The rapid expansion of artificial intelligence has prompted the evolution of skills needed to manage medical writing workflows. AI tools do not replace the expertise, judgment, or regulatory awareness required in clinical and scientific communication. Instead, the use of AI tools introduces new ways of working in which writers combine human insight with technological support.

Shanrael Murphy at Trilogy Writing & Consulting, an Indegene Company

The medical writer’s toolkit

Artificial intelligence (AI) tools are a part of the document development process, assisting with a wide variety of tasks including drafting text, formatting and managing inconsistencies. As of the beginning of 2026, the rapid rise in the use of AI tools and the experience gained has highlighted the need for medical writers to use these tools effectively. Important skills will be needed to effectively navigate the world of AI-assisted writing including:

• Expectation management
• Critical thinking
• Prompt engineering
• Patience
• Ensuring accuracy in AI-supported literature reviews
• Quality control (QC)

Development in these areas ensures that medical writers maintain control of the writing process while developing accurate, reliable, ethically sound and, in the right circumstances, documents that are drafted faster.

AI tools cannot replace medical writers. While AI tools can streamline drafting and support efficiency, these tools are only useful when writers apply strong scientific judgment, precise communication skills and a deep understanding of regulatory expectations. That said, AI tools are here to stay because, when used properly, they create efficiencies. Writers who are willing to develop their professional skillset and adapt to the changes in the field are the ones who are more likely to thrive.

Establishing realistic expectations for AI tools

One essential way that AI tools are changing medical writing is in the way that time and resources are allocated. AI tools are lauded for making the writing process faster and shifting the energy spent away from repetitive tasks. This can be true, with the proper skills. Arguably one of the most important skills writers can develop is the ability to set clear expectations about what AI tools can and cannot do. Without this skill, the AI tools may produce useless outputs or outputs that require a lot of revision. This wastes time, causes frustration and can lead writers to avoid using AI tools.

In general, AI tools are strong at identifying patterns, producing structurally coherent drafts, imitating tone, summarising long documents and reorganising complex sections. These abilities make them useful for early drafting, rephrasing and template-based structuring. However, AI tools cannot provide scientific reasoning, contextual understanding or regulatory judgment. They cannot interpret results or weigh ethical considerations. Developing the ability to distinguish these limits allows writers to assign tasks appropriately and avoid wasted time refining unusable AI tool output.

Critical thinking

Another key skill is learning to think critically when reviewing AI-generated content. When assessing AI-generated content, two things should be kept in mind: the process used when AI tools generate content and acknowledgement that AI tools do not understand what they have written.

When an AI tool writes a sentence, it works by estimating which words are most likely to come next in the sentence based on patterns it learned from large amounts of data (ie, large language models). It does not rely on an understanding of grammar or context. Instead, it calculates the probability for many possible next words and selects the one with the highest likelihood. This process continues word by word, creating sentences that reflect the patterns the model has seen, rather than any true comprehension of the topic.1,2 This means that no two responses are the same, even from the same prompt. Some outputs may be detailed and accurate, while others may be vague or inconsistent. This variability is a direct result of how the model functions and it is why human review is essential to ensure that the final text is appropriate for its intended use.

Additionally, AI tools can produce text that sounds polished but lacks a reliable scientific or regulatory foundation. Writers must practice evaluating claims carefully. Skill development in this area includes learning to ask targeted questions, such as:

• Where did this claim originate?
• Is it supported by the data?
• Is the language appropriate for the document type?

Without careful review, a document may contain inaccurate assumptions, unsupported statements or tone issues. Skills in this area allow writers to catch these problems early. Without this vigilance, the time saved during early drafting can quickly be lost during later review cycles when errors can be more challenging to identify and more costly to correct.

Prompt engineering: The impact of prompt quality on AI output

Strong prompting skills enable writers to guide AI tools effectively and manage the variability of the outputs. The key to guiding AI tools is providing clarity in the specifics of the output. This helps writers to focus the AI tool rather than leaving the tool to make assumptions. If an AI tool is forced to guess, it may fabricate content, leading to rework and wasted time. If the output does not match the writer’s definition of a successful output (ie, data is correct, template adherence, tone is appropriate) then the prompt may need to be adjusted.

A weak prompt is vague, offering little more than a topic. For example, asking an AI tool to ‘write the efficacy results for a Phase 3 study of Drug X for Condition Y’ omits critical elements – endpoints, statistical values, populations and regulatory expectations. Without this information, the AI tool fills gaps with generic or fabricated content, producing text that may sound polished but is scientifically unreliable.

A strong prompt parses the requirements for the output into clearly defined steps. Developing this skill begins with learning to specify:

• Who is audience (eg, regulators, patients, healthcare providers, subject-matter experts)
• The exact deliverable (eg, clinical study report (CSR) efficacy results section, journal abstract, data-driven lay summary)
• Why it is needed (eg, regulatory submission, peer-reviewed publication, internal decision-making)
• The sources to use (eg,
• peer-reviewed journal articles or only the sources uploaded)
• The format of the output (eg, paragraph, bullet or table),
• The length of output
• The type of text/data to pull from huge source files
• The level of detail (eg, high-level overview versus line-by-line data interpretation).

Learning to supply this level of clarity is an essential skill, enabling AI tools to generate structured, accurate drafts grounded in real data.

Prompt complexity should match the assignment. For highly targeted tasks, such as adjusting tone or rephrasing a sentence for clarity, short, well-defined prompts are often sufficient. In contrast, complex drafting tasks like preparing an ICH E3–aligned CSR section require a more structured approach. Writers must learn to balance between detail and conciseness. The detail is important for providing a blueprint for the AI tool, but overly long prompts can confuse the model. Prompt length should be assessed for each task and can vary between tools.

By building strong prompting skills, medical writers can guide AI tools effectively, reduce rework, and maintain control over scientific accuracy and regulatory quality. Clear expectations, task appropriate prompt complexity and well-structured data inputs enable an AI tool to function as a reliable drafting partner while improving both efficiency and document quality.

Patience is a core skill in AI-supported writing

Iterative refinement is at the heart of prompt engineering. Outputs will rarely be correct the first time. It can take several rounds of revision to improve clarity, tone and accuracy of the numbers. If a writer has not developed patience for this process, there may be frustration or steps may be skipped, putting the quality of the document at risk. Patience also helps manage variations in AI tool responses. During the drafting process, the outputs from an AI tool will vary. A calm and systematic method (ie, tightening instructions, choosing more precise terms and reinforcing numerical requirements) can help turn this variability into a controlled and predictable drafting process.

Ensuring accuracy in AI-supported literature reviews

When used for a scientific literature review, AI tools can be helpful in highlighting key findings, identifying themes and condensing long articles. However, it may not always capture every nuance or reflect the full complexity of the research. Medical writers will need to hone their skills in reviewing AI-generated content alongside the original publications. This involves confirming information and noticing elements that AI tools may have overlooked. Developing this skill will require special attention to:

• Statistics
• Direct quotations
• Study conclusions
• Safety findings or outcomes that were under-emphasised.

During the review process, medical writers will need to apply their judgement in evaluating evidence quality, potential biases and relevance to the intended population. By developing these skills, medical writers can combine the efficiency of AI tools with careful scientific assessment. This balanced approach supports accurate interpretation, reduces the risk of miscommunication and enhances the overall reliability of AI-assisted literature reviews.

Quality control: where human expertise remains essential

The need for strong QC skills is not new to medical writing. However, AI tools do challenge the mindset that a medical writer may use to approach the QC process. AI tools can be variable and it can fabricate information. Writers will need to ensure consistency in terminology, tense and voice, and verify that study identifiers, treatment arms and populations are described accurately. Numerical precision remains essential, so every value in the text will need to be checked against the corresponding tables and figures. AI tools may generate language that is promotional or vague, so writers may need to revise it to maintain neutrality, accuracy and alignment with evidence. This includes replacing generic statements with clear, databased descriptions and acknowledging uncertainty when appropriate. AI tools may not follow ICH E3, CONSORT, AMA style or journal specific requirements, so writers must ensure that sections follow the correct hierarchy, organisation and formatting. An essential part of the QC process is the writer’s ability to ensure that the facts build a story and give the data meaning rather than turning a document into a place for dumping data. By strengthening these skills medical writers ensure AI tools support high quality, scientifically sound deliverables.

Why is AI worth using?

It cannot be overstated that every AI-generated output should be reviewed by a human. AI tools can fabricate information (ie, hallucinate) and can make mistakes when it is overloaded. A lot of these challenges can be overcome by ensuring that the AI tool is doing appropriate tasks, with appropriately built prompts. Writers will need to experiment with the limitations of their tools. There will be a learning curve to decide what tasks are appropriate for AI tools and when a task needs to be split into smaller, more manageable parts. If writers are willing to be patient with the learning process, AI tools can be very useful in the following cases:

• Early drafting (eg, creating structure, synthesising information, drafting boilerplate language and organising complex results)
• Repetitive tasks (eg, convert tables into narrative, harmonise tone across sections, rephrase text for clarity and generate alternative wording quickly)
• Highlight inconsistencies or unusual patterns that merit closer review (eg, finding inconsistencies that may appear as a result of the writer’s fatigue after multiple iterations).

Developing the skills to use AI tools effectively allows writers to focus their expertise on messaging, refinement, accuracy and regulatory alignment rather than initial construction. The key is knowing where to apply the AI tools to get the most benefit from them. Strengthening the ability to use AI tools in these targeted ways expands a writer’s capacity and supports faster, more iterative drafting.

A new era of medical writing

Using AI tools creates unique opportunities and challenges as it redefines the document development process. New skills in understanding AI tools, appropriately delegating tasks to AI tools and assessing AI-generated outputs will need to be developed. With these skills, medical writers can leverage the strengths of AI tools and shift their focus from repetitive work to cognitive work. Mastery of prompt design is also a core competency, enabling writers to direct AI tools effectively and produce outputs that are accurate, structured and suitable for refinement. To get the most benefit from AI tools, existing skills in critical thinking, scientific judgement, awareness of the document requirements and awareness of what a well-written document looks like will need to be strengthened. The most successful medical writers will be the ones who adapt by learning new skills and strengthening the ones that AI tools cannot replace. In this new era, AI supports what is possible, but it is the writer’s expertise that ensures the work remains accurate, meaningful and impactful.

Shanrael (Shan) Murphy PharmD, MPH, is a medical writer with a background in pharmacy, public health and health outcomes research. Shan is passionate about connecting people with science through compassionate communication. This passion is most often expressed in their work on plain language translations of regulatory documents and empowering people to leverage technology to streamline workflows. At Trilogy Writing & Consulting, an Indegene Company, Shan contributes by serving clients and supporting development of AI solutions for medical writing.