Should you use AI to write life science content?

When generative artificial intelligence (AI) models hit the mainstream in 2022, the response was a mix of excitement and trepidation.

Schools of thought went from ‘wow, this is going to save me so much time’ to ‘oh no, this might take my job’, and everything in between.

Today, those models have evolved and so too has our understanding of how to use them, their capabilities and, most importantly, their limitations. According to HubSpot, 80% of marketers are using AI for content creation, with many of us using it every day to help distil information, create a first draft or as a sounding board for idea generation. 

Meanwhile, others are still side-eyeing AI with suspicion, ready to say “I told you so!” the moment it gets something wrong. 

But in reality, AI tools are just that: tools. They’re not your colleague, your subject matter expert (SME) or your freelancer, but simply a tool. They cannot take responsibility for the accuracy or originality of the outputs they provide; that judgement lies solely with the person using them. 

For writers and marketers, AI can help speed up the drafting process and articulate complex findings in accessible ways. But in industries such as life sciences, where evidence, accuracy and context are key, further care must be taken to ensure it is used responsibly. 

At ramarketing, we haven’t shied away from using these tools. Instead, we’ve carefully tested them to understand the full range of their capabilities and limitations.

Here, we share what we’ve learned about using AI responsibly for life science writing, including where it helps, hinders and why human oversight is always a non-negotiable. 

What can’t AI do?

A key draw of AI for many is that it can present convincing copy very quickly. Type in even a short prompt for an article on a topic and within seconds, you’ll have 500 words in front of you. 

But convincing copy doesn’t equal credible content. 

Generative AI doesn’t understand your business, your science or your audiences. It cannot create anything new; it identifies patterns of information already available and presents it in what it concludes is a useful response. 

It cannot bring genuine scientific understanding, creativity or commercial context to the page. That has and always will be the role of the writer. 

Some things AI can get wrong when it comes to scientific writing include: 

• Stating inaccurate information with confidence
• Fabricating statistics, quotations or references
• Confusing related scientific topics
• Using incorrect words or phrases when describing scientific processes or context
• Removing important nuances to prioritize simplifying a point
• Filling in gaps instead of flagging missing information

These risks are particularly significant in life sciences, where a small factual error or unsupported claim can undermine confidence and credibility in the overall piece.

AI also can’t decide what your organization genuinely thinks, and therefore cannot share a meaningful point of view without input from those who understand the market and subject matter at hand. 

In other words, it can’t think for you, but it can support you in expressing your thoughts on the page. 

Okay, what can it do? 

Quite a lot, when given the right instructions.

Used well, AI can support with repetitive tasks, freeing up time for work that requires the critical and creative thinking that only an experienced writer can bring.

This might include: 

• Identifying recurrent themes or findings from source materials
• Supporting with content angles or repurposing opportunities
• Helping distill insights across various sources
• Highlighting repetition, jargon or unclear language 

Another valuable use of AI when drafting content, both creative and technical, is to treat it as a sparring partner for refining ideas. 

Share a train of thought for a content theme and ask it to challenge your thinking. Are there any blind spots you’re missing? Are there any assumptions you’re making that need to be backed up by evidence? Have you actually addressed the audience’s questions? 

Instead of trying to outsource ideas, use AI to build on your own thinking and provide it with the subject matter expertise, creativity and human judgement it needs to make the content as strong as possible. 

What are the giveaways?

When you spend enough time reading online content, you begin to recognize many common patterns in AI-generated copy. 

While not all AI copy is easy to spot, being aware of common giveaways can help you identify where your own content might require more originality and scrutiny.

Many common AI giveaways include: 

• Negative parallelisms (such as ‘it’s not just about X, it’s about Y’)
• Tautologies that repeat the same point in different words
• Overreliance on lists of threes 
• Clipped emphasis such as ‘that matters’ 
• Overly conversational interjections such as ‘and honestly?’ or ‘but here’s the thing’ 

Of course, removing a few phrases isn’t enough to give a piece true value. Question if the content could easily apply to a competitor, or if it has enough evidence and experience woven in to ensure credibility. If you’re unsure, it likely needs more human input.

What does responsible AI use look like? 

Using AI responsibly means putting clear boundaries in place between technology use and experienced human oversight.

It may help develop or test an idea, but it does not set the strategy or make decisions. Every output is reviewed by someone who understands the subject, and any technical details or claims are checked before they become part of the final work.

Taking this approach allows users to benefit from its capabilities while ensuring that responsibility sits with the experts behind the work. 

So, where does the human come in?

Throughout the entire process.

Human oversight is always key, as it is our responsibility to define the audience, shape the argument, do the research, verify the claims and ultimately, create a narrative that audiences want to read. 

AI cannot replicate the value of working with real writers, SMEs, strategists, digital experts and creative teams who bring years of experience and sector knowledge to the table. 

These are the people who know the right questions to ask, the strategic approaches to take, when to pivot and which messages need to be developed and refined to make a real impact. 

AI can support these teams, but it cannot replace them. 

So, should life science marketers be using AI? 

Yes, as long as the tool isn’t the main writer.

At ramarketing, AI is a valuable resource in helping our teams explore ideas and challenge their thinking, enabling us to move more efficiently towards the final draft. 

But it can’t replace the real expertise, creativity and strategic thinking that bring real value to our work.

Our strategists, writers, digital specialists, creatives and life science experts lead the thinking, shape the narrative and challenge the approach to ensure we hit our client’s priorities.

Learn more about how we’re using AI at ramarketing responsibly to support us in delivering work that makes a difference.