Journal of Medicine, Surgery, and Public Health 5 (2025) 100173
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Journal of Medicine, Surgery, and Public Health
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Artificial intelligence for access to primary healthcare in rural settings
A R T I C L E I N F O
A B S T R A C T
Keywords:
Healthcare
Rural medicine
Artificial intelligence
Technology
Health
Artificial Intelligence (AI) may enhance access to primary health care in rural settings, especially in areas with an
underserved and rural populace, due to systemic challenges in infrastructure inadequacies, shortages of trained
professionals, and poor preventive measures. This paper discusses the transformative roles of AI technologies in
the efforts toward bridging these gaps by improving healthcare delivery and access. With the rise of machine
learning (ML) and natural language processing (NLP), AI applications may enhance diagnostic accuracy, speed
up and facilitate patient interfaces, and perform resource management more optimally. This list of benefits seems
promising, but the realization of AI in healthcare faces quite a few challenges: it requires ethical considerations,
assurance of data safety, and sound legal frameworks. While AI, Internet of Things (IoT), and mobile health
(mHealth) technologies create innovative solutions in remote monitoring and consultation, these technologies
help promote prevention. Realizing the full potential of AI in rural health is thus a challenge in both infra­
structural and socio-economic spheres, requiring active collaboration. This present study indicates that it is high
time there was an urgent need for high-quality research with real-world evaluation regarding the effectiveness of
AI in bringing improvement in health outcomes among people in rural settings.
Dear Editor,
Artificial Intelligence (AI) has the potential to revolutionize access to
primary healthcare [1]. Geographical isolation, inadequate health in­
frastructures, and a shortage of qualified health professionals have
combined to result in poor health outcomes in rural settings [2]. Rural
residents experience higher age-adjusted mortality and prevalence rates
related to cardiovascular disease, diabetes, cancer, unintentional injury,
and stroke [3–6]. Rural residents also have shorter life spans [7]. Besides
the lack of emphasis on preventive healthcare in rural communities,
combined with stigma and privacy concerns, further deters individuals
from seeking necessary care [8]. Therefore, AI bridges these gaps in
innovative ways to bring better healthcare delivery and make essential
services more accessible to the outlying communities.
AI technologies will dramatically improve the efficiency and quality
of healthcare services in rural and under-reserved areas [9]. Training
health workers to use AI systems will mitigate the shortage of qualified
healthcare providers [10]. The access and quality of rural health services
will be also improved [11]. AI tools are used for the early detection,
diagnosis, and management of diseases. The neural networks, which
include the convolutional and abductive networks, have demonstrated
high accuracies in application areas of use [12]. Studies have indicated
an urgent need for high-quality, real-world research in developing its
value in healthcare and public health [13–15].
Rural population: global overview
Globally, more than 3.4 billion people live in rural areas, comprising
roughly 43 % of the world’s population lives in rural settings [16–19].
Most of the rural population inhabits Asia and Africa; India and China
possess the largest rural populations [20]. Rural healthcare access re­
mains a significant concern due to barriers such as geographic
dispersion, economic constraints, and infrastructural limitations. These
populations are usually characterized by lower incomes, inadequate
healthcare access, and relatively high unemployment rates compared to
urban communities [21,22]. Consequently, this population is most sus­
ceptible to health disparities, which generally include higher chronic
disease burdens and less access to healthcare professionals. AI technol­
ogies can bridge this gap by bringing much-needed efficiency and
effectiveness in healthcare delivery to these underserved areas. It not
only improves the quality of care but also saves health professional’s
time in carrying out diagnoses, enabling them to be more
patient-interactive and have sufficient time for treatment planning [23].
Challenges in healthcare access in rural areas
Rural areas usually have significant barriers to accessing healthcare
services compared to urban areas. One of the main challenges includes a
shortage or lack of health infrastructures like hospitals and diagnostic
facilities [24]. One more major reason behind insufficient healthcare in
rural settings is a noted shortage of health professionals. Many rural
populations have been shown to face shortages of primary and specialist
healthcare providers, leading to poorer health and increased mortality
rates compared to urban communities [25–28]. Economic hardships
further hinder access, as underfunded healthcare infrastructure makes
services both unavailable and unaffordable [29–31]. In addition, rural
residents usually have lower levels of health knowledge and awareness
[32]. Also, health education and promotion programs are often lacking
in rural settings [33].
Travel distance is another major barrier, often resulting in delays
that worsen health conditions, while unreliable transportation exacer­
bates the issue; for example, studies highlight transport as a key obstacle
to accessing maternal and child health services [34,35]. Inadequate
https://doi.org/10.1016/j.glmedi.2024.100173
Received 10 November 2024; Received in revised form 13 December 2024; Accepted 13 December 2024
Available online 15 December 2024
2949-916X/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
M.F.H. Lamem et al.
Journal of Medicine, Surgery, and Public Health 5 (2025) 100173
transport infrastructure also forces difficult prioritization of health
needs in rural settings [36]. This situation is reiterated across different
contexts where rural populations have to grapple with inadequate
transport infrastructure, making priorities hard to make in terms of
health needs [37]. Addressing these challenges demands a comprehen­
sive effort to improve healthcare funding, infrastructure, and workforce
availability [38].
advice [58]. This will result in real-time advice on diagnosis, treatment
options, or patient management and, when appropriate, suggestions for
changes in care that will help improve outcomes [59]. Deep learning
algorithms have shown considerable promise in the area of medical
image processing for analyzing a variety of imaging modalities,
including MRI, CT scans, and X-rays [60]. With these technologies,
healthcare workers can be supported by preliminary assessment sup­
port, therefore relieving the workload and focusing their attention on
patients [61]. Integration with AI and telemedicine methods has the
potential to serve even the most remote sites, thereby minimizing the
transportation of subjects over long distances in search of services [62].
This AI will also widen the accessibility to patient data interpretation
when applied to electronic medical records (EMR) using natural lan­
guage processing (NLP) thus informed decisions could be derived [63].
This approach has emphasized the utilization of diverse data sources
toward enhancing predictive accuracy, a theme similarly developed in
other studies focused on diabetes [64,65].
However, the implementation of AI in healthcare is not without
challenges. Ethical considerations, data security, and a need for legal
frameworks remain paramount concerns that ensure AI technologies are
availed responsibly and effectively [66,67]. Another aspect of engage­
ment is trust from health providers in AI, which can easily be dented
with talks of accountability and perceived risk of replacement by tech­
nology [68]. Various recommendations regarding the use of AI for rural
health can be made to the policymakers and healthcare practitioners for
better results:
Transforming medical service delivery with AI
AI can potentially help extend access to health care in rural areas, at
least by mitigating gaps in health resources and the workforce [39,40].
The most important advantages of AI in rural healthcare are related to
improving access to disease screening and early detection. Several
studies have identified that AI-based screening for diseases like diabetic
retinopathy and skin cancer is cost-effective and can increase its reach in
remote areas [41,42]. AI in use within a healthcare context is altering
the mode of medical service delivery [43], and AI systems in healthcare
are primarily designed to optimize processes, reduce dosage errors,
manage medication, and make preliminary diagnoses [44]. As AI has
promised to revolutionize health due to its efficiency, cost-effectiveness,
and preciseness, there is a developing concern that this new trend could
potentially compromise the empathetic, personalized care that
conventionally characterizes the relationship between patient and pro­
vider [45]. Alarmingly, almost 50 % of the world receives consultations
from physicians that are 5 minutes or less, which will begin to raise
questions about the quality of care and whether such consultations can
address the patient’s health concerns [46]. In this context, leveraging AI
chatbots can streamline patient interactions. Recent advances in large
language models (LLMs), such as ChatGPT, have shown some success in
improving the quality of responses by virtual assistants in health-related
scenarios [47]. As a result of this fact, allows the chatbot then to be
increasingly adept at responding to more complicated health-related
inquiries that might emanate from patients themselves, therefore
developing an improvement in outcomes and satisfaction [48]. It can
also provide timely health information, symptom understanding for
patients, suggesting possible medications, reminders to take medicines,
and management of chronic diseases, all of which will go a long way in
improving patient compliance with treatment [49,50].
Besides, AI-driven applications in rural healthcare may also promote
efficiency in service delivery for the providers. Active AI technologies
can assist in automating routine tasks such as appointment settings and
follow-up with patients, freeing the professionals to attend to the more
complicated needs of patients [51]. This is not all; AI can also provide
insight into better resource planning and patient management beyond
improvement in prescription accuracy. Machine learning (ML) algo­
rithms of AI technologies can present the best practices in prescribing
drugs to health professionals by analyzing huge data sets and mini­
mizing the risks of committing prescription errors [52,53]. ML and
natural language processing (NLP) have been applied for diagnosis,
detection, and surveillance as AI-powered technologies, and they show a
high degree of accuracy on clinical tasks [54]. Mobile health (mHealth)
and Internet of Things (IoT) technologies have made remote health
monitoring a reality by keeping patients in homes rather than expensive
healthcare facilities like hospitals or nursing homes using noninvasive
and unobtrusive wearable sensors that provide continuous real-time
monitoring of physiological signs and activities of the patients that
can be accessed from distant facilities by the health personnel [55].
mHealth technologies, such as text messaging, have the potential to
address challenges faced in rural settings in managing chronic diseases
like tuberculosis, HIV, hypertension, cardiovascular disease, colorectal
cancer, and pneumonia [56]. These technologies could improve health
outcomes and quality of life through cost-effective healthcare solutions
[57].
AI-powered clinical decision support systems (CDSS) can further
help rural healthcare practitioners, who often lack access to specialist’s
i. Extensive training of health practitioners to incorporate and use
AI in clinical practice to achieve the relevant technology accep­
tance, trust, and competence in its application [69].
ii. Convene healthcare providers, companies developing technolo­
gies, to collaborate towards the elaboration of relevant solutions
for needs at country-community levels [70].
iii. Pilot-testing of AI applications for Rural Health demonstration of
proof of concept-effectiveness and best practices.
iv. Engage in developing and implementing AI solutions with local
communities to ensure that they respond to the particular needs
of rural populations.
v. Support different research in applying AI to rural health to study
innovative solutions for specific health challenges.
vi. Improve awareness and education among healthcare providers
and patients about the benefits and limitations of AI in healthcare
so that trust is inspired and adoption is increased.
Conclusion
In conclusion, AI application in primary healthcare holds immense
prospects of improving access to and quality of healthcare in rural
Bangladesh. Through AI technologies, academic health systems can
surmount some of the existing barriers toward timely, effective, and
equitable healthcare services. However, for the full potential of AI in
transforming rural healthcare, infrastructural and socio-economic
challenges need to be overcome through concerted efforts.
Ethical clearance
This article does not involve primary data collection or human sub­
jects, so ethical clearance is not applicable.
CRediT authorship contribution statement
Md Faiazul Haque Lamem: Conceptualization, Writing – original
draft, Writing – review & editing. Muaj Ibne Sahid: Investigation,
Visualization, Writing – review & editing. Anika Ahmed: Literature
review, Resources, Review & editing.
2
M.F.H. Lamem et al.
Journal of Medicine, Surgery, and Public Health 5 (2025) 100173
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This work did not receive any specific grant from public, commer­
cial, or not-for-profit funding agencies.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
We are deeply grateful to all authors whose referenced articles have
contributed much to this work.
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Md Faiazul Haque Lamem* , Muaj Ibne Sahid
Department of Pharmacy, R. P. Shaha University, Naryanganj 1400,
Bangladesh
Anika Ahmed
Department of Pharmacy, School of Pharmacy and Public Health,
Independent University, Dhaka 1245, Bangladesh
*
Corresponding author.
E-mail address: Lamem.hoq@gmail.com (M.F.H. Lamem).
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