Writing Scientific Content in the Age of AI: Why Human Expertise Remains Essential

Artificial intelligence (AI) has become a significant force in the content creation landscape, from marketing copy to clinical trial summaries. Algorithms can now generate polished text within seconds, raising an important question for the world of scientific writing: Should we allow AI to handle the heavy lifting?

The answer is nuanced. AI tools can be remarkably helpful for certain tasks, but scientific communication is not just about grammar or structure. It requires clarity, credibility, and context. When public health, investor confidence, or regulatory approvals are at stake, human expertise is not optional - it is essential.

The Potential of AI in Scientific Writing

The appeal of AI-assisted writing is undeniable. Tools such as ChatGPT, Claude Opus, and GPT-powered research assistants like Elicit, Consensus, or ChatGPT's Advanced Data Analysis offer impressive capabilities. They can summarize complex material, simplify technical language, and even help generate structured outlines or initial drafts.

AI is also increasingly used in the research phase preceding content creation. For example:

• Literature review assistants can identify relevant papers, organize citations, and provide structured summaries of key findings.

• Machine learning-powered data analysis tools are already being used to identify patterns in massive datasets. In fields like oncology, AI aids in detecting breast cancer in medical imaging, sifting through large omics datasets, and supporting hypothesis generation.

For time-pressed teams, AI can support:

• Organizing and synthesizing literature

• Suggesting themes or relationships across studies

• Analyzing large datasets for publication-ready visuals or discussion points

• Generate figures or propose the visuals based upon the data

• Drafting summaries, outlines, or first drafts

• Rewording scientific content for broader audiences

• Structuring common formats (e.g., IMRaD, abstracts)

These tools are particularly useful in early-stage ideation or for accelerating background research. In short, AI can enhance the speed and scale of writing preparation. However, it still cannot interpret results, determine relevance, or decide how best to communicate findings for strategic impact.

The Risks and Limitations of AI

While powerful, AI is not infallible. One of its most persistent and problematic flaws is hallucination - the generation of incorrect or entirely fabricated information. This includes nonexistent citations, misattributed data, or subtly incorrect statements presented with complete confidence.

Additional limitations include:

• Source unreliability: AI may cite journal articles that do not exist or confuse authors and study titles.

• Lack of contextual awareness: It cannot evaluate study quality, clinical significance, or strategic relevance.

• No grasp of regulatory nuance: AI tools are not trained to align with evolving FDA, EMA, or ICMJE guidance.

• Style over substance: AI often produces generic, overwritten content filled with vague qualifiers and unnecessary elaboration.

• Annoying punctuation habits: If you have ever seen a paragraph stitched together with em dashes, you have likely encountered AI’s idea of “sophistication.”

Ultimately, AI produces content that sounds scientific but sounding smart is not the same as being correct, strategic, or credible.

Where Human Expertise Remains Irreplaceable

AI can support scientific writing by generating text, reformatting citations, and summarizing studies. But it lacks a fundamental element: understanding.

This is where human expertise remains essential:

• Strategic Positioning: AI does not understand competitive landscapes, clinical timelines, or the subtle implications of framing a result in different ways. Only experienced professionals can align scientific content with business goals and regulatory context.

• Prioritizing What Matters: A human writer can decide which data points deserve emphasis, which findings are most compelling to a given audience, and which results require cautious framing.

• Ensuring Accuracy and Source Integrity: AI frequently generates fabricated or misattributed citations, even when prompted carefully. Only a human expert can verify whether a referenced study actually exists, is correctly cited, and supports the claims being made.

• Handling Nuance and Risk: AI cannot reliably identify ethical sensitivities, reputational risks, or political implications. For example, it cannot anticipate how a statement about gene editing or mRNA might be received by a skeptical audience.

• Cross-functional Alignment: Scientific communication is rarely a solo effort. Human writers coordinate feedback from regulatory teams, scientific leads, and executives. AI cannot manage multiple, sometimes conflicting perspectives.

• Tone and Voice Calibration: AI-generated text often defaults to bland generalizations or verbose language. Humans bring tone, restraint, and voice that reflect the brand, the audience, and the scientific gravity of the content.

• Deciding When to Say Less: Sometimes, the smartest choice is what you leave out. That judgment is uniquely human.

In short, AI can generate content, but humans ensure it communicates what matters to the right people in the most effective way.

A Collaborative Future: Humans with AI

AI is not the enemy. Used thoughtfully, it can enhance workflows and reduce manual strain. The best approach is a collaborative one - let AI assist with certain tasks while human experts guide the overall process, ensure accuracy, and make strategic decisions.

A well-designed human-AI workflow for scientific writing might include:

1. Research and Ideation:

   • Use AI-powered literature review tools to surface relevant studies, organize citations, and provide initial summaries

   • Leverage AI to analyze large datasets and suggest potential themes, connections, or discussion points

   • Human experts review AI-generated insights, assess relevance, and prioritize key points to include

2. Outlining and Drafting:

   • AI generates initial outlines or section drafts based on human-provided guidance and priorities

   • Human writers review, refine, and expand AI-generated content, ensuring accuracy, clarity, and strategic alignment

   • Iterate between AI drafts and human edits until a cohesive, well-structured draft is achieved

3. Revision and Enhancement:

   • AI assists with tasks like simplifying technical language, formatting references, and suggesting visuals 

   • Human experts fine-tune the narrative flow, sharpen the key messages, and ensure the content meets audience needs

   • Use AI to check for consistency, flag potential errors, and suggest improvements

   • Human writers make final revisions and incorporate feedback from cross-functional stakeholders

4. Fact-Checking and Verification:

   • AI tools cross-reference claims with reputable sources and flag any unsupported or conflicting statements

   • Human experts carefully verify every citation, statistic, and key assertion against original sources

   • Address any discrepancies or inaccuracies identified by AI or human review

5. Finalization and Approval:

   • Human writers, in collaboration with scientific leads and regulatory teams, make final decisions on framing, emphasis, and risk mitigation

   • Ensure the final piece aligns with all strategic, legal, and brand standards

   • Human experts provide final approval before publication or submission

   
   

Throughout this process, think of AI as an eager but inexperienced assistant. It can accelerate certain tasks and offer useful starting points. But it still needs the guidance, oversight, and decision-making abilities of human experts.

By leveraging the speed and scale of AI while retaining the judgment and nuance of human expertise, teams can create scientific content that is not only accurate and compliant, but also strategically impactful. The key is to use AI thoughtfully, always keeping human wisdom at the center of the process.

Conclusion

Artificial intelligence is changing how we work, and that change can be positive if approached responsibly. However, scientific communication still demands interpretation, accountability, and intent - skills that are uniquely human.

At Bulmore Consulting, we use tools that support expertise, not replace it. If you are thinking about how to integrate AI into your scientific communications process without compromising quality, we are ready to help.