o3로 논문 리뷰어 추천받기 (2025.4)

English summary

This video demonstrates using an agentic, sequential-reasoning model, ChatGPT o3, to define peer-reviewer selection criteria and identify qualified candidates for an academic manuscript. The workflow first generates criteria from a manuscript PDF and then uses o3 to conduct multiple web searches and review candidate information.

Key points

• The demonstrated two-stage workflow consists of criteria generation and candidate sourcing.
• The criteria-generation stage considered medical education expertise, knowledge of artificial intelligence in healthcare, familiarity with the concept of the Ironies of Automation, recent publications from 2023 to 2025, the South Korean context, and avoidance of conflicts of interest.
• For candidate sourcing, o3 was described as refining queries and cross-checking information across sources such as JMIR, KJME, PMC, and ResearchGate to identify five potential South Korean reviewers. The reported output presented each candidate's name, position and affiliation, rationale for fit, and a recent paper or relevant project in a table.

Audience

Academic authors preparing manuscript submissions, journal editors and editorial office staff, and researchers examining artificial intelligence assisted workflows in scholarly publishing

Scope and limitations

This account is based on a single video's demonstration and explanation. It does not report a systematic evaluation of recommendation accuracy, reproducibility, bias, search omissions, or effects on actual editorial performance. Human verification of institutional overlap, recent collaborations, and conflicts of interest is still required, and specifying table column headers may improve output consistency.

Research context

The topic is relevant to medical artificial intelligence and data science through its focus on large language models for literature searching, candidate screening, and editorial support workflows in scholarly publishing.