Healthcare 5.0 represents a paradigm-shifting period in healthcare, defined by the amalgamation of cutting-edge technologies like Artificial Intelligence (AI), Big-Data, Brain Computer Interface, Experience Reality (XR), Internet of Things (IoT), and Robotics to fabricate intelligent, adaptable, patient-focused and reliable healthcare systems. This book investigates the ideas of AI-driven workspaces in the context of sustainable telemedicine, that is, healthcare that delivers high quality care services by using advanced technologies in an affordable way, while minimising the impact on the environment. It thoroughly explores AI aligned areas to provide tangible healthcare solutions with the incorporation of proposed ideas, possible results based on designed & developed frameworks or applications, and suitable case studies. It is intended to provide innovators, IT specialists, and healthcare professionals with useful insights and pointers for implementing or improving AI-driven telemedicine solutions in a sustainable manner.

Investigates the ideas of AI-driven workspaces in the context of sustainable telemedicine Explores AI aligned areas to provide tangible healthcare solutions Provides insight about upcoming advancements and moral dilemmas in sustainable telemedicine

Autorentext

Dalip is working as an Associate Professor in M.M. Institute of Computer Technology & Business Management, M.M. (Deemed to be University). His Ph.D is in Information Technology from the M.M (Deemed to be University). He is gold medallist in M.Tech Information Technology from M.M University, and received Master of Computer Application from Kurukshetra University. He has published more than 35+ research papers in reputed International Journals/Conferences, and his interest & expertise is in fields like: GPS based Navigational System, Hydroponics Technology, Artificial Intelligence, Security, Cloud Computing, Technology Trends, Intelligent System, Communication and Mobile Adhoc Networks.

Dr Urvashi is working as an Assistant Professor in Dr. B.R. Ambedkar National Institute of Technology Jalandhar. She has received her B.Tech degree in CSE from the Kurukshetra University and she completed M.Tech in Computer Science and Engineering from MNIT Jaipur. Her PhD in Computer Science and Engineering from NIT Jalandhar. She has published more than 25+ research papers in reputed International Journals/Conferences, and his interest & expertise is in fields/topics like: Information Security, Cyber Warfare, Vulnerability Analysis and Management, Ransomware attacks, detection and mitigation, Bio-metric Authentication.

Arvind Sharma is an alumna of Guru Nanak Dev University India. He is an academician having 8+ years of experience, and currently working in the capacity of Assistant Professor in Shoolini University. Earlier he worked for iHub and HCI Foundation, Indian Institute of Technology Mandi; M.M. (deemed to be) University; Trinity College; in India. He had Ph.D. in Computer Science and Engineering from Rayat Bhara University, India; he received M.Tech in Computer Science and Engineering from Lovely Professional University, India along with another masters M.Sc. in Computer Science from Guru Nanak Dev University, India. His research interests span in Cryptography, Computer Networks, Cloud Computing, Internet of Things. With over 35+ research papers, his scholarly work, journal articles, conference papers, is widely recognized in international/national publications.

Samiya Khan, a University of Delhi alumna, holds a PhD in Computer Science from Jamia Millia Islamia, India. She is currently a faculty member in University of Southampton Delhi campus, and previously worked as Lecturer at the University of Greenwich, United Kingdom. Prior to this, she worked on an ERDF Project as Postdoctoral Research Fellow at the University of Wolverhampton, United Kingdom. With over 30+ research papers, her scholarly work, encompassing journal articles, conference papers and book chapters, is widely recognized in high-impact, international publications. Samiya's recent research endeavours focus on the application of data science in digital health.

Monika Sharma is alumna of Mewar University, Rajasthan, India having postgraduate in M.Sc. Medical Lab Technology (MLT). She pursued her B.Sc. MLT from NIMS University, Rajasthan, India. She had 8+ years of experience in medical stream and currently working in Mobile Medical Unit (National Health Mission), Office of Chief Medical Officer Solan, District Solan, Himachal Pradesh, India. During her tenure as medical laboratory technician, she had fulfilled number of responsibilities some of which concerned to a clinical laboratory tests and procedures to determine the health of human tissues, blood samples and other related to administration. Calibrating equipment, developing standard operating procedures and determining correct reference ranges for tests are other task which she also look after.

Inhalt

The role of artificial intelligence in chronic disease management through telemedicine and future directions.- The integration of ai and telemedicine in chronic illness care current trends and future insights.- Transforming teletherapy using transfer learning and nlp for improved mental health care.- Childhood ptsd and trauma treatment using nlp v/k dissociation, and machine learning.- Robotics, automation and telemedicine in healthcare 5.0 diagnostic and treatment processes.- The role of artificial intelligence in legal decision-making.- The role of artificial intelligence machine learning in managing chronic and mental health conditions through telemedicine during covid-19.-Role of artificial intelligence in the healthcare industry.-Healthcare 5.0 transforming telemedicine workflow through innovation.-Intelligent applications and its impactin improving brain-health.- Smart healthcare systems enhancing patient care through ai and big data.-Hair disease detection system using machine learning.-Blockchain associated machine learning approach for earlier prognosis and preclusion of osteoporosis in elderly.-Ga vs pso effectiveness in feature selection for heart disease prediction.