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  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Machine Learning in Healthcare: Application and Challenges

Aaruksha Dahiya

Aristotle Public Sr.Sec School, Bus Stand, Qutabgarh, Delhi, 110039

9 - 14 Vol. 9, Jan-Dec, 2023
Receiving Date: 2022-11-08;    Acceptance Date: 2023-01-05;    Publication Date: 2023-01-19
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Abstract

Coordinating AI (ML) techniques in medical services has arisen as an unexpected strength, upsetting different parts of patient consideration, ailing the executives, and medical services activities. This research paper investigates the complex applications and the difficulties of using ML in medical services. AI finds broad application in medical services, enveloping early disease identification, customized therapy plans, drug detection, clinical image examination, and patient risk separation. It is essential in clinical decision help, upgrading analytic precision and treatment adequacy. Besides, ML-based telemedicine and remote observing arrangements have extended medical services availability, especially in remote or underserved regions. Even with its exceptional potential, testing ML in medical services. Information protection and security concerns are central as delicate patient data is handled. Information quality, interoperability issues, and moral contemplations connected with algorithm inclination and direct request watchful consideration. Management obstacles and protection from change among medical services experts add intricacy to the mixed interaction. Moral contemplations arise unmistakably as medical service suppliers progressively depend on ML-driven experiences. This paper talks about the ethical aspects encompassing patient information protection, informed consent, and the requirement for transparent and neutral algorithm. kopi ure 2023 rolex replica top replica watches UK are in stock. You can possess best fake watches with less money.
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Keywords: AI Techniques; medical services; Machine Learning

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