Artificial Intelligence and Machine Learning in HealthcareArman Kilic Artificial Intelligence and Machine Learning in Healthcare discusses the potential of groundbreaking technologies on the delivery of care. A lot have been said about how artificial intelligence and machine learning can improve healthcare, however there are still many doubts and concerns among health professionals, all of which are addressed in this book. Sections cover History and Basic Overview of AI and ML, with differentiation of supervised, unsupervised and deep learning, Applications of AI and ML in Healthcare, The Future of Healthcare with AI, Challenges to Adopting AI in Healthcare, and ethics and legal processes for implementation.This book is a valuable resource for bioinformaticians, clinicians, graduate students and several members of biomedical field who needs to get up to speed on the revolutionary role of AI and Machine Learning in healthcare. - Provides an overview of AI and ML to the medical practitioner who may not be well versed in these fields - Encompasses a thorough review of what has been accomplished and demonstrated recently in the fields of AI and ML in healthcare - Discusses the future of AI and ML in healthcare, with a review of possible wearable technology and software and how they may be used for medical care |
Contents
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Other editions - View all
Artificial Intelligence and Machine Learning in Healthcare Arman KIlic,Artur Dubrawski No preview available - 2022 |
Common terms and phrases
Accessed 6 September accuracy AI/ML solutions algorithms ambiguity set analysis applications approach artificial intelligence assessment Assoc automated brain cardiac cardiovascular challenges chatbots Chen claims data Clin clinicians codes critical data mining data-driven datasets decision-making deep learning demonstrated detection diagnosis disease Drug EHR data electronic health records evaluation example framework function healthcare hospital identify IEEE impact implementation improve integration Intelligence and Machine intervention machine learning machine learning models medical claims medical device medical education medical imaging medicine ML models monitoring multiple natural language processing operating opioid optimization outcomes pediatric performance physicians potential prediction primary prognostic Radiol radiologists radiology random forest real-time real-world regulatory require risk robotic scheduling score sepsis September 2025 specific standard support vector machine surgery surgical systematic review tasks techniques technologies treatment triage United utilization validation variability wearable devices wearable sensors workflow