SYMBIOSIS COLLEGE OF

NURSING
GROUP PROJECT ON “LITERATURE SEARCH” (Group 11)

Submitted on: 8/3/2025

INTRODUCTION.

Chronic illnesses, including high blood pressure, diabetes, arthritis, and asthma, are among the
common medical problems worldwide affecting millions of people and somehow placing serious
pressure on the healthcare system. Management for these conditions generally involves
complicated ongoing treatment regimens such as constant monitoring, medication adherence,
lifestyle changes, and regular visits to a doctor. However, the fragmented nature of healthcare,
limited access to medical experts, and the challenges associated with managing multiple ailments
or therapies have made it difficult for many a patient to keep up with these ongoing demands.
The introduction of artificial intelligence (AI) chatbots in chronic illness management addresses
several major challenges for healthcare providers. Such bots can clear up administrative work by
giving patients immediate answers to relevant health questions, freeing the medical professional
to devote much-needed time to treatment. [Liu, S., . et. .al.(2020)]

BODY:

In chronic diseases that require continuous monitoring over time, these tools enable them to
perform key monitoring functions. AI-based chatbots, especially for identifying individuals
suffering from long-term conditions, have continued to be justified. These AI chatbots are
already bridging the increasing gap between chronic conditions, enhancing patient interaction,
and providing a rather needed supportive medical service. That notwithstanding, it's challenging
not to see the apparent lack of reviews within the academic literature analyzing the implications
of AI chatbot interventions in healthcare. A multitude of databases such as PubMed MEDLINE,
CINAHL, EMBASE, PsycINFO, ACM Digital Library, and Scopus were used to undertake a
systematic review of the existing literature. Papers included in the review were original
investigations exploring the use of chatbots or any other kind of AI architecture in the
prevention, management, or rehabilitation of chronic illnesses. There were 784 results of which
eight studies were identified as meeting inclusion criteria. The diseases were receiving favorable
reactions. However, due to a lack of technical documentation, there is insufficient data to support
their usefulness. Future research should include full details and prioritize patient safety.
[Kurniawan, M. H., et. .al.(2024)]

Care and consequent training of educators such as nursing learners depend very much on gaining
contextual background in taking care of others-a matter which artificial intelligence-ChatGPT-
only provides feedback on. Beyond vocational training, it can be used for course and other
administrative-related work. Students can use it for individualized self-paced learning. Problem-
based learning may utilize the technology of AI-ChatBot to provide the learners with very good
hands-on practice. The lens of reliance has played the role of a great concern concerning
technology, allowing high ease of plagiarism that fades critical thinking. Hence, educators have
to present clear standards for allowed use which highlight the need for critical thinking and
correctness in citation. Educators need to change curricula and methodologies. AI-Chatbot
technology could boost nursing by allowing tasks to be performed in days that normally take
days, allowing nurses to focus more on patient care. There is hope in using the AI-Chatbots not
only for the social support of the patient but also, due to their therapeutic properties in mental
health, for the promotion of the patient keeping less overworked exhausted nurses keeping a
good relief. Nearing the barriers to the technological robustness of nursing research and
informatics, AI-Chatbots can provide a way for nursing students from a nontechnical field to
gain access to these innovations. AI-Chatbot technology simply rinses out the work of nurses
allowing them more time for a piece of patient care and aiding in nursing education. [Huynh,
T., et. al. (2023)]
The COVID-19 pandemic being a major global disaster had a serious mental and emotional toll
on global populations, particularly among females, who were affected more than males in terms
of anxiety and sadness. This study examined interactions with Replika, an AI-powered
conversational partner during the COVID-19 pandemic, to understand digitally mediated
empathy, and how the interweaving of the empathic and communicative processes of resilience
acts as a coping strategy for COVID-19 disruption. Many methods were combined in research on
the use of and effects from Replika: ethnographic research, in-depth interviews, and theory-based
analysis. The findings from this study extend the theory of empathy from an intrapersonal
communication theory orientation to human-AI interactions and shows that five types of digitally
mediated empathy arise among Chinese female Replika users, bearing different levels of
involvement of cognitive empathy, affective empathy, and empathic response in information
processing processes: companion buddy, responsive diary, emotion-handling program, electronic
pet, and venting tool. When the AI-generated information and collaborative interactions with the
AI chatbot are being processed, several facets of mediated empathy surface as unanticipated
pathways to resilience and user well-being. This work tackles a research void by exploring
processes of empathy and resilience in human-AI interaction. [Jiang, Q., et. al. (2022)]

This study proposes a chatbot that makes it easier for users to find and understand open-access
adverse-event records regarding medical devices, obtained from the MAUDE database. It is
generative AI technology powered with the capabilities of search and count queries. The chatbot
acts on the user's natural language question, generates needed API calls, and summarizes the
adverse event reports using the open FDA API and GPT-4 model, respectively. Along with that,
the chatbot also provides a link for downloading the original reports. The model's ability to
produce correct API calls is trained on a few query-URL pairings and then evaluated and
improved. The other aspect of quality assessment constitutes human expert evaluations of the
content-based summaries. This is a huge stride towards making patient safety data accessible,
reversible, and adjustable for a wider audience of researchers. [Yu, Y., el .al. (2024)]
This review follows an organized approach based on four key dimensions: eligibility criteria,
selection of studies, data extraction, and data synthesis. Studies on AIs and hybrid chatbots in
healthcare mainly concentrated on chronic disease management and mental health assistance.
The review considered peer-reviewed scholarly publications dated mainly between the years
2022 and 2025 and published in English. Out of 116 screened, 29 studies fulfilled the inclusion
criteria. A standard template for data extraction included study objectives, procedures followed,
results, and challenges or limitations faced. The thematic analysis determined four themes: AI
applications, technological advancements, user acceptance, and challenges/ethical dilemmas.
Data were subjected to statistical and content analyses in quite rigorous synthesis in order to
ensure robust findings. (Wah J. N. K. (2025).

Cancer Informatics for Cancer Centers hosted its biennial symposium, Precision Medicine
Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University),
and Walter J. Curran, MD (GenesisCare) in August 2022. Over three days, speakers covered a
wide range of topics relevant for radiation oncology and the whole cancer informatics
community, such as biomarker development, decision support algorithms, novel imaging tools,
theranostics, and AI in the radiotherapy workflow. Since the symposium, there have been major
changes in the promise and possibility for integrating AI in clinical care mainly through big
breakthroughs in generative AI. AI is now more ripe than ever to transform cancer care. As a
discipline with an enormous consumption and generation of digital data, radiation oncology is
likely to be one of the initial ones transformed by artificial intelligence. Being versed in data
collection, management, and analysis, it is the informatics community that will play a pivotal
role in paving the way for radiation oncology to take proper advantage of the technological
improvements. In this report, we present the highlights from the symposium, which took place in
Santa Barbara, California, from August 29 to August 31, 2022. [Bitterman, D. S., el. al. (2023) ]
CONCLUSION:

Chatbots powered by AI are mutating and shaping into a new host in caring for chronic diseases,
offering such learning levels of comfort and support to these patients. The personalized at best-
real-time advice, check-ups, reminders-all enable the uninformed first-time-in-Charge user more
responsible for oneself, be it in engagement or adherence. AI chatbots, in continual analysis on
data, would come with an earlier sense of opportunity or a hindsight eve if of potential
complications making guidance relaxing to patients living through complexities required by
chronic illnesses. The benefits also come to the healthcare professionals as in these technologies
they become more pain-killers towards the administrative activities involved and proactive
towards the patients. The algorithmic-advancing paradigm expresses the personalization they
offer, through the bundling with other health technologies like wearables, which promises
alterations again in managing chronic illnesses. Never a replacement for human healthcare
providers, AI chatbots may become a complement in enhancing patient outcomes, improving
access to care, creating more scalable and sustainable healthcare delivery solutions. As AI
continues to evolve and expand, the full expectation would be on the innovations these provide
henceforth for chronic illness management, and so safely lead millions to live healthier and
empowered lives. (McKinsey & Company. (2021, April 15).
REFERENCEs:

Jiang, Q., Zhang, Y., & Pian, W. (2022). Chatbot as an emergency exist: Mediated empathy for
resilience via human-AI interaction during the COVID-19 pandemic. Information processing &
management, 59(6), 103074. https://doi.org/10.1016/j.ipm.2022.103074

Kurniawan, M. H., Handiyani, H., Nuraini, T., Hariyati, R. T. S., & Sutrisno, S. (2024). A
systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for
managing chronic illness. Annals of medicine, 56(1), 2302980.
https://doi.org/10.1080/07853890.2024.2302980

Liu, S., Liu, Z., Zhang, Y., & Wang, Z. (2020). AI chatbots for chronic disease management: A
systematic review. Journal of Medical Systems, 44(9), 164. https://doi.org/10.1007/s10916-020-
01610-0

McKinsey & Company. (2021, April 15). The role of AI in chronic disease management.
McKinsey & Company. Retrieved from https://www.mckinsey.com

Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023). Nursing education in
the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet?. Nurse
education today, 129, 105917. https://doi.org/10.1016/j.nedt.2023.105917

Wah J. N. K. (2025). Revolutionizing e-health: the transformative role of AI-powered hybrid

Yu, Y., Shi, Y., Feng, Y., & Gong, Y. (2024). Developing a Generative AI-Powered Chatbot for
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https://doi.org/10.3233/SHTI240639