Feb 22, 2026
By Neel Sheth, MD
In April 2026, it will be one year since I joined Peer AI. At a startup, time is accelerated and one year can feel like five.
The pace is fast, the Peer AI platform is continuously evolving, and the conversations I have with medical writers keep changing. What hasn’t changed is the core question I hear again and again: what value does AI actually provide for medical writing when it’s used in real workflows, under regulatory expectations, and with quality on the line?
A couple of years ago, most conversations about AI in medical writing were exploratory. Writers were experimenting on their own, trying to see what the tools could do and where the risks were. Today, that mindset has shifted. The question I hear now isn’t whether AI belongs in medical writing, but how to use it effectively and responsibly. AI has moved out of experimentation and into enablement.
That shift matters because medical writing is not casual work. It supports regulated deliverables that must stand up to the highest scrutiny. Documents are reviewed, challenged, revised, and reused across programs, and the output has to be defensible. Over time, you realize the job is not just writing. It is interpretation, consistent messaging, judgment, strategic thinking, and coordination across documents that are tightly linked to one another.
One of my former colleagues used to describe medical writing as both the engine and the caboose of the train. You can’t start without it, nor can you finish without it. That description has stayed with me because it captures how vital medical writing is to getting a program moving and seeing it through to the end. Efficiency matters, but not if it comes at the expense of quality or clarity.
AI is not replacing medical writers. It is changing how they work.
One of the most common concerns I hear from medical writers is that AI will replace their role. That fear is understandable. The pace of change has been fast, and AI is suddenly everywhere. When I attended AMWA last November, more than half the booths had some kind of AI component. A few years ago, that would have been hard to imagine.
After a year working closely with these tools, my perspective has shifted. What I see instead is amplification rather than replacement. When AI is paired with a medical writer’s experience and strategic thinking, it creates a stronger and more efficient professional, not a redundant one.
What does change is how time is spent. I expect medical writing to keep moving away from pure document production and toward more strategic work. That means greater focus on scientific interpretation, regulatory strategy, risk benefit framing, and maintaining consistent messaging across tightly linked documents. These are the areas where human expertise remains essential and where AI still cannot operate on its own.
There is a practical reality that often gets overlooked. Medical writers experience real fatigue, and much of it comes from the earliest stages of drafting. Initial document population, building tables, extracting data from listings and figures, and pulling information from dozens of source documents are time-consuming and repetitive. A clinical study report may draw from 70 or more inputs. Traditionally, a writer has to locate each piece of information and manually assemble the first draft before higher-level review can even begin.
This is where AI can make a meaningful difference. If the system can pull information into the draft early, the writer can spend less time on assembly and more time on review and interpretation. I often think about it as the difference between hand cutting and hauling all the lumber yourself versus having the pieces pre-cut and partially constructed so you can focus on finishing the work of building. Some people enjoy the measuring and cutting. Many do not. Most medical writers would rather spend their time reviewing data and shaping the story than populating tables by hand.
The work does not disappear. It shifts toward the parts of the job that require strategic and critical thinking.
Enablement only works when process and oversight stay intact. One of the clearest lessons from the past year is that AI is most effective when strong process and work flow already exists. It does not create quality on its own. It reflects the structure, templates, and source material it is provided. When those foundations are weak or inconsistent, the output is limited no matter how advanced the technology is.
This is also where much of the noise in the market comes from. Many tools describe themselves as AI-powered, but until you actually use them in real workflows, it is hard to understand where they work well and where they break down. Hallucinations remain a real risk. Overpromising is another. In regulated medical writing, the idea that AI can operate independently simply does not hold up in practice.
At Peer AI, we are explicit about where AI fits and where it does not. The technology is powerful, but it requires human oversight. Final quality remains human-owned. That is not a philosophical position. It is a practical requirement when you are producing regulated deliverables.
Even where AI fits in the document lifecycle reflects this reality. Today, AI is used primarily for early drafting. Most organizations are not using it for final documents, and neither are we. At Peer AI, our focus begins with draft one and supports the review process that follows.
In practice, documents move through multiple rounds of review, often four or five, before they are finalized. The goal is not to remove steps from that workflow. The steps remain the same. The goal is to move through them more efficiently and faster.
Internally, we describe this as a loop between AI and the human reviewer. AI accelerates the path to the next human touchpoint. Review happens sooner. Revisions happen sooner.
The loop moves faster without removing the judgment that makes the output reliable. That is what enablement looks like in practice.
Consistency across documents is where the next gains will compound.
If there is one challenge that consistently frustrates medical writers, it is maintaining consistency across documents. Keeping a single document aligned is hard enough. Doing it across multiple writers, reviewers, and tightly linked deliverables is far harder.
Changes are inevitable. A physician revises an interpretation. A statistician updates a data point. A key message shifts. Each change has to be traced and reflected everywhere it appears, or risk creating inconsistencies that surface later.
That work is time-consuming, and it introduces risk. It is easy to believe everything has been updated correctly, only to discover later that it was not. In regulated environments, even small inconsistencies can trigger delays and rework at exactly the wrong moment.
This is where I expect to see the most meaningful progress over the next one to two years. Better reuse of structured content. More pre-populated templates. Stronger connections between data and text. And eventually, program-level harmonization, where updates can be applied across documents in a more systematic and reliable way.
The value here compounds. Saving a few days on a single document may not sound substantial, but when that savings applies across dozens or even hundreds of documents, it adds up to months. Time saved on repetitive work can be reinvested in review, interpretation, and better decisions.
What medical writers should take away from this shift
If there is one message I would want another medical writer to take from my first year working with AI, it is this: AI is not going away, and it does not have to threaten your role. When it is used well, it elevates the work and the writer behind it.
AI removes some of the most repetitive and draining parts of the job. That creates space to focus on higher-value work: interpreting data, maintaining consistent messaging, and collaborating more closely with regulatory teams. Over time, the role shifts away from pure document production and toward strategic storytelling and scientific editing. That is not a loss of expertise. It is an elevation of it.
Through all of this, human oversight remains non-negotiable. AI can move the work faster, but it does not own the outcome. Medical writers do.
