





































       V38 N1 / 2023 

©2023 American Medical Writers Association. All rights reserved.  
ISSN 2163-5315

AMWAJournal.org     30

Speaker 
J. Kelly Byram, MS, MBA, ELS
Founder and CEO, Duke City Consulting, LLC,  

Albuquerque, NM

By Noelle Ochotny, PhD
Artificial intelligence (AI) projects are becoming more 

common assignments for medical communicators. In fact, 

a show of hands in this session revealed that approximately 

one-third of us have worked on a project involving AI and, 

among those who had not, many anticipated they would in 

the coming year.

 The presentation was organized into 3 sections. The first 

discusses the basics of AI and machine learning (ML) tech-

nology. The second describes how AI tools are developed 

and implemented. The third identifies and provides exam-

ples of the current and emerging applications of AI in clini-

cal research and health care.

 The session’s focus is on narrow AI, specifically, on a 

type of ML called supervised learning. It is important to 

note that not all the AI health applications discussed in this 

session are implemented in health care yet.

WHAT ARE SOME CHALLENGES ASSOCIATED  
WITH AI?
ML model creation requires data and computing resources. 

Creating the complexity required for a valid ML model can 

require extensive resources. There are also issues with trust 

in the ability of the model to make correct decisions that are 

for the benefit of the patient. Therefore, data and privacy 

security need to be robust. Having strong data and privacy 

security can help build trust in the ML model.

CATALYSTS FOR THE DEVELOPMENT OF ML
Recent developments have catalyzed the development of 

ML models and include

• Big data, which provides the necessary data and 

resources.

• Cloud storage, which enables organizations to store, 

access, and maintain large amounts of data required 

for ML model development. Also consider that the 

volume of medical data doubles every 8 to 12 months, 

which requires a lot of storage.

• Powerful computing is essential. ML can be a compu-

tationally intensive process, so a powerful computer is 

needed to handle the load.

• Parallel processing, which allows the ML model to be 

deployed across multiple processors. This is neces-

sary for the ML algorithm to perform large amounts of 

computation on large data sets, especially in the deep 

learning context.

• Maturation of statistics and mathematical methods, 

which underlie ML.

Session Report 
The Use of Artificial Intelligence and Machine Learning in  
Clinical Research and Health Care

CONFERENCE

ARTIFICIAL INTELLIGENCE AND MACHINE 
LEARNING TERMS
Artificial Intelligence (AI): Leverages computers and 
machines to simulate the problem-solving and deci-
sion-making capabilities of the human mind.

Weak AI, also called Narrow AI: This type of AI is  
limited to a specific task or narrow area.

Strong AI: An AI that has mental capacities and flexible 
intelligence that mimic the human brain. This type of AI 
is also sometimes referred to as artificial general intelli-
gence, artificial consciousness, or sentience.

Machine Learning (ML): An AI technique that teaches  
computers to learn from data.

Algorithm: A set of instructions. In ML, the algorithm 
learns and evolves without human intervention based on 
the data it processes. The algorithm builds on commonly 
used models such as linear regression, logistic regres-
sion, Bayesian algorithms, and decision trees. The terms 
algorithm, model, and tool are sometimes used inter-
changeably.

Deep Learning: A type of ML that uses an artificial neural 
network (ANN). ANNs are layers of connected nodes 
designed to emulate human processing of information.

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AMWAJournal.org     31The Use of AI and ML in Clinical Research and Health Care

TYPES OF ML
There are 3 common types of ML models, supervised, unsu-

pervised, and reinforcement learning.

 Supervised learning uses labeled training data that pairs 

inputs with outputs (input-output pairs are called exam-

ples; a collection of examples is a data set). An application 

of this type of learning might be an application that predicts 

if a specific type of tumor is likely to be malignant or benign 

based on its size, for example. Algorithm training, valida-

tion, and testing require a data set be subdivided into a 

training data set, a validation data set, and a testing data set. 

Supervised learning occurs via a training loop (Figure).

 In supervised learning, the percentage of the data set 

dedicated to each function depends on biostatistical calcu-

lations that will inform this decision. This can be similar to 

the sample size calculations used to determine the sample 

size in clinical trials. It is important that the data sets are 

representative of the population so that the resulting ML 

model is generalizable.

 Unsupervised learning uses unlabeled training data—

rather, the computer looks for patterns in the data. Some 

examples of this type of learning are images and pathology 

data. Used for clustering, segmentation is an example of 

how this type of learning can be applied.

 Reinforcement learning is a reward-and-penalty type of 

learning beyond the scope of this session.

 Deep learning has more complicated models composed 

of nodes organized in layers. Each layer transforms informa-

tion and passes it to another layer. The term “deep” refers to 

the number of layers through which the model operates. In 

this type of learning, the model can learn from itself to create 

new features. Ground truth information is fed back into the 

model to enable the deep learning model to learn from itself. 

(Ground truthing is a term used in ML that means checking 

the results of machine learning for accuracy against the real 

world.) An example of deep learning is facial recognition. 

Deep learning requires a vast number of resources.

WHAT ARE SOME ML MODELS BEING DEVELOPED 
IN HEALTH CARE AND CLINICAL RESEARCH?
1. Risk assessment and prevention. The patient completes 

a questionnaire that includes a personal and family history,  

and the algorithm can calculate the patient’s risk for 

cancer. To do this, the algorithm uses guidelines such as 

the National Comprehensive Cancer Network guidelines 

to determine a patient’s risk for certain cancers. The algo-

rithm then goes on to suggest risk reduction strategies and 

treatment plans for that patient. Is there a role for medical 

writers? Consider that there are patient-facing and clini-

cian-facing aspects related to the model. Medical commu-

nicators may develop information regarding risk reduction, 

testing, and treatment options that is provided to those 

audiences. The medical writer can communicate what is 

going into the model and what is coming out of the model 

using language specific to the 2 audiences: clinicians and 

patients. In addition, trust in the model is an ongoing  

issue. The medical writer plays an important role to 

develop trust.

 Attendees were interested in whether there are guide-

lines in place on how to communicate the risk assessment 

and prevention tools to patients in order to obtain informed 

consent. These tools collect sensitive data from patients and 

their families, so the patient and the family need to provide 

informed consent.

 The attendees were also interested in whether medical 

students and residents are receiving training on AI and ML. 

One attendee reported that their institution, the University 

of Florida, launched a curriculum on AI development for 

physicians and clinicians. AI and ML models may affect 

how medical students and residents receive training. For 

example, a radiologist has seen thousands of images, and 

current medical students may not get that experience.

2. Clinical decision support software. There is an ongoing 

debate about which software is regulated as a device. The 

United States Food and Drug Administration (FDA) issued 

a guidance1 regarding Clinical Decision Support Software to 

describe the FDA’s regulatory approach to Clinical Decision 

Figure. Supervised learning training loop. Copyright 2022 Duke City 
Consulting, LLC.

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AMWAJournal.org     32The Use of AI and ML in Clinical Research and Health Care

Support software functions. Consult the guidance for a 

complete discussion and examples.

 Keep in mind that if the software contains ML, then 

the FDA considers it to be a device. The FDA consistently 

updates guidelines for AI/ML applications.

3. Diagnosing retinal disease. This is a fast-growing market 

that includes diabetic retinopathy, a common complication 

of diabetes. To diagnose retinal disease, a camera takes an 

image of the patient’s retina that is then analyzed using ML. 

Several papers reported an AI detection rate of retinal disease 

that is better than the detection rate of clinicians. However, 

the AI performed worse at diagnosing negative cases.2,3

4. Reading and segmenting medical images. Several ML 

tools are being used in the radiology field. Radiologists who 

used ML to read medical images worked 65% faster.4 It is 

important to note that the use of ML tools improves work-

flow but does not replace the radiologist.4 Many of the ML 

tools listed on the FDA website as being approved are devel-

oped for radiology.

5. Predictive modeling. These models help predict out-

comes like who will require readmission to the hospital 

within 30 days of discharge, among others. Predictive mod-

eling can be added to a hospital’s electronic health record 

package. The process to add predictive modeling to an elec-

tronic health record package is straightforward because there 

are vendors who can add the models. For example, the track-

ing of fall risk, heart failure, and early detection of sepsis can 

be electronic health record add-ons. However, it is vital that 

these add-ons meet guidelines requiring model reporting, be 

useful, fair, and reliable, and are generalizable and transpar-

ent. A lack of transparency in an AI model can pose a signifi-

cant barrier to gaining the trust of patients and clinicians.

6. Drug discovery and development. DeepMind’s 

AlphaFold 2 can predict how a protein folds with an accu-

racy rate similar to crystallography, but in hours rather than 

months.5 AlphaFold 2 radically shortens the identification 

and development cycles for new drugs, a great boon to  

biomedical researchers.

7. Nanotechnology. There is hope that this emerging tech-

nology field can be applied to cancer diagnostics and 

cancer therapeutics; however, intratumor and interpatient 

heterogeneity have posed significant barriers in this area. 

Application of AI methods to the design and analysis of out-

comes have met with some success.

WHAT IS GENERATIVE AI?
Generative AI is a type of ML algorithm that is designed to 

generate new data based on what it has learned from the 

data that it has been trained on. This can be used to create 

new images, text, or other forms of data that mimic the 

characteristics of the training data. For example, AI can 

create images of people that look real but who do  

not exist.6

WHAT ARE FUNDERS AND THE FDA  
LOOKING FOR?
The main concerns of funders and the FDA are found in 

the Good Machine Learning Practice for Medical Device 

Development: Guiding Principles document.7 The concerns 

that some application developers tend to neglect, in Byram’s 

experience, are included in the following list. Of particular 

focus is improving the performance of the human-AI team. 

Another concern is that models degrade over time and need 

to be retrained.

Key takeaways from this guideline include

1. The multidisciplinary team should work together 

throughout the total product life cycle to ensure that 

the AI remains relevant.

2. Make sure that clinical study participants and data 

sets are representative of the population, so the 

models developed on them are generalizable.

3. Be sure to emphasize the performance of the  

human‐AI team.

4. Ensure that deployed models have the capacity to  

be monitored with a focus on improved safety and 

performance, and appropriate controls are in place  

to manage retraining risks.

 Attendees were curious about the intersection of pri-

vacy laws and training data sets. Attendees indicated that, 

in their experience at their institutions, the patient pro-

vides consent for their data to be used in data training sets. 

To build and maintain trust with the patient, the consent 

form should include a statement that all patient data will be 

kept secure, and the data used for ML is deidentified using 

Protected Health Information guidelines.

CLOSING
Byram closed the presentation by emphasizing the impor-

tance of being aware of the FDA and funder guidelines and 

to consult the references provided in the presentation for 

further guidance.

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AMWAJournal.org     33The Use of AI and ML in Clinical Research and Health Care

Noelle Ochotny is a medical writer at Foremost Medical 
Communications in Mississauga, Ontario, Canada.

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

Author contact: nochotny@fmc.cc

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