230811 리뷰논문작성 브이로그 7일차 - 레퍼런스삽입, 서식정리, 투고완료

English summary

This video presents the final editing and submission workflow for a review paper on the emerging role of large language models in clinical trials. It covers literature and reference verification, AI-assisted refinement of the abstract and manuscript, MeSH validation, simulated peer review, preparation of submission files, and final approval through Editorial Manager for Translational and Clinical Pharmacology.

Key points

• Obsidian and Zotero with word-processing add-ins were used to organize literature and references, while studies first identified as preprints were rechecked against their subsequent journal publications.
• ChatGPT-4 was used to draft, condense, simplify, and iteratively revise sections of the manuscript, including stylistic, flow, and grammar checks.
• MeSH terms proposed by ChatGPT were manually checked against the official National Library of Medicine MeSH database. The video notes that AI may generate plausible but nonexistent MeSH terms. * ChatGPT was prompted to act as a critical scientific-journal reviewer and to identify strengths, weaknesses, ethical considerations, and possible revisions. * The review paper addressed large language model applications in patient-trial matching and prescreening, clinical-trial planning, extraction and coding of free-text medical records, technical documentation, and informed-consent support. * Submission preparation involved separate files for the cover letter, title page, blinded manuscript, tables, author checklist, copyright transfer agreement, and conflict-of-interest disclosure form, followed by entry of classifications and questionnaires, review of the generated PDF proof, and final approval.

Audience

Clinical trialists, clinical pharmacologists, medical writers, biomedical informatics researchers, and advanced learners interested in applying large language models to clinical workflows and scholarly manuscript preparation

Scope and limitations

This summary is based on evidence notes from a video showing the completion and submission of one review manuscript. The review was described as a narrative overview rather than a systematic review and did not provide a systematic search strategy or comparative outcome analysis for the cited applications. AI-generated references and MeSH terms require independent verification against official journal records and the National Library of Medicine database.

Research context

The video is thematically connected to Sangzin Ahn's portfolio through the neutral topics of large language models for clinical-trial support, medical data processing, and AI-assisted scholarly writing within medical AI and data science.