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All Journal Journal Of World Science International Journal of Multidisciplinary Sciences and Arts Journal Research of Social Science, Economics, and Management
Husnain, Ali
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Computer Science & IT Economics, Econometrics & Finance Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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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.
Artificial Intelligence in Healthcare: Revealing Novel Approaches to Cancer Treatment, Fraud Investigation, and Petroleum Industry Perspectives Fahad, Muhammad; Ibrar, Muhammad; Qayyum, Muhammad Umer; Husnain, Ali
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.4637

Abstract

Artificial Intelligence (AI) is increasingly transforming healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiencies. This review explores AI's impact across several key areas: cancer medicine, fraud detection, and lessons from the petroleum industry. In cancer medicine, AI-driven advancements are leading to more accurate diagnostics, personalized treatment plans, and predictive models for patient outcomes. In fraud detection, AI techniques such as anomaly detection and natural language processing are effectively identifying and mitigating fraudulent activities, safeguarding financial and operational integrity. Insights from the petroleum industry reveal how AI applications, such as predictive maintenance and operational optimization, can be adapted to healthcare settings to enhance equipment reliability and resource management. Emerging trends include the integration of AI with genomics, telemedicine, and cross-disciplinary innovations, which promise further advancements in personalized care and operational efficiency. However, ethical considerations such as data privacy, bias, and transparency must be addressed to ensure responsible AI deployment. The review concludes by highlighting the need for continued innovation, collaboration, and patient-centric approaches to fully realize AI's potential in transforming healthcare and improving patient outcomes.
Healthcare Revolution: How AI and Machine Learning Are Changing Medicine Saeed, Ayesha; Husnain, Ali; Rasool, Saad; Yousaf Gill, Ahmad; Amelia, Amelia
Journal Research of Social Science, Economics, and Management Vol. 3 No. 3 (2023): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v3i3.558

Abstract

This essay examines the enormous effects of machine learning and artificial intelligence (AI) on healthcare. Through data analysis, AI is transforming disease detection and prediction and improving the precision of diagnoses. By accelerating medication discovery and improving individualized treatment programs, it is revolutionizing both treatment and drug development. AI is promoting customized medicine by using genetic information to customize therapies. Through automation and optimized resource allocation, it is streamlining hospital processes. The importance of ethical considerations is significant; they center on data privacy, bias reduction, and accountability. The study highlights potential avenues for AI development, such as AI-driven drug discovery, predictive and preventative healthcare, advances in genomic medicine, enhanced medical imaging, and more robotics and automation. Predictive analytics, telehealth, AI virtual assistants, and AI in mental healthcare are all expected to grow. These developments have the potential to improve health care, streamline processes, and boost scientific inquiry. To use AI in healthcare in a fair and ethical manner, however, and usher in a future that is more patient-centric, accurate, and accessible internationally, difficulties related to data quality, ethics, regulation, and prejudice must be addressed.
Co-Authors Amelia Amelia Fahad, Muhammad Ibrar, Muhammad Khawar Hussain, Hafiz Qayyum, Muhammad Umer Rasool, Saad Saeed, Ayesha Yousaf Gill, Ahmad