How to Use DeepResearch and Claude Workflow to Master a New Field Quickly

I regularly use Claude Code, ChatGPT, and Codex for medical‑focused Vibe Research, and I need a way to enter new research domains quickly. Traditionally, a thorough paper review of the past 3‑5 years would take one to two weeks, which is costly in an enterprise setting.

With AI, the same process can be compressed into 1–2 hours. My current method consists of two stages:

Run a ChatGPT DeepResearch pass on the target domain. For example, when investigating the MICCAI 2026 TopAneu challenge on vessel‑specific intracranial aneurysm detection, I ask ChatGPT to produce a research report covering recent literature, problem definition, and potential approaches.

Start a new Claude Code session in ultracode effort mode and launch a /deep-research or Dynamic workflow. I feed Claude the following context:

The PDF report generated by ChatGPT DeepResearch.

The 37‑page TopAneu challenge specification PDF.

My local Zotero database of CTA/MRA aneurysm papers (accessed via Zotero MCP).

A request to retrieve the last five years of arXiv/PubMed papers on aneurysm detection.

The TopAneu dataset, already downloaded.

Claude then executes a dynamic, cross‑validated workflow, producing a detailed report that can serve both as a learning guide for intracranial aneurysm imaging AI and as a competition‑specific plan. Using this report, I was able to generate a baseline for the TopAneu competition within two days, after which Claude can iteratively improve the solution based on experimental results.

Key practical notes:

Ensure the supplied literature is authentic; Zotero‑managed papers are reliable, while arXiv papers should be filtered for top‑journal or Tier‑1 conference status.

Claude’s dynamic workflow consumes many tokens, so align usage with your subscription limits.

This simple yet often overlooked method dramatically reduces the time required for deep domain onboarding while maintaining a level of analysis comparable to a PhD‑level researcher.