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AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems

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Jeff Shuford at Nationally Syndicated Columnist [American City Business Journals - 43 regional business newspapers]
Jeff Shuford
  • Nationally Syndicated Columnist [American City Business Journals - 43 regional business newspapers]
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Abstract

This article explores the with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by iden outcomes and cost treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and m enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real based on patient making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming pa .
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ARTICLEINFO
Article History:
Received:
05.01.2024
Accepted:
10.01.2024
Online: 22.01.2024
Keywords
Artificial Intelligence
(AI),Healthcare,Patient
Care
This article explores the
with a specific focus on how predictive analytics and decision support systems are
revolutionizing patient care. Predictive analytics enable early disease prevention and
diagnosis by iden
outcomes and cost
treatment plans, leveraging individual patient data for tailored interventions that
enhance efficacy and m
enhance diagnostic accuracy, providing rapid and precise assessments. Decision support
systems, powered by AI, streamline healthcare workflows by offering real
based on patient
making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare
interventions by tracking vital signs and identifying potential health issues in real time.
The article
also discusses challenges and ethical considerations associated with AI
integration in healthcare, emphasizing the importance of responsible deployment and
regulatory frameworks. The comprehensive exploration underscores how AI is not only
transforming pa
.
AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems
1
Department of Computer
2
Nationally Syndicated Business & Technology Columnist,USA
*Corresponding Author: Md. Shohel Rana
Introduction:
Artificial Intelligence (AI) has emerged as a revolutionary force in healthcare, offering transformative solutions to
enhance patient care, streamline processes, and
pivotal role of AI in healthcare, with a specific focus on how predictive analytics and decision support systems are
reshaping patient care delivery.[6]
Literature Review:
AI in healthcar
e is transforming patient care through predictive analytics and decision support systems. AI
techniques, such as machine learning and deep learning, are being used to analyze structured and
unstructured healthcare data, including electronic medical records
can identify patterns and trends in patient data that may not be immediately apparent to humans, enabling
earlier diagnosis, treatment, and prognosis evaluation
being used to automate routine tasks and provide personalized health advice, improving accessibility and
Journal of A
https://ojs.boulibrary.com/index.php/JAIGS
ABSTRACT
This article explores the
transformative impact of Artificial Intelligence (AI) in healthcare,
with a specific focus on how predictive analytics and decision support systems are
revolutionizing patient care. Predictive analytics enable early disease prevention and
diagnosis by iden
tifying patterns and risk factors, contributing to improved patient
outcomes and cost
-
effective healthcare. Machine learning facilitates personalized
treatment plans, leveraging individual patient data for tailored interventions that
enhance efficacy and m
inimize adverse effects. AI-
driven algorithms in medical imaging
enhance diagnostic accuracy, providing rapid and precise assessments. Decision support
systems, powered by AI, streamline healthcare workflows by offering real
based on patient
data and clinical guidelines, facilitating evidence
making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare
interventions by tracking vital signs and identifying potential health issues in real time.
also discusses challenges and ethical considerations associated with AI
integration in healthcare, emphasizing the importance of responsible deployment and
regulatory frameworks. The comprehensive exploration underscores how AI is not only
transforming pa
tient care but also shaping the future of healthcare delivery.
AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems
Md. Shohel Rana,1 Jeff Shuford 2
Department of Computer
Science,Dhaka University-
Bangladesh
Nationally Syndicated Business & Technology Columnist,USA
em ail
: sohelrana@gmail.com
Artificial Intelligence (AI) has emerged as a revolutionary force in healthcare, offering transformative solutions to
enhance patient care, streamline processes, and
improve overall healthcare outcomes
.[7]
pivotal role of AI in healthcare, with a specific focus on how predictive analytics and decision support systems are
e is transforming patient care through predictive analytics and decision support systems. AI
techniques, such as machine learning and deep learning, are being used to analyze structured and
unstructured healthcare data, including electronic medical records
and medical images
can identify patterns and trends in patient data that may not be immediately apparent to humans, enabling
earlier diagnosis, treatment, and prognosis evaluation
[3]. AI-
powered chatbots and virtual assistants a
being used to automate routine tasks and provide personalized health advice, improving accessibility and
Vol.1,Issue1,January 2024
Journal of A
rtificial Intelligence General Science (
JAIGS
ISSN:3006-4023
https://ojs.boulibrary.com/index.php/JAIGS
transformative impact of Artificial Intelligence (AI) in healthcare,
with a specific focus on how predictive analytics and decision support systems are
revolutionizing patient care. Predictive analytics enable early disease prevention and
tifying patterns and risk factors, contributing to improved patient
effective healthcare. Machine learning facilitates personalized
treatment plans, leveraging individual patient data for tailored interventions that
driven algorithms in medical imaging
enhance diagnostic accuracy, providing rapid and precise assessments. Decision support
systems, powered by AI, streamline healthcare workflows by offering real
-
time insights
data and clinical guidelines, facilitating evidence
-based decision-
making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare
interventions by tracking vital signs and identifying potential health issues in real time.
also discusses challenges and ethical considerations associated with AI
integration in healthcare, emphasizing the importance of responsible deployment and
regulatory frameworks. The comprehensive exploration underscores how AI is not only
tient care but also shaping the future of healthcare delivery.
AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems
Bangladesh
Nationally Syndicated Business & Technology Columnist,USA
Artificial Intelligence (AI) has emerged as a revolutionary force in healthcare, offering transformative solutions to
.[7]
This article delves into the
pivotal role of AI in healthcare, with a specific focus on how predictive analytics and decision support systems are
e is transforming patient care through predictive analytics and decision support systems. AI
techniques, such as machine learning and deep learning, are being used to analyze structured and
and medical images
[1] [2]. These techniques
can identify patterns and trends in patient data that may not be immediately apparent to humans, enabling
powered chatbots and virtual assistants a
re also
being used to automate routine tasks and provide personalized health advice, improving accessibility and
JAIGS
)
patient engagement [4]. Additionally, AI and machine learning algorithms are optimizing hospital operations,
streamlining administrative tasks, and enhancing resource allocation [5]. However, challenges such as data
privacy, algorithmic biases, and the potential for AI to replace human judgment need to be addressed to
ensure the safe and ethical use of AI in healthcare.
1. Predictive Analytics in Disease Prevention and Early Diagnosis:
AI-driven predictive analytics play a crucial role in disease prevention and early diagnosis. By analyzing vast
datasets, AI algorithms can identify patterns and risk factors, enabling healthcare professionals to predict the
likelihood of diseases such as diabetes, cardiovascular conditions, and certain cancers. Early detection not only
improves treatment outcomes but also reduces the overall cost of healthcare by minimizing the need for extensive
and expensive interventions.
2. Personalized Treatment Plans with Machine Learning:
AI-driven machine learning models are revolutionizing treatment plans by providing personalized and targeted
approaches. These models consider individual patient data, including genetics, medical history, and lifestyle factors,
to recommend tailored treatment options. This personalized medicine approach enhances treatment efficacy,
reduces adverse effects, and improves patient adherence to prescribed therapies.
3. Enhancing Diagnostic Accuracy with Imaging AI:
In medical imaging, AI algorithms are enhancing diagnostic accuracy and efficiency. Machine learning models
trained on vast datasets can analyze medical images, such as X-rays, MRIs, and CT scans, to detect anomalies and
provide rapid, accurate diagnoses. This not only expedites the diagnostic process but also supports healthcare
professionals in making more informed decisions about patient care.
4. Streamlining Workflows with Decision Support Systems:
Decision support systems powered by AI are streamlining healthcare workflows by providing real-time insights
and recommendations to healthcare professionals. These systems analyze patient data, clinical guidelines, and
relevant research to assist in diagnosis and treatment planning. This support aids healthcare providers in making
evidence-based decisions, ultimately improving the quality of care delivered.
5. Remote Patient Monitoring and Proactive Healthcare:
AI facilitates remote patient monitoring, allowing healthcare providers to track patients' vital signs and health
metrics in real-time. Predictive analytics enable the identification of potential health issues before they escalate,
allowing for proactive interventions. This not only enhances patient safety but also reduces hospital readmissions
and healthcare costs.
6. Challenges and Ethical Considerations:
Despite the promising advancements, the integration of AI in healthcare comes with challenges and ethical
considerations. Issues such as data privacy, algorithm bias, and the need for regulatory frameworks must be
addressed to ensure responsible and equitable AI deployment. Striking a balance between innovation and ethical
considerations is crucial for building trust in AI-driven healthcare solutions.
Results and Discussion:
Predictive Analytics in Disease Prevention and Early Diagnosis:
Result: AI-driven predictive analytics have proven effective in identifying patterns and risk factors, enabling the
prediction of diseases such as diabetes, cardiovascular conditions, and certain cancers.
Discussion: The use of predictive analytics enhances disease prevention and early diagnosis, allowing healthcare
professionals to intervene proactively. Early detection not only improves patient outcomes but also contributes to
the cost-effectiveness of healthcare by reducing the need for extensive and costly interventions.
2. Personalized Treatment Plans with Machine Learning:
Result: AI-driven machine learning models provide personalized and targeted treatment plans by considering
individual patient data, including genetics, medical history, and lifestyle factors.
Discussion: The application of machine learning in treatment planning marks a paradigm shift towards personalized
medicine. Tailored treatment options based on individual characteristics improve treatment efficacy, minimize
adverse effects, and increase patient adherence, ultimately leading to better overall healthcare outcomes.
3. Enhancing Diagnostic Accuracy with Imaging AI:
Result: AI algorithms in medical imaging enhance diagnostic accuracy by analyzing X-rays, MRIs, and CT scans to
detect anomalies and provide rapid, accurate diagnoses.
Discussion: The integration of AI in medical imaging significantly improves the efficiency of diagnosis. Rapid and
accurate assessments enable healthcare professionals to make timely decisions, leading to enhanced patient care
and improved overall diagnostic accuracy.
4. Streamlining Workflows with Decision Support Systems:
Result: Decision support systems powered by AI streamline healthcare workflows by providing real-time insights
and recommendations to healthcare professionals based on patient data and clinical guidelines.
Discussion: The implementation of decision support systems in healthcare enhances the decision-making process.
Real-time insights contribute to evidence-based decision-making, supporting healthcare providers in delivering
high-quality care with improved efficiency.
5. Remote Patient Monitoring and Proactive Healthcare:
Result: AI facilitates remote patient monitoring, allowing for real-time tracking of vital signs and health metrics to
identify potential health issues before they escalate.
Discussion: The use of AI in remote patient monitoring transforms healthcare from reactive to proactive. Early
identification of potential health issues enables timely interventions, improving patient safety, reducing hospital
readmissions, and ultimately contributing to a more cost-effective healthcare system.
6. Challenges and Ethical Considerations:
Result: The integration of AI in healthcare brings challenges such as data privacy, algorithm bias, and the need for
regulatory frameworks.
Discussion: Ethical considerations are paramount in the deployment of AI in healthcare. Addressing issues like data
privacy and algorithmic bias is essential to build trust and ensure responsible and equitable use of AI-driven
solutions. Regulatory frameworks must be established to guide the ethical deployment of AI in healthcare settings.
Conclusion:
AI is undeniably transforming patient care through predictive analytics and decision support systems. From early
disease detection to personalized treatment plans and streamlined workflows, AI is revolutionizing healthcare
delivery. As the field continues to evolve, addressing challenges and ethical considerations will be essential to
harness the full potential of AI in improving patient outcomes, enhancing the efficiency of healthcare systems, and
shaping the future of healthcare delivery.
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Cargo Operations of Express Air
Article
Full-text available
  • Sep 2023
View
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Data connectivity in flights using visible light communication
  • Jan 2017
  • 71-74
  • A Singla
  • D Sharma
  • S Vashisth
Singla, A., Sharma, D., & Vashisth, S. (2017). Data connectivity in flights using visible light communication. In *2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)* (pp. 71-74). Gurgaon, India. https://doi.org/10.1109/IC3TSN.2017.82844537
Blended services & enabling seamless lifestyle
  • Jan 2007
  • 1-5
  • N Sullhan
  • T Singh
Sullhan, N., & Singh, T. (2007). Blended services & enabling seamless lifestyle. In *2007 International Conference on IP Multimedia Subsystem Architecture and Applications* (pp. 1-5). Bangalore, India. https://doi.org/10.1109/IMSAA.2007.45590859
Creating panoramic images using ORB feature detection and RANSAC-based image alignment
  • Jan 2023
  • 220-224
  • K Wu
Wu, K. (2023). Creating panoramic images using ORB feature detection and RANSAC-based image alignment. *Advances in Computer and Communication, 4*(4), 220-224. https://doi.org/10.26855/acc.2023.08.00212
  • Jan 2023
  • S Liu
  • K Wu
  • C X Jiang
  • B Huang
  • D Ma
Liu, S., Wu, K., Jiang, C. X., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: towards synergizing performance and interpretability within a hybrid machine learning approach. *arXiv (Cornell University)*. https://doi.org/10.48550/arxiv.2401.00534