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Herausgeber: 
  • Akash Kumar Bhoi
  • Victor Hugo C. de Albuquerque
  • P. Naga Srinivasu
  • Principles and Methods of Explainable Artificial Intelligence in Healthcare 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

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    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Mai 2022  
    Genre:  Naturwissensch., Medizin, Technik 
    ISBN:  9781668437919 
    EAN-Code: 
    9781668437919 
    Verlag:  Medical Information Science Reference 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 260 mm / B 183 mm / D 25 mm 
    Gewicht:  901 gr 
    Seiten:  376 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Explainable artificial intelligence is proficient in operating and analyzing the unconstrainted environment in fields like robotic medicine, robotic treatment, and robotic surgery, which rely on computational vision for analyzing complex situations. Explainable artificial intelligence is a well-structured customizable technology that makes it possible to generate promising unbiased outcomes. The model's adaptability facilitates the management of heterogeneous healthcare data and the visualization of biological structures through virtual reality. Explainable artificial intelligence has newfound applications in the healthcare industry, such as clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. Principles and Methods of Explainable Artificial Intelligence in Healthcare discusses explainable artificial intelligence and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. The book also encompasses computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. Covering topics such as neural networks and disease detection, this reference work is ideal for industry professionals, practitioners, academicians, researchers, scholars, instructors, and students.

      



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