Data Analytics and Artificial Intelligence in Healthcare Industry

IRJET Journal
IRJET JournalFast Track Publications

https://www.irjet.net/archives/V10/i8/IRJET-V10I8154.pdf

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 930
Data Analytics and Artificial Intelligence in Healthcare Industry
Venkata Preethi Mudili
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Artificial intelligence (AI) and data analysis
are emerging as transformative tools across diverse fields,
from economics to security. In healthcare, AI and data
analysis have driven significant changes, leveraging
learning algorithms for impactful outcomes. These
technologies, evident in academic works and global
implementations, hold immense promise. This paper reviews
recent healthcare-focused AI publications, highlighting
advancements and addressing challenges and ethical
concerns faced by healthcare and governments. The paper
aims to analyze current scientific trends, underscore AI's
potential in healthcare, and offer solutions for present issues
while envisioning future AI applications. Two case studies
further illustrate practical AI and data analysis
applications. Overall, AI and data analysis are reshaping
healthcare and various sectors. The paper contributes a
comprehensive overview of recent AI trends, recognizes
their potential, and provides insights to overcome
healthcare challenges while anticipating AI's future
impacts.
Key Words: Artificial intelligence, Data analysis, Scientific
trends, Healthcare challenges
1.INTRODUCTION
The rapid development of technology has led to the
emergence of artificial intelligence (AI) and data analytics
as forces of change, with far-reaching effects in many
different fields, from economics to security [1]. In
healthcare, these dynamic technologies have brought
about significant change by harnessing the power of
learning algorithms to achieve remarkable results. This
change is underscored by their prominent presence in the
academic literature and their widespread adoption
worldwide, demonstrating their potential for disruptive
advances[2]. Amid this landscape, this article presents a
comprehensive survey of recent AI-focused healthcare
publications. In doing so, it explains the advances made in
healthcare through artificial intelligence and data
analytics, and highlights advances that have changed
traditional practices. In addition, the paper plays a central
role in addressing the multifaceted challenges and ethical
considerations that both health entities and governments
grapple with as a result of such technological
changes[3][4]. The general purpose of this work goes
beyond retrospective analysis; it aims to identify current
scientific trends that support the synergy between AI and
healthcare, explore the hidden potential of AI in healthcare
and provide practical solutions that can overcome existing
challenges, looking ahead to the promising future of AI
applications. To illustrate the practical implications of
these concepts, the paper presents two moving case
studies that illustrate the concrete benefits of AI and data
analytics in real-world healthcare scenarios. Finally, this
study highlights the central role of artificial intelligence
and data analytics in reshaping healthcare paradigms and
permeating innovation in various fields [5]. By providing
an in-depth overview of the latest trends in AI, recognizing
its enormous potential, and providing insight into how to
address current health challenges, this book is a catalyst
for transformative change in predicting the continued
impact of AI for years to come.
The intertwining of technology and healthcare continues
to evolve at an astonishing pace, reshaping paradigms and
paving the way for a future where artificial intelligence
(AI) and data analytics stand as pivotal pillars of progress.
As these dynamic technologies flourish, they engender a
profound impact across diverse sectors, from economics
to security, sparking a paradigm shift that transcends
conventional boundaries [6]. Within the realm of
healthcare, the fusion of AI and data analytics has sparked
a revolution, propelling learning algorithms to new
heights and ushering in a era of unprecedented
achievements.
This article embarks on a journey through the labyrinth of
recent AI-focused healthcare publications, delving into the
transformative leaps accomplished through these
innovations. By dissecting and illuminating the strides
made, this paper not only unveils the evolution of AI
within healthcare but also unearths its latent potentials
[7]. It serves as a compass navigating through the intricate
challenges and ethical dilemmas faced by healthcare
entities and governments in the wake of technological
transformations.
Fig -1: Understanding AI in Healthcare Business
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 931
Yet, the scope of this work reaches beyond retrospection.
It assumes the role of a visionary, identifying
contemporary scientific currents that amplify the
symbiosis between AI and healthcare [8]. Moreover, it
unearths the veiled capacities of AI, proffering pragmatic
solutions to surmount existing obstacles, while casting an
anticipatory gaze toward the bright horizon of AI
applications.
In the spirit of tangibility, the paper presents two poignant
case studies that breathe life into the abstract
potentialities of AI and data analytics. These narratives
offer tangible evidence of the tangible benefits reaped
from these technologies within the realm of real-world
healthcare scenarios, affirming the substantial impact of AI
on the ground [9].
1.1 Advancements & Innovations in Healthcare AI
Before The combination of artificial intelligence (AI)
and data analytics has ushered in a new era of
advancement and innovation in healthcare. These
technologies have dramatically changed the way healthcare
is practiced, resulting in improved patient outcomes and
more efficient clinical processes [10]. AI-powered
diagnostic tools have demonstrated exceptional accuracy
in detecting various diseases such as cancer, heart disease
and neurological disorders, often outperforming human
experts. In addition, predictive analytics models use
historical patient data to predict disease progression,
enabling proactive interventions and personalized
treatment plans. This accuracy and early detection could
revolutionize disease management and improve patient
care. Innovations also extend to robot-assisted surgery,
where artificial intelligence algorithms help surgeons
perform complex procedures with greater precision,
minimizing invasiveness and recovery time. In addition, AI-
based drug development accelerates the identification of
potential compounds and streamlines the lengthy drug
development process. The integration of artificial
intelligence and health data has also facilitated the creation
of patient profiles that help tailor treatment to individual
needs, optimize regimens and minimize side effects.
Thanks to the constant monitoring of vital signs and health
trends, artificial intelligence allows healthcare workers to
quickly make informed decisions.
1.2 Global Applications and Case Studies
This section illuminates the concrete impact of AI and
data analytics on healthcare through real case studies and
global applications. We highlight cases where these
technologies have made a difference and explore practical
examples that demonstrate their potential [11]. These case
studies highlight how AI-enhanced diagnostics, care
personalization and predictive analytics have optimized
patient outcomes and simplified healthcare processes. By
studying successful applications around the world, we
gain insight into the versatility and scalability of AI-based
solutions in the healthcare ecosystem.
Fig -2: US Market AI in Healthcare
2. GLOBAL IMPLEMENTATION AND CASE STUDIES
The pervasive impact of artificial intelligence (AI) on
healthcare has transcended geographic boundaries and
ushered in a new era in medical practice and patient care.
This section illuminates the diverse landscape of AI
integration with compelling case studies that highlight
successful applications in various global healthcare
settings.
The convergence of artificial intelligence and medical
imaging at a major European medical center has redefined
diagnostic accuracy. Sophisticated algorithms carefully
analyze radiological images such as X-rays and MRIs,
revealing complex abnormalities that may escape human
control. This innovation speeds diagnosis facilitates early
intervention and reduces the risk of misinterpretation.
This case demonstrates the ability of artificial intelligence
to complement human knowledge and ultimately improve
patient outcomes.
Pharmaceutical research in Asia has seen a paradigm
shift with AI-based drug development efforts. As a result
of the collaboration between scientific institutions and
technological pioneers, artificial intelligence models have
been obtained that can predict possible drug interactions
and side effects. These speeds up the identification of
promising compounds, which greatly reduces the trial-
and-error cycle of drug development. This case highlights
the potential of AI to improve drug innovation and
potentially transform how new drugs are marketed.
AI-powered telemedicine solutions have emerged as
beacons of healthcare access in resource-rich regions
across Africa [12]. Using AI-powered chatbots, patients
can express symptoms and receive preliminary medical
instructions even when a doctor is not immediately
available. This initiative acts as a health channel in
underserved areas by providing timely counseling and
supporting remote diagnostics. This case exemplifies the
role of artificial intelligence in democratizing healthcare
and bridging geographic disparities.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 932
These illuminating case studies highlight the
transformative global impact of AI on healthcare systems.
They serve as beacons of inspiration and show the
possibilities of artificial intelligence to optimize
diagnostics, accelerate drug discovery and overcome
inequalities in healthcare. As these success stories spread
around the world, they encourage the collective medical
fraternity to embrace AI-based innovations that could
transform the landscape of healthcare worldwide.
3. CHALLENGES AND ETHICAL CONSIDERATIONS
The integration of artificial intelligence (AI) and data
analytics into healthcare presents a series of challenges
and ethical considerations that require careful attention.
Concerns about the privacy and security of patient data are
paramount, as the extensive collection and analysis of
sensitive health data increases the risk of unauthorized
access or data breaches. In addition, the potential for
algorithmic bias from historical data can lead to differences
in diagnosis and treatment, exacerbating existing health
care disparities. Ethically, the use of artificial intelligence in
clinical decision-making raises questions about
accountability and transparency. The "black box" nature of
some AI models creates challenges in understanding the
reasons behind certain medical decisions, making it
difficult for healthcare professionals to trust and validate
the results. Striking a balance between automated insights
and human knowledge is another challenging ethical
aspect, as over-reliance on technology can undermine the
patient-provider relationship and critical thinking.
Regulatory frameworks to ensure the responsible
development and deployment of AI in healthcare add
complexity. Achieving consensus on data ownership,
sharing and standards requires interdisciplinary
collaboration. Addressing these challenges and ethical
issues requires multifaceted strategies that include strong
data governance, algorithmic transparency, continuous
monitoring, and active collaboration with stakeholders to
develop a healthy landscape that harnesses the potential of
AI and adheres to ethical standards.
Navigating the terrain where artificial intelligence (AI) and
data analytics converge with healthcare unveils a tapestry
of challenges and ethical dimensions that merit thoughtful
consideration. The urgency of safeguarding patient privacy
and data security looms large, as the amplification of
sensitive health information's collection and scrutiny
escalates the specter of unauthorized access and breaches.
Moreover, the specter of algorithmic bias—emerging from
historical data—casts a shadow on diagnoses and
treatments, perpetuating disparities in healthcare.
In the realm of ethics, the integration of AI into clinical
decisions births a plethora of accountability and
transparency quandaries. The opaqueness inherent in
some AI models begets uncertainty about the rationale
underlying medical verdicts, impeding healthcare
practitioners from corroborating and confiding in these
outputs. Striking equilibrium between automated insights
and human expertise proves a tightrope walk, with
excessive reliance on technology undermining the sacred
patient-provider alliance and analytical acumen.
The labyrinthine regulatory frameworks that ought to
undergird the judicious development and deployment of AI
in healthcare further compound the intricacies. Plying the
waters of data ownership, sharing norms, and standardized
practices mandates interdisciplinary collaborations.
Tackling these multifarious challenges and ethical
quandaries necessitates a symphony of strategies,
including robust data governance, algorithmic lucidity,
vigilant monitoring, and vibrant engagement with
stakeholders. In harmonizing these elements, we shape a
landscape that not only capitalizes on AI's potential but
also upholds the venerable mantle of ethical standards.
4. CONCLUSIONS
In conclusion, this paper explored the current progress of
artificial intelligence (AI) in academia and industry,
highlighting its diverse applications in healthcare. Beyond
its potential, ethical concerns were examined, potentially
impacting society's future. Two healthcare cases
showcased AI's problem-solving prowess. As medical
systems evolve, AI and analytics will play a pivotal role,
improving outcomes and reducing errors. Ethical
guidelines are crucial for safe AI application. AI is
anticipated to enhance global healthcare, addressing
challenges, and making diagnostics and treatment more
precise and accessible.
In the ever-evolving narrative of healthcare, this study
serves as a compass, charting the course of artificial
intelligence (AI) in academia and industry, illuminating its
multifaceted healthcare applications. While potential
abounds, ethical underpinnings cast a profound shadow,
bearing the power to mold society's trajectory. Through
the lens of two illustrative healthcare cases, AI's capacity to
untangle complex challenges gleams. As healthcare
ecosystems unfurl, the orchestration of AI and analytics
assumes a linchpin role, reshaping outcomes and curbing
fallibility. With ethical signposts as guiding lights, the
voyage toward secure AI implementation unfurls. The saga
of AI's ascent within global healthcare unfolds, poised to
recalibrate diagnostics, therapeutics, and accessibility,
heralding a future of refined care and resolute solutions.
5. FUTURE SCOPE
In Artificial intelligence (AI), considered a key force in
future healthcare, is poised to revolutionize personalized
care, a major advance in the field. Despite initial challenges
in providing diagnostic and treatment recommendations,
AI is ruling the field. The increasing proficiency of artificial
intelligence in image analysis indicates that computer
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 933
systems will eventually dominate the evaluation of most
radiology and pathology images. Also, the speech and text
recognition extension cover patient communication and
clinical documentation tasks.
However, the biggest challenge is not the capabilities of
AI, but its seamless integration into standard healthcare
practices. Regulatory validation, integration with
electronic health records (EHR), compatibility with
comparable products, effective training of medical
professionals, securing funding and timely updates are
prerequisites for widespread adoption. These obstacles
will eventually be overcome but solving them will likely
exceed the timeline of technological development.
REFERENCES
[1] Nasir Abdul Jalil and Mikkay Wong Ei Leen. Big Data in
the Era of Pandemic COVID-19 : Application of IoT
based data analytics, Machine Learning and Artificial
Intelligence. 7.
https://doi.org/10.1145/3524383.3524433
[2] Urvashi Gupta and Rohit Sharma. 2023. A Study of
Cloud Based Solution for Data Analytics in Healthcare.
https://doi.org/10.1109/iscon57294.2023.10112083
[3] Muhib Anwar Lambay and S. Pakkir Mohideen. 2020.
Big Data Analytics for Healthcare Recommendation
Systems. In (2020 International Conference on
System, Computation, Automation and Networking
(ICSCAN)), 1–6.
https://doi.org/10.1109/ICSCAN49426.2020.926230
4
[4] Jieyuan Liu. 2020. Artificial Intelligence and Data
Analytics Applications in Healthcare General Review
and Case Studies.
https://doi.org/10.1145/3433996.3434006
[5] Narcisa Roxana Moşteanu. Artificial Intelligence
Helping the Fight against COVID-19. Supporting the
Pharmaceutical Industry beyond the Financial Aid.
https://doi.org/10.1145/3507485.3507495
[6] Wahyu Sardjono, Astari Retnowardhani, Robert Emil
Kaburuan, and Aninda Rahmasari. 2021. Artificial
intelligence and big data analysis implementation in
electronic medical records.
https://doi.org/10.1145/3512576.3512618
[7] Augustina O Ugwu, Xianghua Gao, Johnson O Ugwu,
and Victor Chang. 2022. Ethical Implications of AI in
Healthcare Data: A Case Study Using Healthcare Data
Breaches from the US Department of Health and
Human Services Breach Portal between 2009-2021.
https://doi.org/10.1109/iiotbdsc57192.2022.00070
[8] Jiaxin Zhang. Artificial Intelligence and Machine
Learning Algorithm Optimization Applied in Health
Big Data Digitization.
https://doi.org/10.1145/3495018.3501124
[9] Aisha Alobaidli, Al-Hareth Al-Khalifa, and Noura Al-
Mutairi. 2021. Integrating Blockchain and Artificial
Intelligence for Secure Healthcare Applications. In
2021 IEEE/ACS 18th International Conference on
Computer Systems and Applications (AICCSA), 298-
305.
https://doi.org/10.1109/AICCSA51660.2021.959571
2
[10] Yuchen Li, Saeid Motiian, Siyuan Chen, and Jiawen Liu.
2019. Explainable Deep Learning in Healthcare: A
Brief Survey. In 2019 IEEE EMBS International
Conference on Biomedical & Health Informatics (BHI),
1-4. https://doi.org/10.1109/BHI.2019.8834661
[11] Carolina Wählby, Charlotte Kibbel, and Mikael
Lundqvist. 2022. Artificial Intelligence and Machine
Learning in Medical Imaging—A New Era in
Healthcare. Diagnostics, 12(1), 1-15.
https://doi.org/10.3390/diagnostics12010008
[12] [12] Xing Wang, Zhihan Lv, Yanyu Xu, Ronglin Jiang,
and Yunfei Chen. 2021. Artificial Intelligence in
Healthcare: A Comprehensive Review. Artificial
Intelligence Review, 54(5), 4073-4117.
https://doi.org/10.1007/s10462-020-09919-2

Recomendados

ARTIFICIAL INTELLIGENCE-TRANSFORMING NURSING Original Research Paper por
ARTIFICIAL INTELLIGENCE-TRANSFORMING NURSING Original Research PaperARTIFICIAL INTELLIGENCE-TRANSFORMING NURSING Original Research Paper
ARTIFICIAL INTELLIGENCE-TRANSFORMING NURSING Original Research PaperDereck Downing
13 vistas3 diapositivas
Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and... por
Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and...Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and...
Explainable AI in Healthcare: Enhancing Transparency and Trust upon Legal and...IRJET Journal
26 vistas8 diapositivas
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEM por
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEMIMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEM
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEMijseajournal
23 vistas7 diapositivas
Artificial intelligence in healthcare por
Artificial intelligence in healthcareArtificial intelligence in healthcare
Artificial intelligence in healthcareIRJET Journal
37 vistas3 diapositivas
Hamid_2016-2 por
Hamid_2016-2Hamid_2016-2
Hamid_2016-2Sobia Hamid
146 vistas4 diapositivas
20Q91A6753 (1) (1).pdf por
20Q91A6753 (1) (1).pdf20Q91A6753 (1) (1).pdf
20Q91A6753 (1) (1).pdfNeerajPoosala
6 vistas37 diapositivas

Más contenido relacionado

Similar a Data Analytics and Artificial Intelligence in Healthcare Industry

List out the challenges of ml ai for delivering clinical impact - Pubrica por
List out the challenges of ml ai for delivering clinical impact -  PubricaList out the challenges of ml ai for delivering clinical impact -  Pubrica
List out the challenges of ml ai for delivering clinical impact - PubricaPubrica
9 vistas3 diapositivas
CHOOSELYF por
CHOOSELYFCHOOSELYF
CHOOSELYFIRJET Journal
9 vistas7 diapositivas
AI and machine learning in healthcare.pdf por
AI and machine learning in healthcare.pdfAI and machine learning in healthcare.pdf
AI and machine learning in healthcare.pdfMuhammadTayyab71890
29 vistas14 diapositivas
IRJET- Integration of Big Data Analytics in Healthcare Systems por
IRJET- Integration of Big Data Analytics in Healthcare SystemsIRJET- Integration of Big Data Analytics in Healthcare Systems
IRJET- Integration of Big Data Analytics in Healthcare SystemsIRJET Journal
61 vistas5 diapositivas
Role of ai in healthcare whitepaper november 2020 por
Role of ai in healthcare whitepaper november 2020 Role of ai in healthcare whitepaper november 2020
Role of ai in healthcare whitepaper november 2020 santoshkumar3075
70 vistas41 diapositivas
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing” por
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”IJAEMSJORNAL
18 vistas24 diapositivas

Similar a Data Analytics and Artificial Intelligence in Healthcare Industry(20)

List out the challenges of ml ai for delivering clinical impact - Pubrica por Pubrica
List out the challenges of ml ai for delivering clinical impact -  PubricaList out the challenges of ml ai for delivering clinical impact -  Pubrica
List out the challenges of ml ai for delivering clinical impact - Pubrica
Pubrica 9 vistas
IRJET- Integration of Big Data Analytics in Healthcare Systems por IRJET Journal
IRJET- Integration of Big Data Analytics in Healthcare SystemsIRJET- Integration of Big Data Analytics in Healthcare Systems
IRJET- Integration of Big Data Analytics in Healthcare Systems
IRJET Journal61 vistas
Role of ai in healthcare whitepaper november 2020 por santoshkumar3075
Role of ai in healthcare whitepaper november 2020 Role of ai in healthcare whitepaper november 2020
Role of ai in healthcare whitepaper november 2020
santoshkumar307570 vistas
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing” por IJAEMSJORNAL
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”
Managing the Pharmaceutical Supply Chain: ‘By Wire’ “The next big thing”
IJAEMSJORNAL18 vistas
Here are the Benefits of AI for the Medical Field in 2023 and Beyond!.pdf por Techugo
Here are the Benefits of AI for the Medical Field in 2023 and Beyond!.pdfHere are the Benefits of AI for the Medical Field in 2023 and Beyond!.pdf
Here are the Benefits of AI for the Medical Field in 2023 and Beyond!.pdf
Techugo51 vistas
Data science in healthcare-Assignment 2.pptx por ArpitaDebnath20
Data science in healthcare-Assignment 2.pptxData science in healthcare-Assignment 2.pptx
Data science in healthcare-Assignment 2.pptx
ArpitaDebnath2047 vistas
The emerging role of Generative AI in Healthcare..pdf por Bluebash LLC
The emerging role of Generative AI in Healthcare..pdfThe emerging role of Generative AI in Healthcare..pdf
The emerging role of Generative AI in Healthcare..pdf
Bluebash LLC84 vistas
Secinaro et al-2021-bmc_medical_informatics_and_decision_making por NethminiWijesinghe
Secinaro et al-2021-bmc_medical_informatics_and_decision_makingSecinaro et al-2021-bmc_medical_informatics_and_decision_making
Secinaro et al-2021-bmc_medical_informatics_and_decision_making
NethminiWijesinghe57 vistas
Application of Data Analytics to Improve Patient Care: A Systematic Review por IRJET Journal
Application of Data Analytics to Improve Patient Care: A Systematic ReviewApplication of Data Analytics to Improve Patient Care: A Systematic Review
Application of Data Analytics to Improve Patient Care: A Systematic Review
IRJET Journal13 vistas
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an... por Tauseef Naquishbandi
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...
Tauseef Naquishbandi1.1K vistas
Gleecus Whitepaper : Applications of Artificial Intelligence in Healthcare por Suprit Patra
Gleecus Whitepaper : Applications of Artificial Intelligence in HealthcareGleecus Whitepaper : Applications of Artificial Intelligence in Healthcare
Gleecus Whitepaper : Applications of Artificial Intelligence in Healthcare
Suprit Patra451 vistas
Generative AI in Healthcare Market.pptx por GayatriGadhave1
Generative AI in Healthcare Market.pptxGenerative AI in Healthcare Market.pptx
Generative AI in Healthcare Market.pptx
GayatriGadhave183 vistas
Generative AI in Healthcare Market - Copy - Copy.pptx por GayatriGadhave1
Generative AI in Healthcare Market - Copy - Copy.pptxGenerative AI in Healthcare Market - Copy - Copy.pptx
Generative AI in Healthcare Market - Copy - Copy.pptx
GayatriGadhave1525 vistas
Impact of Artificial Intelligence in the Pharmaceutical World A Review por ijtsrd
Impact of Artificial Intelligence in the Pharmaceutical World A ReviewImpact of Artificial Intelligence in the Pharmaceutical World A Review
Impact of Artificial Intelligence in the Pharmaceutical World A Review
ijtsrd22 vistas
Ai in healthcare by nuaig.ai por Ruchi Jain
Ai in healthcare by nuaig.aiAi in healthcare by nuaig.ai
Ai in healthcare by nuaig.ai
Ruchi Jain287 vistas
Cloud based Health Prediction System por IRJET Journal
Cloud based Health Prediction SystemCloud based Health Prediction System
Cloud based Health Prediction System
IRJET Journal8 vistas
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using... por IRJET Journal
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...
IRJET Journal10 vistas
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN... por ijsc
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...
A REVIEW OF DATA INTELLIGENCE APPLICATIONS WITHIN HEALTHCARE SECTOR IN THE UN...
ijsc2 vistas

Más de IRJET Journal

SOIL STABILIZATION USING WASTE FIBER MATERIAL por
SOIL STABILIZATION USING WASTE FIBER MATERIALSOIL STABILIZATION USING WASTE FIBER MATERIAL
SOIL STABILIZATION USING WASTE FIBER MATERIALIRJET Journal
25 vistas7 diapositivas
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles... por
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...IRJET Journal
8 vistas7 diapositivas
Identification, Discrimination and Classification of Cotton Crop by Using Mul... por
Identification, Discrimination and Classification of Cotton Crop by Using Mul...Identification, Discrimination and Classification of Cotton Crop by Using Mul...
Identification, Discrimination and Classification of Cotton Crop by Using Mul...IRJET Journal
8 vistas5 diapositivas
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula... por
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...IRJET Journal
13 vistas11 diapositivas
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR... por
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...IRJET Journal
14 vistas6 diapositivas
Performance Analysis of Aerodynamic Design for Wind Turbine Blade por
Performance Analysis of Aerodynamic Design for Wind Turbine BladePerformance Analysis of Aerodynamic Design for Wind Turbine Blade
Performance Analysis of Aerodynamic Design for Wind Turbine BladeIRJET Journal
7 vistas5 diapositivas

Más de IRJET Journal(20)

SOIL STABILIZATION USING WASTE FIBER MATERIAL por IRJET Journal
SOIL STABILIZATION USING WASTE FIBER MATERIALSOIL STABILIZATION USING WASTE FIBER MATERIAL
SOIL STABILIZATION USING WASTE FIBER MATERIAL
IRJET Journal25 vistas
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles... por IRJET Journal
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...
Sol-gel auto-combustion produced gamma irradiated Ni1-xCdxFe2O4 nanoparticles...
IRJET Journal8 vistas
Identification, Discrimination and Classification of Cotton Crop by Using Mul... por IRJET Journal
Identification, Discrimination and Classification of Cotton Crop by Using Mul...Identification, Discrimination and Classification of Cotton Crop by Using Mul...
Identification, Discrimination and Classification of Cotton Crop by Using Mul...
IRJET Journal8 vistas
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula... por IRJET Journal
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...
“Analysis of GDP, Unemployment and Inflation rates using mathematical formula...
IRJET Journal13 vistas
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR... por IRJET Journal
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...
MAXIMUM POWER POINT TRACKING BASED PHOTO VOLTAIC SYSTEM FOR SMART GRID INTEGR...
IRJET Journal14 vistas
Performance Analysis of Aerodynamic Design for Wind Turbine Blade por IRJET Journal
Performance Analysis of Aerodynamic Design for Wind Turbine BladePerformance Analysis of Aerodynamic Design for Wind Turbine Blade
Performance Analysis of Aerodynamic Design for Wind Turbine Blade
IRJET Journal7 vistas
Heart Failure Prediction using Different Machine Learning Techniques por IRJET Journal
Heart Failure Prediction using Different Machine Learning TechniquesHeart Failure Prediction using Different Machine Learning Techniques
Heart Failure Prediction using Different Machine Learning Techniques
IRJET Journal7 vistas
Experimental Investigation of Solar Hot Case Based on Photovoltaic Panel por IRJET Journal
Experimental Investigation of Solar Hot Case Based on Photovoltaic PanelExperimental Investigation of Solar Hot Case Based on Photovoltaic Panel
Experimental Investigation of Solar Hot Case Based on Photovoltaic Panel
IRJET Journal3 vistas
Metro Development and Pedestrian Concerns por IRJET Journal
Metro Development and Pedestrian ConcernsMetro Development and Pedestrian Concerns
Metro Development and Pedestrian Concerns
IRJET Journal2 vistas
Mapping the Crashworthiness Domains: Investigations Based on Scientometric An... por IRJET Journal
Mapping the Crashworthiness Domains: Investigations Based on Scientometric An...Mapping the Crashworthiness Domains: Investigations Based on Scientometric An...
Mapping the Crashworthiness Domains: Investigations Based on Scientometric An...
IRJET Journal3 vistas
DESIGN AND SIMULATION OF SOLAR BASED FAST CHARGING STATION FOR ELECTRIC VEHIC... por IRJET Journal
DESIGN AND SIMULATION OF SOLAR BASED FAST CHARGING STATION FOR ELECTRIC VEHIC...DESIGN AND SIMULATION OF SOLAR BASED FAST CHARGING STATION FOR ELECTRIC VEHIC...
DESIGN AND SIMULATION OF SOLAR BASED FAST CHARGING STATION FOR ELECTRIC VEHIC...
IRJET Journal62 vistas
Efficient Design for Multi-story Building Using Pre-Fabricated Steel Structur... por IRJET Journal
Efficient Design for Multi-story Building Using Pre-Fabricated Steel Structur...Efficient Design for Multi-story Building Using Pre-Fabricated Steel Structur...
Efficient Design for Multi-story Building Using Pre-Fabricated Steel Structur...
IRJET Journal12 vistas
Development of Effective Tomato Package for Post-Harvest Preservation por IRJET Journal
Development of Effective Tomato Package for Post-Harvest PreservationDevelopment of Effective Tomato Package for Post-Harvest Preservation
Development of Effective Tomato Package for Post-Harvest Preservation
IRJET Journal4 vistas
“DYNAMIC ANALYSIS OF GRAVITY RETAINING WALL WITH SOIL STRUCTURE INTERACTION” por IRJET Journal
“DYNAMIC ANALYSIS OF GRAVITY RETAINING WALL WITH SOIL STRUCTURE INTERACTION”“DYNAMIC ANALYSIS OF GRAVITY RETAINING WALL WITH SOIL STRUCTURE INTERACTION”
“DYNAMIC ANALYSIS OF GRAVITY RETAINING WALL WITH SOIL STRUCTURE INTERACTION”
IRJET Journal5 vistas
Understanding the Nature of Consciousness with AI por IRJET Journal
Understanding the Nature of Consciousness with AIUnderstanding the Nature of Consciousness with AI
Understanding the Nature of Consciousness with AI
IRJET Journal12 vistas
Augmented Reality App for Location based Exploration at JNTUK Kakinada por IRJET Journal
Augmented Reality App for Location based Exploration at JNTUK KakinadaAugmented Reality App for Location based Exploration at JNTUK Kakinada
Augmented Reality App for Location based Exploration at JNTUK Kakinada
IRJET Journal6 vistas
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba... por IRJET Journal
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...
IRJET Journal18 vistas
Enhancing Real Time Communication and Efficiency With Websocket por IRJET Journal
Enhancing Real Time Communication and Efficiency With WebsocketEnhancing Real Time Communication and Efficiency With Websocket
Enhancing Real Time Communication and Efficiency With Websocket
IRJET Journal5 vistas
Textile Industrial Wastewater Treatability Studies by Soil Aquifer Treatment ... por IRJET Journal
Textile Industrial Wastewater Treatability Studies by Soil Aquifer Treatment ...Textile Industrial Wastewater Treatability Studies by Soil Aquifer Treatment ...
Textile Industrial Wastewater Treatability Studies by Soil Aquifer Treatment ...
IRJET Journal4 vistas
Text Summarization Using the T5 Transformer Model por IRJET Journal
Text Summarization Using the T5 Transformer ModelText Summarization Using the T5 Transformer Model
Text Summarization Using the T5 Transformer Model
IRJET Journal35 vistas

Último

unit 1.pptx por
unit 1.pptxunit 1.pptx
unit 1.pptxrrbornarecm
5 vistas53 diapositivas
CPM Schedule Float.pptx por
CPM Schedule Float.pptxCPM Schedule Float.pptx
CPM Schedule Float.pptxMathew Joseph
6 vistas5 diapositivas
Pitchbook Repowerlab.pdf por
Pitchbook Repowerlab.pdfPitchbook Repowerlab.pdf
Pitchbook Repowerlab.pdfVictoriaGaleano
6 vistas12 diapositivas
Robotics in construction enterprise por
Robotics in construction enterpriseRobotics in construction enterprise
Robotics in construction enterpriseKhalid Abdel Naser Abdel Rahim
5 vistas1 diapositiva
Ansari: Practical experiences with an LLM-based Islamic Assistant por
Ansari: Practical experiences with an LLM-based Islamic AssistantAnsari: Practical experiences with an LLM-based Islamic Assistant
Ansari: Practical experiences with an LLM-based Islamic AssistantM Waleed Kadous
9 vistas29 diapositivas
Renewal Projects in Seismic Construction por
Renewal Projects in Seismic ConstructionRenewal Projects in Seismic Construction
Renewal Projects in Seismic ConstructionEngineering & Seismic Construction
5 vistas8 diapositivas

Último(20)

Ansari: Practical experiences with an LLM-based Islamic Assistant por M Waleed Kadous
Ansari: Practical experiences with an LLM-based Islamic AssistantAnsari: Practical experiences with an LLM-based Islamic Assistant
Ansari: Practical experiences with an LLM-based Islamic Assistant
M Waleed Kadous9 vistas
MongoDB.pdf por ArthyR3
MongoDB.pdfMongoDB.pdf
MongoDB.pdf
ArthyR349 vistas
BCIC - Manufacturing Conclave - Technology-Driven Manufacturing for Growth por Innomantra
BCIC - Manufacturing Conclave -  Technology-Driven Manufacturing for GrowthBCIC - Manufacturing Conclave -  Technology-Driven Manufacturing for Growth
BCIC - Manufacturing Conclave - Technology-Driven Manufacturing for Growth
Innomantra 15 vistas
REACTJS.pdf por ArthyR3
REACTJS.pdfREACTJS.pdf
REACTJS.pdf
ArthyR337 vistas
Integrating Sustainable Development Goals (SDGs) in School Education por SheetalTank1
Integrating Sustainable Development Goals (SDGs) in School EducationIntegrating Sustainable Development Goals (SDGs) in School Education
Integrating Sustainable Development Goals (SDGs) in School Education
SheetalTank19 vistas
Proposal Presentation.pptx por keytonallamon
Proposal Presentation.pptxProposal Presentation.pptx
Proposal Presentation.pptx
keytonallamon67 vistas
Design of machine elements-UNIT 3.pptx por gopinathcreddy
Design of machine elements-UNIT 3.pptxDesign of machine elements-UNIT 3.pptx
Design of machine elements-UNIT 3.pptx
gopinathcreddy37 vistas
Design_Discover_Develop_Campaign.pptx por ShivanshSeth6
Design_Discover_Develop_Campaign.pptxDesign_Discover_Develop_Campaign.pptx
Design_Discover_Develop_Campaign.pptx
ShivanshSeth649 vistas
Créativité dans le design mécanique à l’aide de l’optimisation topologique por LIEGE CREATIVE
Créativité dans le design mécanique à l’aide de l’optimisation topologiqueCréativité dans le design mécanique à l’aide de l’optimisation topologique
Créativité dans le design mécanique à l’aide de l’optimisation topologique
LIEGE CREATIVE8 vistas
Web Dev Session 1.pptx por VedVekhande
Web Dev Session 1.pptxWeb Dev Session 1.pptx
Web Dev Session 1.pptx
VedVekhande17 vistas

Data Analytics and Artificial Intelligence in Healthcare Industry

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 930 Data Analytics and Artificial Intelligence in Healthcare Industry Venkata Preethi Mudili ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Artificial intelligence (AI) and data analysis are emerging as transformative tools across diverse fields, from economics to security. In healthcare, AI and data analysis have driven significant changes, leveraging learning algorithms for impactful outcomes. These technologies, evident in academic works and global implementations, hold immense promise. This paper reviews recent healthcare-focused AI publications, highlighting advancements and addressing challenges and ethical concerns faced by healthcare and governments. The paper aims to analyze current scientific trends, underscore AI's potential in healthcare, and offer solutions for present issues while envisioning future AI applications. Two case studies further illustrate practical AI and data analysis applications. Overall, AI and data analysis are reshaping healthcare and various sectors. The paper contributes a comprehensive overview of recent AI trends, recognizes their potential, and provides insights to overcome healthcare challenges while anticipating AI's future impacts. Key Words: Artificial intelligence, Data analysis, Scientific trends, Healthcare challenges 1.INTRODUCTION The rapid development of technology has led to the emergence of artificial intelligence (AI) and data analytics as forces of change, with far-reaching effects in many different fields, from economics to security [1]. In healthcare, these dynamic technologies have brought about significant change by harnessing the power of learning algorithms to achieve remarkable results. This change is underscored by their prominent presence in the academic literature and their widespread adoption worldwide, demonstrating their potential for disruptive advances[2]. Amid this landscape, this article presents a comprehensive survey of recent AI-focused healthcare publications. In doing so, it explains the advances made in healthcare through artificial intelligence and data analytics, and highlights advances that have changed traditional practices. In addition, the paper plays a central role in addressing the multifaceted challenges and ethical considerations that both health entities and governments grapple with as a result of such technological changes[3][4]. The general purpose of this work goes beyond retrospective analysis; it aims to identify current scientific trends that support the synergy between AI and healthcare, explore the hidden potential of AI in healthcare and provide practical solutions that can overcome existing challenges, looking ahead to the promising future of AI applications. To illustrate the practical implications of these concepts, the paper presents two moving case studies that illustrate the concrete benefits of AI and data analytics in real-world healthcare scenarios. Finally, this study highlights the central role of artificial intelligence and data analytics in reshaping healthcare paradigms and permeating innovation in various fields [5]. By providing an in-depth overview of the latest trends in AI, recognizing its enormous potential, and providing insight into how to address current health challenges, this book is a catalyst for transformative change in predicting the continued impact of AI for years to come. The intertwining of technology and healthcare continues to evolve at an astonishing pace, reshaping paradigms and paving the way for a future where artificial intelligence (AI) and data analytics stand as pivotal pillars of progress. As these dynamic technologies flourish, they engender a profound impact across diverse sectors, from economics to security, sparking a paradigm shift that transcends conventional boundaries [6]. Within the realm of healthcare, the fusion of AI and data analytics has sparked a revolution, propelling learning algorithms to new heights and ushering in a era of unprecedented achievements. This article embarks on a journey through the labyrinth of recent AI-focused healthcare publications, delving into the transformative leaps accomplished through these innovations. By dissecting and illuminating the strides made, this paper not only unveils the evolution of AI within healthcare but also unearths its latent potentials [7]. It serves as a compass navigating through the intricate challenges and ethical dilemmas faced by healthcare entities and governments in the wake of technological transformations. Fig -1: Understanding AI in Healthcare Business
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 931 Yet, the scope of this work reaches beyond retrospection. It assumes the role of a visionary, identifying contemporary scientific currents that amplify the symbiosis between AI and healthcare [8]. Moreover, it unearths the veiled capacities of AI, proffering pragmatic solutions to surmount existing obstacles, while casting an anticipatory gaze toward the bright horizon of AI applications. In the spirit of tangibility, the paper presents two poignant case studies that breathe life into the abstract potentialities of AI and data analytics. These narratives offer tangible evidence of the tangible benefits reaped from these technologies within the realm of real-world healthcare scenarios, affirming the substantial impact of AI on the ground [9]. 1.1 Advancements & Innovations in Healthcare AI Before The combination of artificial intelligence (AI) and data analytics has ushered in a new era of advancement and innovation in healthcare. These technologies have dramatically changed the way healthcare is practiced, resulting in improved patient outcomes and more efficient clinical processes [10]. AI-powered diagnostic tools have demonstrated exceptional accuracy in detecting various diseases such as cancer, heart disease and neurological disorders, often outperforming human experts. In addition, predictive analytics models use historical patient data to predict disease progression, enabling proactive interventions and personalized treatment plans. This accuracy and early detection could revolutionize disease management and improve patient care. Innovations also extend to robot-assisted surgery, where artificial intelligence algorithms help surgeons perform complex procedures with greater precision, minimizing invasiveness and recovery time. In addition, AI- based drug development accelerates the identification of potential compounds and streamlines the lengthy drug development process. The integration of artificial intelligence and health data has also facilitated the creation of patient profiles that help tailor treatment to individual needs, optimize regimens and minimize side effects. Thanks to the constant monitoring of vital signs and health trends, artificial intelligence allows healthcare workers to quickly make informed decisions. 1.2 Global Applications and Case Studies This section illuminates the concrete impact of AI and data analytics on healthcare through real case studies and global applications. We highlight cases where these technologies have made a difference and explore practical examples that demonstrate their potential [11]. These case studies highlight how AI-enhanced diagnostics, care personalization and predictive analytics have optimized patient outcomes and simplified healthcare processes. By studying successful applications around the world, we gain insight into the versatility and scalability of AI-based solutions in the healthcare ecosystem. Fig -2: US Market AI in Healthcare 2. GLOBAL IMPLEMENTATION AND CASE STUDIES The pervasive impact of artificial intelligence (AI) on healthcare has transcended geographic boundaries and ushered in a new era in medical practice and patient care. This section illuminates the diverse landscape of AI integration with compelling case studies that highlight successful applications in various global healthcare settings. The convergence of artificial intelligence and medical imaging at a major European medical center has redefined diagnostic accuracy. Sophisticated algorithms carefully analyze radiological images such as X-rays and MRIs, revealing complex abnormalities that may escape human control. This innovation speeds diagnosis facilitates early intervention and reduces the risk of misinterpretation. This case demonstrates the ability of artificial intelligence to complement human knowledge and ultimately improve patient outcomes. Pharmaceutical research in Asia has seen a paradigm shift with AI-based drug development efforts. As a result of the collaboration between scientific institutions and technological pioneers, artificial intelligence models have been obtained that can predict possible drug interactions and side effects. These speeds up the identification of promising compounds, which greatly reduces the trial- and-error cycle of drug development. This case highlights the potential of AI to improve drug innovation and potentially transform how new drugs are marketed. AI-powered telemedicine solutions have emerged as beacons of healthcare access in resource-rich regions across Africa [12]. Using AI-powered chatbots, patients can express symptoms and receive preliminary medical instructions even when a doctor is not immediately available. This initiative acts as a health channel in underserved areas by providing timely counseling and supporting remote diagnostics. This case exemplifies the role of artificial intelligence in democratizing healthcare and bridging geographic disparities.
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 932 These illuminating case studies highlight the transformative global impact of AI on healthcare systems. They serve as beacons of inspiration and show the possibilities of artificial intelligence to optimize diagnostics, accelerate drug discovery and overcome inequalities in healthcare. As these success stories spread around the world, they encourage the collective medical fraternity to embrace AI-based innovations that could transform the landscape of healthcare worldwide. 3. CHALLENGES AND ETHICAL CONSIDERATIONS The integration of artificial intelligence (AI) and data analytics into healthcare presents a series of challenges and ethical considerations that require careful attention. Concerns about the privacy and security of patient data are paramount, as the extensive collection and analysis of sensitive health data increases the risk of unauthorized access or data breaches. In addition, the potential for algorithmic bias from historical data can lead to differences in diagnosis and treatment, exacerbating existing health care disparities. Ethically, the use of artificial intelligence in clinical decision-making raises questions about accountability and transparency. The "black box" nature of some AI models creates challenges in understanding the reasons behind certain medical decisions, making it difficult for healthcare professionals to trust and validate the results. Striking a balance between automated insights and human knowledge is another challenging ethical aspect, as over-reliance on technology can undermine the patient-provider relationship and critical thinking. Regulatory frameworks to ensure the responsible development and deployment of AI in healthcare add complexity. Achieving consensus on data ownership, sharing and standards requires interdisciplinary collaboration. Addressing these challenges and ethical issues requires multifaceted strategies that include strong data governance, algorithmic transparency, continuous monitoring, and active collaboration with stakeholders to develop a healthy landscape that harnesses the potential of AI and adheres to ethical standards. Navigating the terrain where artificial intelligence (AI) and data analytics converge with healthcare unveils a tapestry of challenges and ethical dimensions that merit thoughtful consideration. The urgency of safeguarding patient privacy and data security looms large, as the amplification of sensitive health information's collection and scrutiny escalates the specter of unauthorized access and breaches. Moreover, the specter of algorithmic bias—emerging from historical data—casts a shadow on diagnoses and treatments, perpetuating disparities in healthcare. In the realm of ethics, the integration of AI into clinical decisions births a plethora of accountability and transparency quandaries. The opaqueness inherent in some AI models begets uncertainty about the rationale underlying medical verdicts, impeding healthcare practitioners from corroborating and confiding in these outputs. Striking equilibrium between automated insights and human expertise proves a tightrope walk, with excessive reliance on technology undermining the sacred patient-provider alliance and analytical acumen. The labyrinthine regulatory frameworks that ought to undergird the judicious development and deployment of AI in healthcare further compound the intricacies. Plying the waters of data ownership, sharing norms, and standardized practices mandates interdisciplinary collaborations. Tackling these multifarious challenges and ethical quandaries necessitates a symphony of strategies, including robust data governance, algorithmic lucidity, vigilant monitoring, and vibrant engagement with stakeholders. In harmonizing these elements, we shape a landscape that not only capitalizes on AI's potential but also upholds the venerable mantle of ethical standards. 4. CONCLUSIONS In conclusion, this paper explored the current progress of artificial intelligence (AI) in academia and industry, highlighting its diverse applications in healthcare. Beyond its potential, ethical concerns were examined, potentially impacting society's future. Two healthcare cases showcased AI's problem-solving prowess. As medical systems evolve, AI and analytics will play a pivotal role, improving outcomes and reducing errors. Ethical guidelines are crucial for safe AI application. AI is anticipated to enhance global healthcare, addressing challenges, and making diagnostics and treatment more precise and accessible. In the ever-evolving narrative of healthcare, this study serves as a compass, charting the course of artificial intelligence (AI) in academia and industry, illuminating its multifaceted healthcare applications. While potential abounds, ethical underpinnings cast a profound shadow, bearing the power to mold society's trajectory. Through the lens of two illustrative healthcare cases, AI's capacity to untangle complex challenges gleams. As healthcare ecosystems unfurl, the orchestration of AI and analytics assumes a linchpin role, reshaping outcomes and curbing fallibility. With ethical signposts as guiding lights, the voyage toward secure AI implementation unfurls. The saga of AI's ascent within global healthcare unfolds, poised to recalibrate diagnostics, therapeutics, and accessibility, heralding a future of refined care and resolute solutions. 5. FUTURE SCOPE In Artificial intelligence (AI), considered a key force in future healthcare, is poised to revolutionize personalized care, a major advance in the field. Despite initial challenges in providing diagnostic and treatment recommendations, AI is ruling the field. The increasing proficiency of artificial intelligence in image analysis indicates that computer
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 933 systems will eventually dominate the evaluation of most radiology and pathology images. Also, the speech and text recognition extension cover patient communication and clinical documentation tasks. However, the biggest challenge is not the capabilities of AI, but its seamless integration into standard healthcare practices. Regulatory validation, integration with electronic health records (EHR), compatibility with comparable products, effective training of medical professionals, securing funding and timely updates are prerequisites for widespread adoption. These obstacles will eventually be overcome but solving them will likely exceed the timeline of technological development. REFERENCES [1] Nasir Abdul Jalil and Mikkay Wong Ei Leen. Big Data in the Era of Pandemic COVID-19 : Application of IoT based data analytics, Machine Learning and Artificial Intelligence. 7. https://doi.org/10.1145/3524383.3524433 [2] Urvashi Gupta and Rohit Sharma. 2023. A Study of Cloud Based Solution for Data Analytics in Healthcare. https://doi.org/10.1109/iscon57294.2023.10112083 [3] Muhib Anwar Lambay and S. Pakkir Mohideen. 2020. Big Data Analytics for Healthcare Recommendation Systems. In (2020 International Conference on System, Computation, Automation and Networking (ICSCAN)), 1–6. https://doi.org/10.1109/ICSCAN49426.2020.926230 4 [4] Jieyuan Liu. 2020. Artificial Intelligence and Data Analytics Applications in Healthcare General Review and Case Studies. https://doi.org/10.1145/3433996.3434006 [5] Narcisa Roxana Moşteanu. Artificial Intelligence Helping the Fight against COVID-19. Supporting the Pharmaceutical Industry beyond the Financial Aid. https://doi.org/10.1145/3507485.3507495 [6] Wahyu Sardjono, Astari Retnowardhani, Robert Emil Kaburuan, and Aninda Rahmasari. 2021. Artificial intelligence and big data analysis implementation in electronic medical records. https://doi.org/10.1145/3512576.3512618 [7] Augustina O Ugwu, Xianghua Gao, Johnson O Ugwu, and Victor Chang. 2022. Ethical Implications of AI in Healthcare Data: A Case Study Using Healthcare Data Breaches from the US Department of Health and Human Services Breach Portal between 2009-2021. https://doi.org/10.1109/iiotbdsc57192.2022.00070 [8] Jiaxin Zhang. Artificial Intelligence and Machine Learning Algorithm Optimization Applied in Health Big Data Digitization. https://doi.org/10.1145/3495018.3501124 [9] Aisha Alobaidli, Al-Hareth Al-Khalifa, and Noura Al- Mutairi. 2021. Integrating Blockchain and Artificial Intelligence for Secure Healthcare Applications. In 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA), 298- 305. https://doi.org/10.1109/AICCSA51660.2021.959571 2 [10] Yuchen Li, Saeid Motiian, Siyuan Chen, and Jiawen Liu. 2019. Explainable Deep Learning in Healthcare: A Brief Survey. In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 1-4. https://doi.org/10.1109/BHI.2019.8834661 [11] Carolina Wählby, Charlotte Kibbel, and Mikael Lundqvist. 2022. Artificial Intelligence and Machine Learning in Medical Imaging—A New Era in Healthcare. Diagnostics, 12(1), 1-15. https://doi.org/10.3390/diagnostics12010008 [12] [12] Xing Wang, Zhihan Lv, Yanyu Xu, Ronglin Jiang, and Yunfei Chen. 2021. Artificial Intelligence in Healthcare: A Comprehensive Review. Artificial Intelligence Review, 54(5), 4073-4117. https://doi.org/10.1007/s10462-020-09919-2