As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare.

The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.

This book targets a diverse audience including data scientists, software engineers, clinicians, researchers, academics, and students. By fostering collaboration and knowledge exchange, the book emerges as a driving force for collective progress in the integration of artificial intelligence and new technologies into healthcare. Policymakers, too, stand to gain valuable insights, enabling them to update technical standards, clinical guidelines, and regulations, thereby optimizing the distribution of medical resources in the realm of data-driven healthcare. From quantum-enhanced machine learning to ethical considerations in AI-enhanced data-driven healthcare systems, the book stands as a rallying call for the healthcare community to overcome challenges, propelling us towards a future where artificial intelligence integrates with advanced technologies to redefine healthcare paradigms.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Cloud Computing in Signal Processing of Wearable Sensors
Computational Fluid Dynamics Simulation in Cardiovascular Disease
COVID-19 Diagnosis and Severity Prediction with Artificial Intelligence
Deep Learning in Neuroimaging
Machine Learning and Deep Learning in Risk Prediction of Major Clinical Events
Machine Learning in Photoplethysmography Signal Quality Assessment for Wearable Sensors
Multidomain Bio-Signal Processing
Multimodal Data Fusion in Data-Driven Healthcare Systems
Population Screening of Cardiovascular Risks
Quantum-Enhanced Machine Learning in Diagnostics
Real-Time Signal Processing in the Internet of Medical Things (IoMT)
Regulatory and Ethical Considerations

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Editor/Author Biographies

Haipeng Liu received his Bachelor and Master in Engineering degrees from Zhejiang University, China, in 2012 and 2015, respectively, and Doctor of Philosophy in Medical Sciences from the Chinese University of Hong Kong, in 2018. From 2019 to 2020, he was a research fellow with the Medical Technology Research Center, Anglia Ruskin University. Since 2020, he has been a research fellow with Coventry University, UK. He is the author of over 60 journal articles and 10 conference papers. His research interests include biomechanics, physiological measurement, and computational simulation of cardiovascular diseases.

Dr. Pronaya Bhattacharya received the Ph.D. degree from Dr. A. P. J Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India. He is currently an Associate Professor with the Computer Science and Engineering Department, Amity School of Engineering and Technology, Amity University, Kolkata, India. He has over ten years of teaching experience. He has authored or coauthored more than 130 research papers in leading SCI journals and top core IEEE COMSOC A* conferences. Some of his top-notch findings are published in reputed SCI journals, such as IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Computational Social Systems, IEEE Transactions of Network and Service Management, IEEE Access, IEEE Sensors Journal, IEEE Internet of Things Magazine, IEEE Communication Standards Magazine, ETT (Wiley), Expert Systems (Wiley), CCPE (Wiley), FGCS (Elsevier), OQEL (Springer), WPC (Springer), ACM-MOBICOM, IEEE-INFOCOM, IEEE-ICC, IEEE-CITS, IEEE-ICIEM, IEEE-CCCI, and IEEE-ECAI. He has an H-index of 30 and an i10-index of 67. He has edited two books and is currently editing six books from famed publishers like IGI Global, Elsevier, and Springer. His research interests include healthcare analytics, optical switching and networking, federated learning, blockchain, and the IoT. He is listed as Top 2% scientists as per list published by Stanford University. He has been appointed at the capacity of a keynote speaker, a technical committee member, and the session chair across the globe. He was awarded Eight Best Paper Awards in Springer ICRIC-2019, IEEE-ICIEM-2021, IEEE-ECAI-2021, Springer COMS2-2021, and IEEE-ICIEM-2022. He is a Reviewer of 21 reputed SCI journals, such as IEEE Internet of Things Journal, IEEE Transactions on Industrial Informatics, IEEE Transactions of Vehicular Technology, IEEE Journal of Biomedical and Health Informatics, IEEE Access, IEEE Network magazine, ETT (Wiley), IJCS (Wiley), MTAP (Springer), OSN (Elsevier), WPC (Springer), and others.

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