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.
Haipeng Liu is currently a Research Fellow with the Research Centre for Intelligent Healthcare, Coventry University. He received his 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. His research interests include wearable sensing, physiological measurement, AI-assisted diagnostics, and computational simulation of cardiovascular diseases. He is the author of over 60 journal articles and 11 conference papers, and is open for collaboration in healthcare technologies and medical data analytics. Rajesh Kumar Tripathy received the B.Tech degree in Electronics and Telecommunication Engineering from the Biju Patnaik University of Technology (BPUT), Odisha, India, in 2009, and the M.Tech degree in Biomedical Engineering from the National Institute of Technology (NIT) Rourkela, Rourkela, India, in 2013, and the PhD degree in Electronics and Electrical Engineering (EEE) from the Indian Institute of Technology (IIT) Guwahati, Guwahati, India in 2017. Currently, he is working as an Assistant Professor in the department of Electrical and Electronics Engineering (EEE), Birla Institute of Technology and Science (BITS), Pilani, Hyderabad Campus. His research interests are Biomedical Signal Processing, Sensor Data Processing, Machine Learning and Medical Image Processing. He has published research papers in reputed international journals and conferences. 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 150 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 33 and an i10-index of 74. He has edited four books and is currently editing eight 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.
