# Health Disparities through Generative AI Models: A Comparison Study Using A
Domain Specific large language model

Yohn Jairo Parra Bautista, Ph.D.
Computing Information Science Department
Florida A&M University
Tallahassee, FL 32317
<EMAIL_ADDRESS>
&Vinicious Lima
Computer Graphics Department
Purdue University
West Lafayette, IN 47906
<EMAIL_ADDRESS>
&Carlos Theran, PhD
Computing Information Science Department
Florida A&M University
Tallahassee, FL 32317
<EMAIL_ADDRESS>
&Richard Aló, PhD
College of Science and Technology
Florida A&M University
Tallahassee, FL 32317
<EMAIL_ADDRESS>

###### Abstract

Health disparities are differences in health outcomes and access to healthcare
between different groups, including racial and ethnic minorities, low-income
people, and rural residents. An artificial intelligence (AI) program called
large language models (LLMs) can understand and generate human language,
improving health communication and reducing health disparities. There are many
challenges in using LLMs in human-doctor interaction, including the need for
diverse and representative data, privacy concerns, and collaboration between
healthcare providers and technology experts. We introduce the comparative
investigation of domain-specific large language models such as SciBERT with a
multi-purpose LLMs BERT. We used cosine similarity to analyze text queries
about health disparities in exam rooms when factors such as race are used
alone. Using text queries, SciBERT fails when it doesn’t differentiate between
queries text: "race" alone and "perpetuates health disparities." We believe
clinicians can use generative AI to create a draft response when communicating
asynchronously with patients. However, careful attention must be paid to
ensure they are developed and implemented ethically and equitably.

##  1 Introduction

###  1.1 Addressing Health Disparities Through AI Models: An Important Issue

Healthcare research and practice have long been marked by inequalities in
health outcomes and difficulty accessing healthcare services. These
discrepancies are called health disparities and bring great concern with using
artificial intelligence (AI)[1]. Some efforts to reduce these disparities have
become a vital public health priority, and healthcare professionals are
increasingly exploring using artificial intelligence models as a potential
solution to improve healthcare outcomes.

Healthcare professionals are using artificial intelligence models more
frequently to combat various health disparities, such as those resulting from
differences in race, ethnicity, socioeconomic status, and geographical
location [2]. These models can potentially analyze vast amounts of data,
recognize patterns and trends, and offer individualized and specific
interventions to improve health outcomes for all groups [3].

Health disparities, which refer to differences in health outcomes among
different population groups, remain a significant challenge in healthcare.
Generative artificial intelligence (AI) models are emerging as promising
approaches to address these disparities [4]. Generative AI models can analyze
vast amounts of data, identify patterns and trends, and generate new data,
such as text, images, and audio. The potential of these models to provide
personalized and targeted interventions to improve health outcomes for all
populations has captured the interest of healthcare researchers and
practitioners [5].

A generative adversarial network (GAN) is an AI model that generates synthetic
medical images to tackle racial disparities in dermatology. A particular study
revealed that GAN effectively produced synthetic images that were clinically
relevant and accurately represented the underlying pathology [6]. This
approach can potentially improve access to dermatological care for underserved
populations.

Another example is a deep learning model that identifies and mitigates
disparities in cervical cancer screening rates [7]. The study demonstrated
that the model could predict which patients were at increased risk of missing
screening appointments and provided targeted interventions to increase
screening rates.

In terms of the potential of natural language processing (NLP) models in
enhancing health equity through the analysis of electronic health records
(EHRs) [8], research has shown promising results. By analyzing EHRs, NLP
models can identify disparities in health outcomes and assist in developing
targeted interventions for underserved populations. However, it is crucial to
consider social determinants of health, such as race, ethnicity, and
socioeconomic status, when designing and implementing NLP models to address
health disparities[9]. Using domain-specific language models tailored to
specific healthcare domains can also improve the accuracy and effectiveness of
NLP models in addressing health disparities. In general, NLP models have the
potential to have a significant impact in promoting health equity and reducing
health disparities in healthcare.

The primary objective of this comparative study is to investigate the
application of domain-specific large language models such as SciBERT in
healthcare research to address health disparities through generative AI models
[10]. The study aims to offer an assessment of two models to provide valuable
insights into the potential of AI models in resolving health disparities.
SciBERT has about 3x as many tokens with digits. SciBERT’s number of tokens is
much more diverse. They are often subwords, and many include decimal places or
other symbols like "%" or "(.".

In this study, we analyze text queries that involve health disparities. We use
cosine similarity to test two LLMs: SciBERT and BERT, on those queries. We
further test the cosine similarity in three scenarios where domain-specific
LLMs like SciBERT do not make a distinction on text query that involved health
disparities.

Cosine similarity is a widely used metric to measure the similarity between
two vectors in a high-dimensional space[11, 12]. In language models, we often
represent words or documents as vectors in a vector space, where each
dimension corresponds to a feature or a characteristic of the text.

The formula for cosine similarity:

$\text{similarity}(u,v)=\frac{u\cdot v}{\lVert u\rVert\lVert v\rVert}$

\- $u\cdot v$ is the dot product between the two vectors, which is the sum of
the element-wise products of the vectors. \- $\lVert u\rVert$ and $\lVert
v\rVert$ are the Euclidean norms of the vectors, which are the square roots of
the sum of the squares of the vector elements.

Cosine similarity is commonly employed in language models to assess the
similarity between two documents, frequently represented as vectors of word
frequencies or embeddings. For example, suppose we have two documents $d_{1}$
and $d_{2}$ represented as vectors of word frequencies or embeddings. We can
calculate their cosine similarity as:

$\text{similarity}(d_{1},d_{2})=\frac{\sum_{i=1}^{n}w_{1,i}\times
w_{2,i}}{\sqrt{\sum_{i=1}^{n}w_{1,i}^{2}}\times\sqrt{\sum_{i=1}^{n}w_{2,i}^{2}}}$

where $n$ is the number of words in the vocabulary, $w_{1,i}$ and $w_{2,i}$
are the word frequencies or embeddings of the $i$-th word in the two documents
$d_{1}$ and $d_{2}$, respectively.

This formula measures the cosine of the angle between the two vectors, ranging
from -1 (opposite directions) to 1 (the same direction). A value of 0
indicates that the two vectors are orthogonal (perpendicular) to each other,
meaning they have no similarity.

##  2 Literature Review

###  2.1 Understanding Health Disparities through AI: Previous studies

Over the past few decades, machine learning has emerged as a game-changing
technology with the potential to revolutionize various domains, including
healthcare. In the medical field, artificial intelligence/machine learning
(AI/ML) has made significant advances in the diagnosis and prognosis of
diseases, allowing researchers to understand the behavior of the human body
better and classify medical images or videos [13]. One of the most recent and
notable applications of machine learning in healthcare is its ability to
accurately detect COVID-19 by analyzing chest radiographs, reducing the risk
of human transmission and infection. With the development of generative
adversarial networks (GANs) and other machine learning algorithms, the
potential for advancements in the healthcare sector is vast. The possibilities
will continue to grow as technology advances[14].

Artificial intelligence (AI) in healthcare care is a promising field that
could revolutionize medicine by enabling more accurate decisions and
personalized treatment. However, progress in this area is limited by legal and
ethical issues, such as patient data privacy and concerns about non-consensual
data sharing [3]. Researchers have proposed a novel framework for generating
synthetic data that closely approximates variables’ joint distribution in an
original electronic health record (EHR) dataset to address this. Using a
conditional generative adversarial network (GAN) framework, the proposed
model, GAN, demonstrated reliability in joint distributions and consistently
outperformed state-of-the-art methods in evaluating similarity in model
performance across four independent datasets [15]. This method could provide a
solution to more open data sharing and enable the development of AI solutions
while lowering the risk of breaching patient confidentiality.

A potential gap in the GAN model is the lack of discussion on the
effectiveness of the proposed GAN method compared to accurate patient data.
While the model was evaluated for similarity in model performance across four
independent datasets, it is unclear how well the generated synthetic data
corresponds to the actual patient data. Further research could address this
gap and assess the practical applicability of the GAN framework in real-world
healthcare settings.

AI in healthcare and medicine has become increasingly popular. Still, the
quality and diversity of training data are critical in ensuring that the
algorithms function effectively and are free of biases. Biases can arise from
sample selection biases and class imbalances, which can degrade the
performance of trained AI models [16]. Synthetic data, which can be created
using accurate forward models, physical simulations, or AI-driven generative
models, have been proposed as a potential solution to overcome the lack of
annotated medical data in real-world settings. However, using synthetic data
raises several issues, such as their possible misuse for malicious purposes
and the need for regulatory frameworks to govern their usage in modifying AI
algorithms in healthcare [17]. Despite these challenges, synthetic data can
increase the diversity and representativeness of datasets and improve the
robustness and adaptability of AI models, leading to better medical decisions
in a broader range of real-world environments. Further research is needed to
fully understand the benefits and limitations of synthetic data in healthcare
and to develop appropriate policies to govern their use.

Studies have shown that healthcare providers’ implicit biases can influence
their clinical decision-making, communication, and treatment recommendations,
leading to differential treatment and outcomes for patients of different
races, ethnicities, sex, and socioeconomic backgrounds. Implicit bias, which
refers to unconscious attitudes or stereotypes that can affect an individual’s
behavior, has been recognized as a significant factor contributing to health
disparities in medical care [18]. For example, a provider may use words such
as "non-compliant" or "difficult" to describe a patient who misses
appointments or does not follow their recommended treatment plan, which can
reflect negative stereotypes about specific populations. Additionally,
healthcare providers may use language that assumes a patient’s cultural
background or beliefs, such as assuming that a Hispanic patient is a migrant
worker or that an Asian patient practices traditional medicine.

####  2.1.1 Domain-Specific Large Language Models for Healthcare

Recent advances in pre-trained contextualized language models (PLMs) have
developed several domain-specific PLMs, enabling various downstream
applications [19]. In mental healthcare, the need for existing PLMs has
hindered the potential benefits of such technology. However, with the release
of MentalBERT and MentalRoBERTa, two pre-trained masked language models for
mental healthcare, the research community now has access to tools that can
benefit the early detection of mental disorders and suicidal ideation [20].
Similarly, social media has been recognized as a potential source of
information for public health surveillance (PHS). Despite the potential, the
technology is still in its early stages, and currently, there are no PLMs for
PHS-related social media tasks. The introduction of PHS-BERT, a transformer-
based PLM for identifying PHS tasks on social media, opens up new
possibilities for the community to monitor disease trends and emergency cases,
track disease awareness and response, and identify possible outbreaks[19]. By
comparing and benchmarking the performance of PHS-BERT on 25 datasets from
different social media platforms related to seven other PHS tasks, the
research community can reduce computational costs and introduce new baselines
for future work across various PHS-related tasks. Overall, the development and
evaluation of domain-specific PLMs such as MentalBERT, MentalRoBERTa, and PHS-
BERT demonstrate the potential of PLMs to benefit various downstream
applications and encourage further research in developing domain-specific
PLMs.

Using general-purpose language models in natural language processing for
downstream modeling has become increasingly popular, but the text embeddings
they generate can pose privacy risks[21]. For instance, the privacy risks of
eight state-of-the-art language models, including Google’s Bert and OpenAI’s
GPT-2, demonstrate how sensitive information can be reverse-engineered from
unprotected embeddings. A study proposes four defenses to obfuscate these
embeddings and mitigate privacy risks. The study’s findings have significant
implications for the future use of general-purpose language models in
sensitive data applications and highlight the need for further research into
mitigation techniques that balance privacy and utility [22].

BERT (Bidirectional Encoder Representations from Transformers) is a powerful
and popular pre-trained language model developed by Google. BERT has
significantly advanced the natural language processing (NLP) field by
achieving state-of-the-art results in various downstream NLP tasks, such as
question answering, sentiment analysis, and text classification [23]. BERT
employs a transformer-based architecture, which allows it to capture complex
relationships between words and produce context-aware embeddings that
represent the underlying meaning of a sentence or document. Additionally, BERT
can be fine-tuned on specific downstream tasks by adding task-specific layers
on top of the pre-trained model, improving its performance on those tasks
[24]. BERT’s success has led to widespread adoption in industry and academia
and has opened up new possibilities for developing advanced NLP applications.
However, the large size of BERT and its resource-intensive nature pose
challenges for practical applications on resource-constrained devices.
Therefore, researchers actively explore ways to compress and optimize BERT
models for more efficient deployment in real-world applications.

SciBERT is a pre-trained language model developed by the Allen Institute for
Artificial Intelligence (AI2) in collaboration with the University of
Washington and the Allen Institute for Cell Science. Unlike BERT, which was
trained on a general corpus of text, SciBERT is specifically designed to
improve the performance of natural language processing (NLP) models on
scientific and biomedical texts[25]. SciBERT is trained in a large corpus of
scientific articles, including papers from biology, chemistry, and computer
science. The model also includes specialized vocabularies and embeddings to
represent better the complex language and terminology used in scientific
publications [26]. These features have been shown to significantly improve the
performance of SciBERT on a range of scientific NLP tasks, such as named
entity recognition, relation extraction, and sentence classification. In
addition, SciBERT’s pre-training can be fine-tuned on specific downstream
tasks, allowing it to achieve state-of-the-art performance on a range of
biomedical and scientific tasks[27]. The success of SciBERT has led to
increased interest in developing domain-specific language models that can
better capture the nuances of specialized domains, opening up new
possibilities for developing advanced NLP applications in various scientific
and technical fields.

In a recent research article, ChatGPT was evaluated in the context of
improving knowledge about healthcare topics, such as vaccinations and
lifestyle choices. The article highlighted that although ChatGPT has the
potential to be a valuable tool in this domain, concerns remain regarding the
reliability of the information it provides. Specifically, it is unclear
whether the source of information used by ChatGPT can be considered
trustworthy and accurate. This raises important questions regarding the use of
ChatGPT in healthcare contexts and the need for further research to ensure
that the information it provides is reliable and of high quality.[28]. ChatGPT
is a language model based on GPT3, so it can potentially perpetuate bias based
on its training data [29]. Indeed, developers of ChatGPT modify or filter the
training data to reduce bias in its outputs. However, the specific details of
this process still need to be fully disclosed by OpenAI. It is important to
note that, while debiasing is crucial, it can also mask reality, and the
precision of using such a model to reveal bias issues may be questioned [30].

##  3 Methodology

Health disparities are a critical issue affecting the well-being of various
groups, including low-income populations, rural communities, and racial and
ethnic minorities [31]. Addressing these disparities is crucial to ensuring
equitable access to healthcare and reducing health inequalities. Large
language models (LLMs) have emerged as a promising technology to aid this
effort. By enabling natural language processing and generation, LLMs have the
potential to improve health communication and increase productivity in the
exam room. However, their use in healthcare also presents challenges, such as
ensuring data diversity and addressing privacy and bias concerns[32]. This
study highlights the potential of LLMs, specifically, BERT and SciBERT, to
improve physician productivity in the exam room. However, it also demonstrates
the need for careful attention to the ethical and equitable development and
implementation of these technologies. As we continue to explore the use of
LLMs in healthcare care, it is critical to prioritize the needs of underserved
populations and ensure that these technologies do not perpetuate health
disparities.

Figure 1: Flowchart Comparative Study with Health Disparities prompts

Researchers have developed domain-specific language models by training the
BERT architecture from scratch on a corpus specific to a particular domain
instead of using the general-purpose English text corpus utilized to train the
original BERT model. This methodology results in a language model with
vocabulary and word embeddings more tailored to the needs of domain-specific
NLP tasks than the original BERT model. A few instances of such models
include:

SciBERT (biomedical and computer science literature corpus)

FinBERT (financial services corpus)

BioBERT (biomedical literature corpus)

ClinicalBERT (clinical notes corpus)

mBERT (corpora from multiple languages)

patentBERT (patent corpus)

We used BERT and SciBERT to test cosine similarity in different contexts of
health disparities of text queries. Research on potential biases in AI toward
healthcare decisions is currently insufficient. Figure 1 shows the method used
to text similarities using BERT and SciBERT. Our approach uses a semantic
similarity task; we have three pieces of text, calling them "query", "A", and
"B", which are all on health disparities topics. We did this structure so that
the query text is always more similar to A than B.

In NLP, words are often represented as numerical vectors in a high-dimensional
space called an embedding space, also known as word embeddings. Word
embeddings capture the meaning of words in the context of the language they
are used in and enable computational models to reason about the meaning of
text. One common way to measure the similarity between two word embeddings is
to use cosine similarity. Cosine similarity calculates the cosine of the angle
between two vectors in the embedding space and ranges from -1 (opposite
directions) to 1 (same direction). To calculate cosine similarity, we take the
dot product of the two vectors and divide it by the product of their lengths.

##  4 Results

Labels | Target Text | Models | CS
---|---|---|---
Text Query 1 |  During a routine check-up, a patient from a historically marginalized community discussed their struggles with accessing affordable healthcare, highlighting health disparities in the doctor’s room. |  |
Query A |  The doctor expressed concern over the significant health disparities low-income communities face. | SciBERT | 0.91
BERT | 0.86
Query B |  The observed cosmic shear auto-power spectrum receives an additional contribution due to shape noise from intrinsic galaxies. | SciBERT | 0.83
BERT | 0.60
Table 1: Cosine similarity score with health disparities against astrophysics topic. Labels | Target Text | Models | CS
---|---|---|---
Text Query 2 |  Food desert is an area with little to no access to affordable and nutritious food, which can lead to poor health outcomes for residents. For instance, in the rural community of Meadowville, there is no grocery store within a 50-mile radius. |  |
Query A |  Food deserts have been linked to health disparities, such as higher rates of obesity, diabetes, and cardiovascular disease. | SciBERT | 0.92
BERT | 0.81
Query B |  Life expectancy refers to the average years a person is expected to live based on their birth year, gender, and other characteristics. | SciBERT | 0.93
BERT | 0.76
Table 2: Cosine similarity score with food desert against life expectancy topic. Labels | Target Text | Models | CS
---|---|---|---
Text Query 3 |  Race can indicate certain health risks, such as sickle cell anemia in African descent or Tay-Sachs disease in individuals of Ashkenazi Jewish descent. |  |
Query A |  AI has the potential to perpetuate health disparities if not developed and used responsibly. | SciBERT | 0.96
BERT | 0.91
Query B |  AI has the potential to help reduce health disparities by improving access to quality healthcare. | SciBERT | 0.96
BERT | 0.90
Table 3: Cosine similarity score: AI improving health disparities using race alone vs. AI perpetuating health disparities topic Labels | Target Text | Models | CS
---|---|---|---
Text Query 4 |  Race, external factors, diet, and age can indicate certain health risks, such as sickle cell anemia in African descent or Tay-Sachs disease in individuals of Ashkenazi Jewish descent. |  |
Query A |  AI has the potential to perpetuate health disparities if not developed and used responsibly. | SciBERT | 0.90
BERT | 0.74
Query B |  AI has the potential to help reduce health disparities by improving access to quality healthcare. | SciBERT | 0.88
BERT | 0.71
Table 4: Cosine similarity score: AI improving health disparities using race
plus other factors vs. AI perpetuating health disparities topic

SciBERT is a variant of BERT specifically trained on scientific papers, making
it a useful tool for natural language processing (NLP) tasks in the scientific
domain [33]. Several studies have demonstrated the effectiveness of SciBERT in
various NLP tasks, such as extracting scientific information from images and
classifying drugs based on free data [34]. Additionally, SciBERT has been
shown to outperform the original BERT model in assigning higher similarity
scores to matched clinical trial texts.

Cosine similarity has been used in various tasks, such as text classification
and question answering [35]. For example, in a study on the scientific
literature on COVID-19, SciBERT was used as a pointer network to select an
answer start and end index given a question and a paragraph [36]. In another
study, researchers proposed a method for determining the similarity of text
documents for the Kazakh language, taking into account synonyms, and evaluated
the method’s effectiveness using cosine, Dice, and Jaccard similarity measures
[37].

We used the first two queries in Query 1 and 2 as a baseline, shown in Tables
1 and 2. We found that SciBERT correctly distinguishes for query B in both
cases. However, in Query 3, Table 3, SciBERT does not distinguish nither to
query A or B. Instead, BERT was slightly biased toward query A. When we add
other factors besides race in Query 4, Table 4, which improves the similarity
towards Query A.

We compare two models for semantic similarity and how well they rank the
similarities. We did not compare the specific cosine similarity values across
the two models. We have structured our example as "is query more similar to A
or to B?"

Query three and A discuss race as an indicator in a certain disease, such as
sickle cell anemia, and how the race factor can perpetuate health disparities.
In contrast, B discusses the positive side of AI in health disparities.
However, to recognize the similarity between queries three and B, LLMs must
know that race alone is not responsible for anemia in African descent.

The data analysis was performed using Python software (Google Colab), and the
code used for the analysis is available in the following GitHub repository:

https://github.com/Yohnjparra/CosineSimilarity-
HealthDisparities/blob/main/Generative_AI_Health_Disparities.ipynb

We believe that SciBERT, trained in biomedical text, would better distinguish
similarities in a healthcare disparities context than BERT. However, BERT, a
multipurpose language with a lower similarity score, makes a slight
distinction better.

Cosine similarity is a widely used measure for comparing the similarity
between two vectors in a vector space model (VSM). However, some studies have
pointed out that cosine similarity can be biased by features of higher values
and may not consider how many features two vectors share. To address these
issues, researchers have proposed several modifications to cosine similarity,
such as the soft cosine measure and the distance-weighted cosine similarity
measure.

In our study, we examine the performance of BERT and SciBERT in recognizing
the role of race as a factor in certain diseases. We found that in some cases,
the models were biased toward race as a unique factor, which can perpetuate
health disparities. Specifically, we identified a few examples where "race" as
a unique factor is already biased in the trained corpus data for BERT and
SciBERT. This highlights the importance of carefully examining the training
data and ensuring they are representative and unbiased. Additionally, it
underscores the need for ongoing efforts to improve the fairness and accuracy
of NLP models, particularly in the context of health disparities. Our findings
suggest that further research is needed to develop more effective methods for
addressing bias in NLP models and promoting health equity.

##  5 Discussion

Health disparities are a critical issue that affects the well-being of various
social groups, including low-income populations, rural communities, and racial
and ethnic minorities. Addressing these disparities is crucial to ensuring
equitable access to healthcare and reducing health inequalities. Large
language models (LLMs) have emerged as promising technology that can
potentially aid this effort. By enabling natural language processing and
generation, LLMs have the potential to improve health communication and
increase productivity in the exam room. However, its use in healthcare also
presents challenges, such as ensuring data diversity and addressing privacy
and bias concerns. This study highlights the potential of LLMs, specifically,
BERT and SciBERT, to improve physician productivity in the exam room. However,
it also demonstrates the need for careful attention to the ethical and
equitable development and implementation of these technologies. As we continue
to explore the use of LLMs in healthcare care, it is critical to prioritize
the needs of underserved populations and ensure that these technologies do not
perpetuate health disparities.

Integrating Large Language Models (LLMs) into healthcare workflows can
significantly increase the productivity of physicians and other healthcare
workers, from back-office staff to schedulers. Using generative AI to create
draft responses when communicating asynchronously with patients can save time
and improve the quality of care. However, it is essential to ensure that the
LLMs are appropriately trained on healthcare terminology and do not perpetuate
healthcare disparities. There is a risk of increasing healthcare inequalities
when LLMs do not accurately identify similarities between sentences or words
in healthcare terminology. It is crucial to recognize that LLMs are not a
panacea and that their integration must be approached with caution and
thoughtful consideration of ethical and equitable implications. With proper
implementation, LLMs can be valuable in reducing healthcare disparities and
improving access to care for underserved populations.

Our results demonstrate that BERT outperformed SciBERT when comparing text
queries on health disparities. This result is somewhat surprising given that
SciBERT was specifically designed as a domain-specific model trained in
biomedical corpora. This suggests that disparities may not have been
adequately represented in the training data for SciBERT, whereas BERT has a
broader background training across diverse corpora. One can also suggest that
data used as the basis for SciBERT give low importance on ethical issues
compared to the core scientific information.

##  6 Conclusions

In conclusion, health disparities are a pressing issue that affects various
social groups and must be addressed to ensure equitable access to health care
and reduce health inequalities. Large language models (LLMs) hold significant
potential for improving healthcare productivity and communication, but their
implementation must be approached with care. The study highlights the
potential of LLMs, such as BERT and SciBERT, to increase physician
productivity in the exam room. However, it also emphasizes addressing ethical
and equitable development and implementation, including data diversity,
privacy, and bias concerns. To ensure that LLMs do not perpetuate health
disparities, it is critical to prioritize the needs of underserved
populations. Although LLMs are not a panacea, they can be valuable in reducing
healthcare disparities and improving access to care with proper
implementation.

Our goal is to demonstrate how some pre-trained language models may not
effectively account for health disparity contexts. It is important to consider
this topic, as language models are expanding in the health field, and ethical
issues may arise if they are not properly applied. Health disparities are a
critical concern, and it is crucial to ensure that language models are
sensitive to these disparities to avoid exacerbating existing biases. By
highlighting the limitations of some pre-trained language models in this
regard, we hope to draw attention to the need for more inclusive and equitable
approaches to natural language processing in healthcare.

ACKNOWLEDGMENT

The authors acknowledge NIH BioMed Grant Number 150108136 under Florida A&M
University and CI-New: Cognitive Hardware and Software Ecosystem Community
Infrastructure for allowing us to run our application in their infrastructure
(Nautilus).

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