230915 리뷰논문작성 브이로그 마지막 - 리비전 과정에서 ChatGPT 활용

English summary

This video describes practical workflows for using large language models, particularly ChatGPT, during the peer review revision stage of academic manuscripts. The main applications are restructuring tables, inserting and highlighting manuscript content, interpreting and organizing reviewer comments, and drafting response letters to editors and reviewers.

Key points

• The presenter demonstrates how to provide a distorted or unformatted table together with a reviewer request and specify an output format, such as a Markdown table, to reconstruct the table.
• By separating the original manuscript, proposed additional content, and task instructions, authors can ask the model to integrate new material into the text and highlight changes in bold. They can also iteratively check whether placing the material after a particular paragraph produces a coherent flow.
• Complex reviewer comments can be submitted for simplification, decomposition into bullet points, and reformulation as plain-language questions. The examples include distinctions between traditional natural language processing and large language models, patient-friendly information, and technical criteria.

Audience

The material is intended for academic authors, clinician researchers, graduate students, and other researchers responding to peer review.

Scope and limitations

The supplied material summarizes demonstrations and prompting practices from a video; it does not present an empirical study establishing the accuracy or efficiency of ChatGPT for manuscript revision. Model outputs may place text awkwardly or produce overly formal boilerplate, so authors must review and manually refine all generated text before submission. The scientific meaning and intended tone also require human validation.

Research context

The topic is thematically connected to the use of large language models and data-driven tools to support scholarly communication and manuscript revision in medical research.