Research Efficiency

ChatGPT (vs. Gemini, Claude, Grok) for Clinical Research Writing: A Comparison and Guide

We compare the top LLMs and explore their optimal use for clinical research.

Introduction

The use of large language models (LLMs) in academic writing is no longer a futuristic concept. In clinical research, where clarity and efficiency matter, LLMs can help streamline the writing process, from structuring abstracts to tightening readability in manuscripts.

For my own clinical research I’ve dived DEEP into the  four of the most prominent LLMs today—ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google DeepMind), and Grok (xAI)—with a specific lens on clinical research writing. Below is a breakdown of what I think are their strengths, limitations, and how they stack up for researchers looking to boost productivity and quality.

Comparison of top LLMs for Clinical Research and Writing 


Which LLM should you use for clinical research writing 

1. ChatGPT (OpenAI)

Pros:

  • Currently has the largest ecosystem of tools and plugins.
  • Excellent at language polishing, tightening drafts, and summarizing complex scientific points.
  • Easy integration with workflows (copy/paste, browser, API).
  • Strong understanding of academic formatting (references, Vancouver style, etc.).
  • Reliable at keeping citations in plausible format (but authors still need to double check)

Cons:

  • Occasionally overconfident in generating references—hallucinations still happen.
  • Free versions are a big step down from paid models
  • Sometimes too "polished" and can sound like AI written content

Best Use: Refining drafts, summarizing articles, preparing cover letters.


2. Claude (Anthropic)

Pros:

  • Strong focus on safety and accuracy—tends to be more cautious, which is good in clinical writing.
  • Writing style often more ‘natural’ feeling compared with ChatGPT
  • Handles long context lengths well (can handle entire manuscripts at once).
  • Feels "more thoughtful" in complex ethical reasoning tasks.
  • Good at multi-step reasoning (e.g., revising based on journal instructions).

Cons:

  • Less fluent at stylistic polishing compared to ChatGPT.
  • Sometimes verbose or repetitive.

Best Use: Thoughtful rephrasing, responding to peer-review feedback, ethical nuance, drafting method sections where precision matters.


3. Gemini (Google)

Pros:

  • Access to Google ecosystem—quick fact-checking and integration with Google Docs is a plus.
  • Very good at pulling in factual information from recent literature.
  • Has the longest context window (can handle multiple long documents as context to help it write better)
  • Handles technical language well, including medical terminology.

Cons:

  • Less polished at creative rewording than ChatGPT.
  • Sometimes inconsistent in tone—needs tighter prompts.
  • Citations generation is not reliable; may fabricate references.

Best Use: Drafting outlines, creating checklists from reporting guidelines, generating structured abstracts.


4. Grok (xAI)

Pros:

  • Built for snappy, fast outputs, which is good for brainstorming.
  • Conversational tone can help break writer’s block.
  • Unique integration with X (Twitter), useful for turning papers into threads or outreach summaries.

Cons:

  • Least tested in academic/scientific writing.
  • Higher tendency for irreverent or casual tone—not always desirable in clinical manuscripts.
  • Lacks integration with academic tools (reference management, style guides).

Best Use: Brainstorming section headings, turning manuscripts into social media summaries, finding plain-language explanations.


The Ethics of Using LLMs in Clinical Research Writing

The use of LLMs in research writing raises valid ethical considerations. The most critical principle is transparency. If you use an LLM to help draft, revise, or edit a manuscript, you should disclose this in your acknowledgments (or specific AI Declaration section).

The truth is, LLMs can improve both the speed and quality of manuscript preparation, especially for non-native English speakers. They democratize scientific communication by helping researchers focus on science rather than formatting or grammar.

However, academia is often slow to adapt. Some reviewers or journals might still view LLM-assisted writing with suspicion, fearing plagiarism or fabrication of references. Until consensus guidelines are clearer, err on the side of full disclosure.

Of note, many journals indicate that AI used only for formatting (e.g. grammar or manuscript structuring) do not require declaration. This varies journal to journal, but as a general rule, if the AI is writing a significant portion of your manuscript you should declare. If it just making minor changes, no declaration needed. 


Key Takeaways for Using LLMs in Clinical Research Writing

1. Prompting Matters

  • Be explicit. Tell the model what you want: e.g., “Summarize this methods section according to STROBE guidelines.”
  • For clinical research, dictate the points you want to make in bullet form before asking for full-text drafts. This creates a blueprint that the LLM can then follow and populate.
  • Always provide context about the intended audience: e.g., “This is for an ICU-focused clinical journal.”
  • Don’t try to prompt everything in one go. Often, it takes 10+ prompts to get the content even close to what you want.

2. LLMs Work best for Summarizing and Rewriting

  • LLMs are often better at tightening and clarifying your existing text than generating high-quality de novo content.
  • Use them to improve readability, flow, and alignment with journal tone.
  • Avoid asking them to "write the whole paper" from scratch—it will feel generic and risk errors.

3. Fact-Check and Review Always

  • Never rely on LLMs for unverified references or claims. Cross-check all citations.
  • Use them to scaffold your work, not as a final product.

4. Transparency Builds Trust

  • Disclose your use of LLMs in manuscript preparation.
  • Maintain accountability for the scientific content—AI can’t take responsibility for errors.

5. LLMs Are Fantastic for Brainstorming

More than just writing tools, LLMs are powerful brainstorming partners. My main use for them isn’t drafting manuscripts—it's accelerating my thinking.

LLMs help you:

  • Spot gaps in your research or methods.
  • Simulate peer review: critique drafts, flag unclear reasoning, surface methodological risks.
  • Generate future directions, secondary analyses ideas, or lists of study limitations.
  • Reframe findings for different audiences—clinicians, policymakers, patients.
  • Rapid-fire generate title options, figure captions, or abstract summaries.
  • Explore "what if" scenarios for design or interpretation.

The real unlock is treating the model as a collaborative partner, not just a text generator. Prompt it, challenge it, iterate with it. You’ll find it pushes your thinking, sharpens your arguments, and helps you approach your research from new angles.

One PitFall to Avoid:

Avoid the “shiny new object” effect where each time a new LLM model is released you shift to using the newest model on a different platform. I advocate for researchers to choose the platform that works well for them, and then only switch between platforms if there is a specific strength / limitation you are hoping to exploit with another model. How human and AI synergize is more important than the ‘strength’ of the model at this stage for clinical writing. 

Conclusion

LLMs are rapidly changing how clinical researchers write, edit, and prepare their work for publication. Used wisely, they are powerful accelerators of productivity and quality. No model is perfect, and all require human oversight, but by understanding the strengths and limits of each, you can integrate them into your research workflow with confidence.

As with any tool in medicine, it’s not just about having it—it’s about knowing how to use it properly.

If you want to use a more niche AI tool that can accurately format your manuscripts for any journal in minutes check us out at Resub.