









































       V39 N2 / 2024 

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

AMWAJournal.org     e1

THEME ARTICLE

This session provided attendees with an overview of the ele-
ments of artificial intelligence (AI) that medical communi-
cation professionals can use in their decision-making when 
communicating about and with AI applications. Knowing 
how to determine if an AI application is reliable and secure 
guides the professional in their assessment of applications 
they write about and their choice of applications they use 
in their practice. In turn, this ability to assess AI applica-
tions underpins professionals’ abilities to identify and apply 
best practices for developing and writing about AI. Taken 
together, this understanding of how AI works, how applica-
tions are developed, and how to identify and ethically apply 
best practices will guide professionals in their communica-
tion about and with AI applications.

AI FUNDAMENTALS
The field of AI emerged in the early 1940s, and one touch-
stone of the field, the Turing test of machine intelli-
gence (designed to determine if a machine can think 
like a human)1 dates to 1950. Although initially consid-
ered a single field of research, in the intervening decades, 
researchers have split the study of AI across multiple 

domains involving myriad disciplines. As a result, AI is 
defined many ways, but 2 particularly salient definitions 
are (1) a machine performing a task requiring human intel-
ligence and (2) a machine replicating human intelligence. 
Both definitions, like the Turing test, evaluate AI in the con-
text of human intelligence (including behavior); however, 
the AI applications that have been developed thus far lack 
human traits, such as empathy and creativity, and human 
reasoning in the frameworks of ethics and complex strategy.

Machine Learning
Machine learning (ML) underpins many of the AI appli-
cations that medical communicators will communicate 
about and with. Patient triage, hospital management, and 
imaging applications approved by the US Food and Drug 
Administration use ML. What makes ML applications differ-
ent from traditional software applications is that most ML 
does not use explicit or rule-based programming. In explicit 
programming, a program gives the computer specific com-
mands or lines of code, that is, the function. ML uses statis-
tical and mathematical modeling and incredible volumes 
of data to learn relationships between variables, that is, it 
determines the function. The model learns and refines itself 
as it ingests and processes data. Some common models 
used in ML include linear regression, logistic regression, 
Bayesian algorithms, and decision trees.2

Deep Learning and Generative Pretrained Transformer 
Applications
Deep learning (DL), a type of ML that uses an artificial 
neural network modeled on the human brain and designed 
to emulate human processing, uses layers of connected 
nodes that process information and pass the transformed 
data up to the next layer. It learns from itself and can create 
new features on its own. It can learn nonlinear, high-dimen-
sional relationships from data that are not just unstructured 
but multimodal. Throw it all in the mix—imaging, biomet-
rics, audio, visual, and time series data. DL applications 
include target validation, identification of prognostic bio-
markers, analysis of digital pathology CT data, and genera-
tive pretrained transformer applications (GPTs).

J. Kelly Byram, MS, MBA, ELS / Founder and CEO, Duke City Consulting, LLC, Albuquerque, NM

Communicating About and With Artificial Intelligence Applications

* This article is based on the presentation Communicating About and 
With Artificial Intelligence Applications by J. Kelly Byram, MS, MBA, 
ELS, at AMWA’s 2023 Medical Writing & Communication Conference.

Editor’s Note
Developments in artificial intelligence (AI) will continue to 
be of critical importance to medical communicators for the 
foreseeable future. Accordingly, AMWA Journal expects to 
continue to feature AI-related articles in upcoming issues. 
 Given how rapidly advancements are occurring in AI 
as they relate to medical communication, we are striving 
to be as timely as possible in bringing relevant articles to 
you. In this spirit, we are supplementing the Summer 2024 
Digital Revolution theme issue with a timely article titled 
‘Communicating About and With Artificial Intelligence 
Applications’ by J. Kelly Byram, based on a presentation 
made by the author at the most recent AMWA Medical 
Writing & Communication Conference.

http://www.amwajournal.org


AMWAJournal.org     e2Communicating About and With Artificial Intelligence Applications

 Because DL creates its own algorithms, the models and 
applications created by DL can lack transparency. This black 
box effect enhances a distrust of AI in many population seg-
ments, and 60% of Americans overall indicated discomfort 
with the use of AI in their care.3 It can help to keep this dis-
comfort front of mind when writing for lay audiences.
State of the Science
 We typically divide AI into 2 categories: weak and strong 
(Figure 1). Weak AI is what we have today—systems or 
machines that have learned how to perform specific tasks 
in a way similar to how a human would perform the task. 
Some systems display human intelligence; that is, they have 
the ability to learn and solve some types of problems, but 
not all.4 Although today the mention of AI elicits discussion 
of GPT applications such as ChatGPT, AI has been a part of 
the knowledge professional’s workflow for so long that it has 
been taken for granted as the power behind search engines, 
spam filters, and smart assistants such as Siri and Alexa.
Strong AI, or sentience, is the flexible intelligence that can 
flit from one type of task to another and has advanced rea-
soning capabilities. HAL from 2001: A Space Odyssey and 
Skynet from Terminator usually come to mind when dis-
cussing this type of AI. Unlike Siri and Alexa, references to 
HAL and Skynet usually evoke fear and dystopian angst. 
Depending on one’s point of view, strong AI is either an 
aspirational goal or an existential threat.

AI in Health Care
Three subfields of AI more commonly leveraged in health 
care research and practice include ML, DL, and large lan-
guage models (LLMs) as GPT applications. These appli-
cations segment images to assist in the identification and 

segmentation of lesions, identify promising molecules and 
guide drug development, determine dosage, and assist in 
genomics and precision medicine, public epidemiology, 
emergency department triage, and hospital management.5 
Some of these models are standalone software packages, 
others are slick software-as-a-service applications inte-
grated with electronic health records.

COMMUNICATING ABOUT AND WITH AI 
APPLICATIONS
Medical writers and editors who work with AI development 
teams have been writing proposals for AI projects for years. 
As these projects come to fruition, more communicators will 
join the effort and find that the complicated and sometimes 
obscure methods used to develop AI applications  
can pose a challenge to effective communication about  
AI. Although many standard research design concerns  
(eg, hypothesis, sample size, data quality and representative-
ness, design rigor, multidisciplinary representativeness of the 
team, generalizability) also apply to AI model development, 
communicators must also interrogate designs for AI-specific 
matters (eg, portability of the model, validation and testing 
plan, human–AI team required for implementation, main-
tenance plan to address drift). For AI applications being 
developed for clinical use, the FDA’s Good Machine Learning 
Practice for Medical Device Development: Guiding Principles 
document6 provides excellent, clear guidance. Many of the 
points on their list of guiding principles should be considered 
in the research design development and proposal writing 
stages, in addition to the funder’s explicit requirements.
 When writing about AI-based health care apps, the 
importance of understanding how researchers develop 
these applications quickly becomes apparent, but, when 
writing with AI apps, one may ask why any of the technical 
aspects of AI matter to the end user. Generative AI appli-
cations are, after all, a tool—but every good craftsperson 
knows their tools. Communicators using generative AI in 
their practice likewise should understand the tools. In the 
medical writing and editing practices, this largely means 
understanding GPTs.
 GPTs are a type of LLM. LLMs sit at the intersection of 
DL and natural language processing, an AI domain specific 
to teaching machines to understand and generate human 
language. LLMs use DL techniques applied to enormous 
data sets. Their objective is to understand and generate text 
based on what they have learned from the data they have 
ingested. Although their output sounds human, it is the 
output of a statistical model, like any other GPT. The text is 
the algorithm’s best statistical prediction of what the next 
word (and then the next and the next) should be. Unlike 
earlier AI, like predictive text that suggests a word or brief 

Figure 1. AI is typically divided into 2 categories: weak AI and strong 
AI. Although strong AI is the goal of the AI field, weak AI represents 
the current state of the science. AI, artificial intelligence.

http://www.amwajournal.org


AMWAJournal.org     e3Communicating About and With Artificial Intelligence Applications

phrase, generative AI takes a holistic approach, creating a 
more complex model that understands the larger context of 
sentences and paragraphs and can generate paragraphs of 
cohesive and coherent human-sounding text. Some medical 
communication products especially suitable for generative 
AI production include patient education materials, medi-
cal guidelines, package inserts, patient-facing chatbots to 
answer medical questions, patient discharge instructions, 
and letters (to insurers, employers, etc),7 and other plain 
language materials.

Using Consumer GPTs to Generate Technical Content
Consumer versions of GPTs hit the market big with Dall-E 
and then ChatGPT in 2022. In the intervening time, the 
GPT offerings have only multiplied and expanded across 
tasks, including writing, image generation, programming, 
and data analysis. Developers train consumer GPTs on the 
Internet, meaning the GPTs ingest online content, good 
and bad. Although that is a large amount of data by any-
one’s standards, a consumer GPT’s training is limited to the 
data to which it had access, including copyrighted material 
(in violation of copyright laws),8 but without access to pay-
walled peer-reviewed content and with training cutoffs that 
may mean the most recent content is months or years old.†  
However, high-quality medical communication requires 
accurate, detailed, and current sources, so the quality of 
most content generated by consumer GPT applications may 
not meet those standards, especially for more complicated 
or technical topics.
 The human quality of GPTs’ language can cause users to 
trust the applications’ content more than they should. GPTs 
provide inaccurate and biased information and plagiarize 
their sources—all issues ethical medical communicators 
cannot ignore. And, although the human-sounding quality 
of the content increases the value of GPT-generated content 
in many contexts, consumer GPTs have a limited ability to 
generate meaningful technical language.

Using Proprietary or Enterprise Models to Generate 
Technical Content
To protect intellectual property, including research data and 
information about a novel technology or design, some com-
panies have implemented proprietary or enterprise pur-
pose-built models, trained on the research corpus specific 

to their industry and organization. Often these are sparse 
expert models (<100 billion parameters vs ChatGPT version 
3.5’s 175 billion parameters), which can be more accurate 
than larger general models because the data ingested are 
more specific to the users’ needs.
 Unlike consumer GPT applications, the models are 
trained with industry-appropriate information, including 
paywalled articles, then further trained on the organization’s 
data. Data ingested by private generative models are only 
available to members of the organization. But, like consumer 
GPT applications, the tendency of the technology to prevari-
cate, hallucinate, and plagiarize persists in these models, too.

CONCLUSION
Regardless of the type of AI application being used, whether 
it is an application for analyzing imaging or one for gener-
ating content for a patient education website, current AI 
applications are imperfect tools for our use. These tools 
augment human productivity, intelligence, and creativity 
if used strategically and well, which will result in a shrink-
ing of the workforce.9 As Erik Brynjolfsson, director of the 
Stanford Digital Economy Lab, summarized the situation for 
knowledge workers, “I think if done right, it's not going to be 
AI replacing lawyers. It's going to be lawyers working with AI 
replacing lawyers who don't work with AI.”10 Similar to ear-
lier industrial revolutions, the Fourth Industrial Revolution 
brings technologies that will displace workers who per-
form work that new technologies can do faster and cheaper. 
But teams will always have a need for communicators with 
domain expertise or other exceptional skills who ethically 
and effectively use these tools in their practice.

Acknowledgment
I thank John W. Byram for his review of the manuscript.

Author declaration and disclosures: The author notes no  
commercial associations that may pose a conflict of interest in 
relation to this article.

Author contact: kellybyram@dukecityconsulting.com

References
1.  Turing AM. I.—Computing machinery and intelligence. Mind. 

1950;LIX(236):433-460. doi:10.1093/mind/LIX.236.433
2.   Ochotny N. The use of artificial intelligence and machine learning 

in clinical research and health care. Am Med Writ Assoc J. 
2023;38(1). doi:10.55752/amwa.2023.241

3.   Faverio M, Tyson A. What the data says about Americans' views 
of artificial intelligence. Pew Research Center website. Updated 
November 21, 2023. Accessed January 8, 2023. https://www.
pewresearch.org/short-reads/2023/11/21/what-the-data-says-
about-americans-views-of-artificial-intelligence/

4.   Biever C. ChatGPT broke the Turing test - the race is on for new 
ways to assess AI. Nature. 2023;619(7971):686-689. doi:10.1038/
d41586-023-02361-7

†After this presentation last year, more consumer GPTs have introduced 
real-time web search functionality. In practical terms, a GPT with real-
time search capabilities may have been trained through September 
2021, for example, but it can search the internet for information in real 
time. For many casual users employing a consumer GPT with real-
time search functionality, the training date of the GPT has become a 
distinction without a difference.

http://www.amwajournal.org
https://www.pewresearch.org/short-reads/2023/11/21/what-the-data-says-about-americans-views-of-artificial-intelligence/
https://www.pewresearch.org/short-reads/2023/11/21/what-the-data-says-about-americans-views-of-artificial-intelligence/
https://www.pewresearch.org/short-reads/2023/11/21/what-the-data-says-about-americans-views-of-artificial-intelligence/


AMWAJournal.org     e4Communicating About and With Artificial Intelligence Applications

5.   Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing 
healthcare: the role of artificial intelligence in clinical practice. 
BMC Med Educ. 2023;23(1):689. doi:10.1186/s12909-023-04698-z

6.   Good machine learning practice for medical device development: 
guiding principles. US Food and Drug Administration. October 
27, 2021. Accessed May 14, 2024. https://www.fda.gov/medical-
devices/software-medical-device-samd/good-machine-learning-
practice-medical-device-development-guiding-principles

7.   Doyal AS, Sender D, Nanda M, Serrano RA. ChatGPT and 
artificial intelligence in medical writing: concerns and ethical 
considerations. Cureus. 2023;15(8):e43292. doi:10.7759/
cureus.43292

8.   Grynbaum M, Mac R. The Times sues OpenAI and Microsoft 
over A.I. use of copyrighted work. New York Times. December 
27, 2023. Accessed December 27, 2023. https://www.nytimes.
com/2023/12/27/business/media/new-york-times-open-ai-
microsoft-lawsuit.html

9.   Doumi L, Goel S, Kovács-Ondrejkovic O, Sadun R. Reskilling in 
the Age of AI. Harvard Business Review. September-October 2023. 
Accessed May 14, 2024. https://hbr.org/2023/09/reskilling-in-the-
age-of-ai

10.  Oliver, J. Artificial Intelligence: Last Week Tonight with John Oliver 
[Video]. https://youtu.be/Sqa8Zo2XWc4. Published February 27, 
2023. Accessed June 24, 2024.

http://www.amwajournal.org
https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles
https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles
https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles
https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html
https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html
https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html
https://hbr.org/2023/09/reskilling-in-the-age-of-ai
https://hbr.org/2023/09/reskilling-in-the-age-of-ai
https://youtu.be/Sqa8Zo2XWc4

