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©2024 American Medical Writers Association. All rights reserved.  
ISSN 2163-5315

AMWAJournal.org     11

THEME ARTICLE

ABSTRACT 
The capability of artificial intelligence (AI) is rapidly increas-
ing and is now sitting on the threshold of the medical writ-
ing field. This article presents an AI integration framework 
that breaks down adoption of this new technology into 
manageable steps that ensure an informed and thorough 
approach. Using this framework, individuals and corpora-
tions can leverage the benefits of this evolving technology 
while minimizing risks.
 The first step, AI literacy, provides a foundation for 
informed decision making and appropriate expectations 
for AI capabilities. This knowledge inspires creative explo-
ration of which use cases would be a suitable application 
of AI tools. Once the scope of potential uses is defined, risks 
can be assessed, including incorrect content generation, 
data leakage, and bias. AI tools can then be evaluated to find 
tools that can both satisfy the use cases and mitigate critical 
threats. The final step is to integrate the tools transparently 
with appropriate guardrails. Then the cycle begins again as AI 
technology evolves and new applications become possible.
 As medical writers are ushered further into the AI era, 
clear and consistent advocacy for a synergy point between 
the efficiency of AI and the experience, ability, and human-
ity of a medical writer will maximize the impact of these 
innovative models.

The introduction of publicly available generative artificial 
intelligence (AI) tools has marked a significant turning point 
in integrating AI into medical communication. Mass-market 
releases of this new technology started with the launch of 
ChatGPT by OpenAI in November 2022,1 which was quickly 
followed by other significant large language model (LLM) 
chatbots like Claude by Anthropic and Bard by Google. 
Generative AI demonstrated remarkable reasoning capabili-
ties that previously required human medical writing expertise 
such as turning an unformatted data table into a summary 
paragraph that includes correct comparisons between 
groups. An upgrade to GPT-4 in late 20232 was the first of a 

wave of large multimodal models (LMMs) that could process 
and produce images and audio in addition to text.

 This period of technological novelty brings with it a wave 
of AI anxiety among professionals, stemming from fears of 
job displacement and the reluctance to adapt to changes AI 
could bring to our daily work. Our psychological response 
to dramatic change mimics the stages of grief; we start with 
shock, denial, and anger, then progress to depression due 
to feelings of overwhelm and inadequacy. Despite these 
concerns, it's crucial to recognize the potential of AI to opti-
mize drug development processes, reducing the time and 
expense of bringing new drugs to patients. Exponential 
growth in the number of AI-enabled drugs and devices may 
make it necessary for medical writers to adopt generative AI 
tools to keep up with the workload and do our part to bring 
treatments to patients faster. This hope for the future can 
bring us to the upside of the grief response curve, inspiring 
us to experiment with AI tools, increase our AI literacy, and 
eventually integrate AI into our work.
 In the “Take the Leap! Steps to Integrate AI Into Your 
Work” presentation at AMWA’s 2023 Medical Writing and 

Jenni Pickett, PhD,1 and Mandy Pennington, BS, MWC2  / 1Whitsell Innovations, Inc, Chapel Hill, NC;  
2Freelance Medical Editor, Downingtown, PA

Take the Leap! Steps to Integrate AI Into Your Work

Figure 1. AI integration framework. AI, artificial intelligence.

AI literacy

Consider AI 
use case(s)

AI risk 
assessment

AI tool 
evaluation

AI  
implementation 

and risk 
management

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AMWAJournal.org     12Take the Leap! Steps to Integrate AI Into Your Work

Communication Conference in Baltimore, Maryland, a 
comprehensive framework for integrating AI was intro-
duced that is relevant for leaders, employees, and freelanc-
ers (Figure 1). As medical communicators, it is up to us to 
set the foundation for a future in which the efficiency of 
generative AI is seamlessly blended with the expertise of 
medical communicators, harnessing the optimum capabili-
ties of both.

AI LITERACY
Successful application of LLM technology to medical writing 
hinges on our clear understanding of its benefits and risks. 
LLMs are a subset of AI, specifically within the field of gener-
ative AI. LLMs are created through machine learning, specif-
ically deep learning using neural network architecture.
 Machine learning is a way to build a computer program 
that is distinct from typical programming. It uses different 
hardware designed to perform many computational steps in 
parallel. Instead of having a programmer define each step 
for the computer to execute, a data scientist or machine 
learning engineer sets up a model for the computer to train 
itself based on provided data sets.3

 LLMs such as GPT-4 from OpenAI are trained on mas-
sive amounts of data, allowing them to comprehend inputs 
and generate human-like responses. GPT stands for gen-
erative pretrained transformer, meaning a machine learn-
ing model that generates unique outputs, accesses a broad 
subset of human knowledge from its pretraining, and 
understands complex user requests using transformer tech-
nology. LLMs can perform tasks like writing, translating lan-
guages, coding, creating images, data analysis, and more.
 An important step in AI literacy for medical communi-
cators is understanding the difference between LLMs and 
other AI technologies. Many medical writing and editing AI 
tools are largely expert systems. Expert systems resemble 
the structure and consistency you get with highly detailed 
templates; they are deterministic and built with extensive 
human knowledge, which leads to predictable outputs. 
However, they require structured inputs and are less adapt-
able to varying tasks.
 Conversely, LLMs resemble the flexibility and variabil-
ity you get with simpler, open-ended templates. They are 
probabilistic, generating outputs based on likelihoods and 
patterns learned from their vast data sets. This nature makes 
them more adaptable and flexible but also introduces 
inconsistency in output and the need for human oversight 
for accuracy and context.
 Writers may interact with LLMs in a standalone chatbot 
(like ChatGPT), as a functionality in software (like Copilot 
by Microsoft), or in an internet browser (like Chrome). LLM 
chatbots, whether standalone or within an application like 

Word, typically have an interface consisting of a blank box, 
which leaves the determination of what tasks are suitable 
and reliable entirely up to the user. AI literacy training to 
understand what tasks are appropriate for LLMs enables 
medical writers to leverage these tools effectively, allowing 
for productivity enhancements while maintaining the high 
standards of accuracy and context sensitivity crucial in  
the field.
 Understanding how LLMs work helps users predict 
appropriate tasks. There are fundamental differences in how 
humans and LLMs write content. Human writers research, 
understand context, and cite specific sources. They bring a 
unique perspective and a depth of understanding to their 
writing, albeit with the possibility of errors and gaps, espe-
cially when dealing with unfamiliar topics. LLMs, on the 
other hand, do not read in the traditional sense. Instead, 
they are trained on text data broken down into tokens (words 
or parts of words). LLMs generate text based on patterns 
learned from their training data, predicting the next token in 
a sequence. This process can lead to innovative content gen-
eration, but it lacks the depth of understanding and context 
that human writers and editors bring. Moreover, LLMs often 
cannot trace back to specific sources and might create fake 
citations or inaccurate content, particularly on topics not 
well-represented in their training data.
 Retrieval-augmented generation (RAG) technology, 
introduced to mass-market LLM tools in late 2023, assists 
LLMs by adding a retrieval step before generation. This 
retrieval step pulls out snippets relevant to the user request 
from writer-provided or Internet-based sources, which are 
then used and cited in the generation step.4

CONSIDERING AI USE CASES
Armed with a general sense of how LLMs work, users can 
think of routine challenges in their work that LLMs could 
help with. However, avoid the feeling that an LLM can solve 
everything (AI solutionism) when other tools, such as expert 
systems or regular software would do a better job. Ideal use 
cases are those that can benefit from an LLM's unique  
capabilities.
 Before evaluating use cases, users should understand 
that information shared with mass-market LLMs may be 
used to train a model owned by another entity. AI etiquette 
requires requesting permission before using someone’s 
nonpublic content in any AI system and compliance with 
AI use policies (employer, client, publisher, etc). Because 
LLMs can provide inaccurate information, use case outputs 
should be externally verifiable. 
 LLMs and LMMs can augment users by taking on 
simple tasks, assist users step-by-step, and amplify users by 
expanding their skill set (box on next page).

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AMWAJournal.org     13Take the Leap! Steps to Integrate AI Into Your Work

“Augment” Use Case Examples
• Formatting lists of abbreviations: fixing capitalization 

and spelling errors with awareness of proper nouns.
•  Formatting references: aligning to a provided  

example style.
•  Converting images to text: transform photos of  

handwriting, slides, or scanned documents to  
editable text.

“Assist” Use Case Examples
•  Preparing slide scripts: generating a draft of a presen-

tation script for a slide based on the slide title and  
key points.

•  Creating images: convert text prompts to pictures for 
use in presentations or social media.

•  Providing technical support: taking users step-by-step 
through common computer issues (if the LLM recom-
mends entering an admin password or editing registry 
files, wait for a human to help).

“Amplify” Use Case Examples
•  Creating a PubMed search string: converting a text 

request into Boolean operators.
•  Writing macros for Microsoft Office: translating user 

requests into Visual Basic for Applications and taking 
the user through the steps to run the program.

•  Performing basic data analysis: parsing large data 
sets and providing charts to visualize the data (users 
should be mindful that LLMs do not clean data  
automatically).

AI RISK ASSESSMENT
Before using LLM-based tools, understanding potential 
risks and planning how to mitigate them is paramount. 
Establishing a risk profile will help identify tools that fit that 
profile. Risks related to LLMs can be due to training data 
limitations, human factors, functional limitations, and/or 
implementation challenges (Figure 2).

 An LLM’s knowledge is rooted in its training data. Gaps 
or weaknesses in training data can result in hallucinations 
(false information invented by the LLM), outdated outputs, 
or biased responses.

Examples of Training Data Limitations
•  Data set does not include recent information (ie, after 

the training data cutoff), unless connected to internet 
browsing capability.

•  Data set includes outdated practices and language; 
LLM is unaware which practices are now preferred  
or required.

•  Data set is missing valuable context because it does 
not include nondigitized content (eg, conference  
presentations), content behind a paywall (eg, journal 
articles), or content in an inaccessible format  
(eg, regulatory guidances in PDF form).

 LLMs are very different from any technology previously 
available, which can introduce risks from human users.

Examples of Human Factor Limitations
•  Automation bias, the assumption that machine- 

generated content is accurate.
•  Distrust, leading to loss of interest from readers,  

attrition of employees, or client dissatisfaction.
•  Providing the model with an incorrect or outdated 

source.
•  Model damage from poisoned training data or  

manipulative prompts (prompt injection).

 The machine learning process is the root of some risks 
related to the way LLMs function. LLMs are probabilistic 
systems that cannot be predicted or entirely understood.

Examples of Functional Limitations
•  Opaque decision-making process.
•  Inconsistency of output, even with identical prompts.

LLM Use Case “A-List”
Augment: delegate specific tasks to the LLM and 
review the outcome.

Assist: collaborate with the LLM—the model helps 
write, edit, and create content.

Amplify: the LLM provides new capabilities, such 
as coding, creating a data visualization, or teaching 
a new concept.

Figure 2. Examples of risk categorization for LLM tools.

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AMWAJournal.org     14Take the Leap! Steps to Integrate AI Into Your Work

•  Not able to cite sources (unless equipped with RAG 
technology).

•  Can leak your inputs into other users’ outputs (eg, pro-
prietary data, protected health information).

 Implementing AI systems presents challenges that 
should be considered as part of a risk assessment.

Examples of Implementation Challengess
•  Cost (including employee time and opportunity cost).
•  Obsolescence as AI technology quickly advances.
•  Finding legal, nonproprietary training data.

 Risk evaluations should be captured together with 
planned risk mitigation steps in a risk management plan. 
The National Institute of Standards and Technology has cre-
ated an AI Risk Management Framework and Playbook as a 
resource to complete this process.5

AI TOOL EVALUATION
With a solid understanding of applicable use case(s) and a 
defined risk profile, it is time to select an appropriate tool to 
meet both criteria. When evaluating a tool, it is very import-
ant to understand which model the tool uses and what, if 
any, modifications have been made to the model – a tool 
based on an earlier LLM may be a lot less capable than the 
more recent models. For an expert system with LLM fea-
tures, it is important to understand what features of the tool 
are based on the LLM and what features are based on more 
deterministic programming so you can determine if the tool 
matches your risk profile.
 Because machine learning involves various degrees of 
learning, it can be helpful to think of LLM capabilities in 
layers (Figure 3):

•  The foundation model, like GPT-4 in ChatGPT, is  
similar to the college education of a medical writer, 
providing a broad base of knowledge.

•  Fine-tuning the model with additional specialty data 
sets like clinical study reports is similar to the special-
ized knowledge gained by a medical writer in a gradu-
ate or certificate program.

•  Providing the model with access to your data is like 
on-the-job training.

•  Finally, a collection of proven prompts in a prompt 
library mimics the efficiency gained with work  
experience.

 When delegating a task to a beginning medical writer, 
you would provide more detail, instructions, and follow up 
than you would with an experienced medical writer. The 

same logic applies to LLM tools. If the tool has limited layers 
of capability, your prompt needs a lot of context and spe-
cific instruction, and your output may require substantial 
revision. If your tool has multiple layers of capability, your 
prompt can be more straightforward and the quality of the 
LLM output will require fewer edits.

AI IMPLEMENTATION AND RISK MANAGEMENT
After choosing your use case(s), evaluating risks, and 
assessing and choosing an AI tool, the last step in the AI 
integration cycle is to integrate the tool into your work or 
organization. AI transparency is critical before, during, and 
after implementation. It is vital to address reservations that 
stakeholders may have and set guardrails for AI use.
 Clearly communicate to partners and users what the AI 
tool is capable of, who will be authorized to use it, when it is 
appropriate to use AI, why this change is being made, and 
how to use it appropriately. AI policies to record and share 
these principles are becoming a common business practice. 
An AI policy can provide rigid guardrails to protect against 
the risks identified in your risk management framework.
 Building a prompt library of successful, reliable use 
cases provides a set of flexible guardrails to further improve 
quality and reduce risk. The PLANTS acronym is a good 
starting point for building a prompt: persona, length, audi-
ence, nuance, type, and style guide (Figure 4).
 Improve the probability of successful implementation by 
starting with 1 or 2 use cases that an LLM can consistently 
make easier. This feeling of productivity and success can 
spark more interest in trying other use cases. As you explore 
new use cases, record not only what works, but what does 
not work so that those failed prompts can be potentially 
revised or revisited as LLM capability improves. Sometimes 
a failed prompt can become successful when adding one or 
two examples inside the prompt (also known as one-shot or 
two-shot prompting).

Figure 3. Potential layers of capability of an LLM tool. LLM, large 
language model.

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AMWAJournal.org     15Take the Leap! Steps to Integrate AI Into Your Work

A VISION FOR THE FUTURE
Medical writers must have involvement in defining the  
optimal balance between human effort and AI assistance. 
At one extreme, staying with the status quo of 100% human 
effort in drug development means continuing to struggle 
to accelerate time to market and rising development costs, 
and potentially falling behind competitors. On the other 
extreme, replacing entire medical writing functions with AI 
also presents risks. Health authorities would reject applica-
tions after finding missing submission elements, fake refer-
ences, and hallucinations. Teams would be left with a void 
of document leadership to break down tasks, set timelines, 
critically evaluate sources, and gain consensus.
 Now is the time for medical writers to define and advo-
cate for a synergy point that combines the expertise of 
medical writers with the efficiency of AI. The key is to be 
strategic, integrating AI where it adds value and ensur-
ing that the core responsibilities of medical writing remain 
grounded in human expertise.

Acknowledgments
The authors acknowledge Cathi Harmon for her 
contributions to the presentation that preceded this 
manuscript. We also thank Sean Whitsell, Mary Ellis 
Bogden, Pam Fioritto, and Ann Winter-Vann for their 
review of the manuscript.

Author declaration and disclosures: The authors note 
no commercial associations that may pose a conflict of 
interest in relation to this article. The authors discuss 
multiple AI tools but this article is not intended as an 
endorsement of any product.

Author contact: jenni.pickett@whitsellinnovations.com

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Figure 4. Using the PLANTS method to construct an LLM prompt. LLM, large 
language model.

http://www.amwajournal.org
https://openai.com/blog/chatgpt
https://openai.com/blog/chatgpt
https://openai.com/blog/chatgpt-can-now-see-hear-and-speak
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