the information used from the references, the types of statistical analysis methods used, the conclusions drawn, and the time it took to complete the task.
This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.
Qiao Jin, Nicholas Wan, Robert Leaman et al.· Nature Protocols· 1 citation
Whether contemporary LLMs can reproduce the research outcomes of a fully documented human study: a 1991 article that identified dermatophytosis (ringworm) in historical fine art was evaluated.
Overall, it is concluded that model selection for domain RAG applications should be treated as a modular process that considers trade-offs between metric weights and insights from embedding-based clustering, so AskPumas can adapt its priorities as the LLM landscape evolves.
A. Vinchhi, Juan José González Oneto, Michael Hatherly et al.· Quantitative Medicine· 0 citations
Artificial intelligence has become an essential part of academic writing, where researchers rely on it at nearly
every stage of the research, writing and publication process. This review works through 13 widely used AI
research tools. These tools are GitMind, SciSpace, Consensus, Paperpal, Jenni, CitedEvidence, Elicit, Scite,
Logically, Genspark, Gemini Notebook, Julius, and Claude. The review is organized into two complementary
parts. The first part maps each tool to the stage of academic writing where it performs several tasks such as
idea generation, literature searching, evidence verification, drafting, language editing, data analysis, citation
management, peer-review preparation, and journal selection. The second part involves a hands-on look at each
platform on its own, covering its main features, interface, supported workflows, and export options. Finally,
a comparative workflow, quick-reference tables, and a worked case study show how several tools can fit
together into one coherent research work flow. The review also addresses some of the common limitations,
citation inaccuracies, AI hallucinations, and data privacy concerns. It emphasizes on the case that human
judgment and verification still matter more than ever. It closes with a summary of current recommendations
from major publishing organizations on transparency and disclosure around AI-assisted writing. Rather than
favoring any platform, this article provides researchers, graduate students, and educators with a practical
framework for choosing appropriate AI tools and integrating them responsibly across the full academic writing
lifecycle.
Abdulmajid Hesham, Asma Sharfeddin· South Mediterranean Universi...· 0 citations
The role of traditional models in current NLP research and practice is discussed, especially in contrast and comparison to modern neural network-based approaches including LLMs.
Robin Jegan· Schriften aus der Fakultät W...· 0 citations
This work study large language model (LLM)-based simplification of scientific texts and presents a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists.
Kyuri Im, Michael Färber· arXiv.org· 0 citations
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