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Khawar Hussain, Hafiz
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Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Role of Artificial Intelligence in Cardiovascular Health Care Khawar Hussain, Hafiz; Tariq, Aftab; Yousaf Gill, Ahmad
Journal of World Science Vol. 2 No. 4 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i4.284

Abstract

In the field of cardiovascular health, machine learning and artificial intelligence (AI) have become effective tools with potential applications ranging from disease detection and diagnosis to individualized treatment planning and decision making. The purpose of this study is to identify and analyze the role of AI in cardiovascular health care. The methodology of this review paper involved an extensive literature review of the existing research on the topic of AI in cardiovascular health care. Medical imaging is very important in the diagnosis and treatment of many diseases, but the interpretation of medical images is often time-consuming and subjective. Artificial intelligence (AI) algorithms, such as supervised and unsupervised learning, have been developed to assist in the analysis and interpretation of data from medical imaging. Convolutional neural networks (CNNs) and support vector machines (SVM) are the two most frequently used AI algorithms in medical image analysis. Artificial intelligence (AI) and machine learning in cardiovascular healthcare have great potential to improve patient outcomes and lower costs. However, there are still some hurdles that need to be overcome such as integration with clinical workflows, model validation and generalization, and privacy and security issues related to patient data. To overcome this, collaboration between doctors, researchers and industrial partners is needed. This technology has a bright and promising future with continuous investment in research and development.
AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare Husnain, Ali; Rasool, Saad; Saeed, Ayesha; Yousaf Gill, Ahmad; Khawar Hussain, Hafiz
Journal of World Science Vol. 2 No. 10 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i10.448

Abstract

In the realm of healthcare, artificial intelligence (AI) emerges as a transformative force, reshaping established practices and offering unprecedented advancements. This comprehensive analysis delves into the multifaceted ways AI is revolutionizing healthcare, focusing on its transformative capabilities, inherent challenges, and the crucial ethical complexities entwined in its application. The challenge lies in balancing transparency and accountability amid the intricate algorithms, particularly concerning the interpretability of AI-generated insights. The analysis explores ethical dilemmas tied to patient autonomy and the evolving responsibilities of healthcare providers. It advocates for open dialogue among AI systems, patients, and healthcare professionals, navigating the delicate balance between innovation and patient welfare. The article emphasizes the imperative for robust ethical frameworks and regulations governing AI implementation in healthcare. The comprehensive investigation concludes by exploring AI's potential applications in healthcare, envisioning improved medical procedures, drug discoveries, remote patient monitoring, and diagnostic enhancements. To harness AI's transformative power while safeguarding patient interests, collaboration between healthcare professionals, data scientists, policymakers, and ethicists is paramount. This abstract encapsulates the profound shifts AI has initiated in healthcare, underscoring the vital need to harness its potential while addressing the ethical and regulatory complexities arising with its integration. Ultimately, it portrays a holistic view of AI's evolving role in healthcare, highlighting its potential to revolutionize patient care, medical practices, and the entire healthcare landscape.
Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses - a Comprehensive Review of AI's Impact on Medical Diagnosis Yousaf Gill, Ahmad; Saeed, Ayesha; Rasool, Saad; Husnain, Ali; Khawar Hussain, Hafiz
Journal of World Science Vol. 2 No. 10 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i10.449

Abstract

The integration of machine learning into healthcare heralds a new era where the convergence of technology and human compassion reshapes the very essence of healing. This monumental shift transcends mere technological advancement; it represents a profound evolution in patient care. By unraveling intricate patterns within medical data, machine learning empowers healthcare professionals with early disease detection and precise risk assessment, augmenting human intuition rather than replacing it. This synergy between AI-driven insights and human expertise has led to remarkable achievements, from redefining radiological interpretations to foreseeing infectious disease outbreaks, painting a future where healthcare is not only precise but profoundly patient-centered. Yet, amidst these groundbreaking advancements, ethical considerations stand as pillars guiding responsible innovation. Upholding patient autonomy, ensuring data privacy, and addressing algorithmic bias are essential to maintain trust and integrity. As we navigate this transformative path, the promise of a healthcare landscape where healing becomes a symphony of technology and tradition becomes evident. It is a future where the well-being and hopes of millions are at the core, promising a brighter, more compassionate tomorrow for healthcare, where every diagnosis, treatment, and act of care resonates with the harmony of human expertise and technological marvels.
Co-Authors Husnain, Ali Rasool, Saad Saeed, Ayesha Tariq, Aftab Yousaf Gill, Ahmad