chrisThe explosion of AI-powered research tools can sometimes be overwhelming when trying to understand how different methodologies affect the depth, efficiency, and reliability of research. Two dominant AI approaches stand out:
- Automated AI Research Pipelines (e.g., DeepResearch, or as we will use here, Open DeepResearch Solutions) – Designed for structured, iterative deep dives into complex topics.
- Conversational AI Assistants (e.g., ChatGPT-4o) – Optimized for interactive, dynamic discussions and immediate knowledge retrieval.
In this article, we’ll use a real-world example to compare an open-source DeepResearch solution and ChatGPT, both utilizing OpenAI’s GPT-4o-mini, and examine how they differ in research methodology. We’ll explore how Open DeepResearch (for details on the solution used, see the end of the article) enhances research depth and when to use each AI tool effectively.
How AI Research Works: Structured vs. Conversational Approaches
DeepResearch: The Automated Research Pipeline
DeepResearch (and its open-source variants) is an AI-driven research system designed to automate structured research by leveraging:
- Search Query Optimization: AI-generated search queries to retrieve live web data.
- Automated Web Scraping: Extracting high-quality insights from search engine results (SERPs).
- Iterative Deep Research: Using past learnings to refine future searches.
- AI Summarization & Knowledge Extraction: Filtering information for key takeaways.
- Automated Report Writing: Compiling findings into structured reports.
- Error Handling & Adaptive Query Execution: Managing API rate limits, preventing timeouts, and adjusting research depth dynamically.
ChatGPT-4o: The Conversational AI Assistant
ChatGPT-4o is a powerful interactive AI designed for on-the-fly reasoning and discussion. It works by:
- Retrieving knowledge from pre-trained data (without live web access unless explicitly triggered).
- Generating real-time responses based on context.
- Refining answers dynamically based on user feedback.
- Facilitating brainstorming & exploratory discussions.
- Providing adaptive insights based on iterative user interaction.
While ChatGPT-4o excels in adaptability and quick knowledge retrieval, it lacks automated web searching and iterative learning across multiple queries—which (Open) DeepResearch compensates for.
Case Study: A Deep Dive into the European AI Act’s Impact on Medical Devices
To compare these AI models in real-world research, we conducted a case study focusing on the European AI Act and its implications for medical devices, particularly the European Commission’s guidelines on the definition of an AI system published on February 6, 2025.
Since these guidelines were published just 48 hours before this case study, they were unlikely to be retrievable from all but the bleeding edge of pre-trained conversational AI models.
Open DeepResearch’s Approach to the Problem
Step 1: Initial Research Question amp; Scope Definition
The research began with the user-supplied question:
“What are the European Commission’s AI system definitions to facilitate the first AI Act’s rules?”
To ensure comprehensive coverage, the DeepResearch model determined that it needed to answer:
- What specific aspects of the AI Act define an AI system?
- What are the implications of these definitions for medical devices?
- How does this align with existing medical regulations (MDR, IVDR, GDPR)?
- What compliance challenges do companies face?
Open DeepResearch’s Approach: Automatically structured the research into sub-queries for deeper exploration.
ChatGPT-4o’s Approach: Required iterative manual refinement from the user.
Step 2: Automated Query Generation amp; Research Planning
Open DeepResearch autonomously generated structured queries to extract live web data:
- “European Commission AI system definition February 2025 guidelines” (Finding official sources.)
- “Impact of European AI Act on medical device regulations” (Understanding regulatory changes.)
- “Comparison of AI Act with GDPR in healthcare” (Exploring compliance overlaps.)
- “Ethical considerations of AI in medical devices under European law” (Investigating bias, accountability, and patient safety.)
- “Future trends in AI regulation for medical devices in Europe” (Predicting regulatory changes and industry adaptation.)
Open DeepResearch Advantage: AI ensured search diversity and structured research breadth.
ChatGPT-4o Limitation: Required the user to manually refine search directions.
Step 3: Executing the Research amp; Iterative Expansion
Open DeepResearch executed searches and analyzed the retrieved content in multiple rounds:
- Initial Findings (Breadth: 6, Depth: 4)
- Deep Research Phase (Breadth: 3, Depth: 3)
- Final Depth Refinements (Breadth: 2, Depth: 2)
Step 4: AI-Powered Report Generation
Once research was complete, Open DeepResearch:
- Compiled findings into structured sections.
- Ensured coverage of all major aspects.
- Generated a multi-page final report.
- Included citations and visited URLs.
Open DeepResearch Advantage: AI automatically generated a structured report without manual intervention.
ChatGPT-4o Synergy: While ChatGPT-4o can also generate structured reports and summaries, it is most effective in refining and adapting reports for different audiences, such as executives, legal teams, or product developers.
Final Thoughts: A Hybrid AI Research Approach
The best approach? Combine both AI tools:
1️⃣ Start with ChatGPT-4o for brainstorming, refining research questions, and identifying core themes.
2️⃣ Run Open DeepResearch for structured, in-depth research with live data retrieval.
3️⃣ Use ChatGPT-4o again to interpret and refine the Open DeepResearch-generated report for different audiences.
By leveraging both AI approaches, you get the best of both worlds: instant adaptability + deep structured research.
Do you rely more on structured AI research or interactive AI discussions? Let’s discuss in the comments!
Note on Implementation
The system referred to as Open DeepResearch was implemented using a tweaked local version of David Zhang’s Deep-Research code, utilizing OpenAI and FireCrawl APIs. 🔗 GitHub Repository: dzhng/deep-research
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