AI-Driven Robotics in Healthcare

• Utgivningsdatum: 2027-06-01
• Mått: 191 x 235 x undefined mm
• Format: Häftad
• Språk: Engelska
• Serie: Medical Robotics and Computer Assisted Surgery: AI-enhanced, Data-driven, and Evidence-based Approaches
• Antal sidor: 300
• Förlag: Elsevier Science
• ISBN: 9780443516894

• Biomedicinsk teknik
• Biokemisk teknik
• Artificiell intelligens
• Systemvetenskap och AI

Dr. Rajiv Pandey is a Faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India. He possesses a diverse background experience of around 35 years to include 15 years in industry and 20 years of academic research and instruction. His research interests include blockchain and crypto currencies, information security, semantic web provenance, Cloud computing, Big Data, and Data Analytics. Dr. Pandey is a Senior Member of IEEE and has been a session chair and technical committee member for various IEEE conferences. He has been on the technical committees of various government and private universities, and is the editor of Quantum Computing: A Shift from Bits to Qubits from Springer, Data Modelling and Analytics for the Internet of Medical Things from CRC Press/Taylor & Francis, and Artificial Intelligence and Machine Learning for Edge Computing from AP/Elsevier.Dr. Pratibha Mehra is an Assistant Professor and clinical researcher specializing in Ayurvedic medicine and botanical therapeutics. She holds a Bachelor of Ayurvedic Medicine and Surgery (BAMS) and an MS in Ayurveda (Prasuti Tantra avum Stri Roga) from Rajiv Gandhi Govt. Post Graduate Ayurvedic College. Furthermore, the book is also intended for learners with a professional and academic interest in cognitive health preventive nutrition and natural therapeutics, specifically research on research and development in adaptogens and nootropics. With over three years of intensive clinical experience, Dr. Mehra has a specialized background in managing complex physiological conditions through Ayurvedic drug protocols and therapeutic interventions. Her academic work is focused on the bridge between traditional botanical knowledge and modern evidence-based healthcare. In this handbook, she applies her clinical insights to the study of bioactive constituents, focusing on how natural adaptogens and nootropics can be integrated into functional foods and preventive healthcare strategies to enhance human resilience and wellbeing.Dr. Babak Namazi is a technical lead affiliated with the Department of Surgery at The University of Texas Southwestern Medical Center in Texas, USA. He brings over 10 years of academic research experience in artificial intelligence (AI), machine learning, computer vision and graphics, and computer networks, alongside more than 7 years of specialized experience applying these technologies within the medical field. Dr. Namazi works within a multidisciplinary team comprising surgeons, radiologists, internal medicine specialists, emergency medicine professionals, nursing staff, residents, and data scientists. Together, they focus on addressing existing challenges and unmet needs in healthcare through AI-driven solutions.His primary area of expertise involves leveraging AI, machine learning (ML), and deep learning (DL) techniques for operating room pre-operative process improvements. This includes data processing and task automation aimed at enhancing efficiency and outcomes in surgical workflows. In addition to his hands-on work in healthcare AI applications, Dr. Namazi actively contributes to the academic community by regularly reviewing scientific papers related to machine learning in the medical domain.His role illustrates a strong interdisciplinary approach, integrating advanced computational methods with clinical knowledge to innovate healthcare practices. Through his work at UT Southwestern Medical Center, Dr. Namazi exemplifies the growing intersection between cutting-edge technology and medicine, striving to improve patient care and operational processes in healthcare environments.Dr. Kanishka Tyagi is Director of Artificial Intelligence at UHV Technologies, Ft. Wayne, IN, USA, where he leads the development of Machine Learning in diverse R&D projects, including the sorting of non-recyclable plastics, metal alloys, pathological samples, and the analysis of Roots CT images, funded by the US Department of Energy. Previously, he has worked as a lead machine learning autonomous driving scientist at Aptiv Corporation in Agoura Hills, California. Prior to Aptiv, he worked at Siemens research, interned in ML groups at The MathWorks and Google Research. He has worked as a visiting researcher at Ajou University and Seoul National University. Dr. Tyagi worked as a Research Associate at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, with Dr. P.K. Kalra. He received his M.S. and Ph.D. degree with Dr. Michael Manry in the Department of Electrical Engineering at the University of Texas at Arlington. His research interests are optimization theory, music and audio processing, neural networks, hardware machine learning, and radar machine learning. He is a co-editor of Quantum Computing: A Shift from Bits to Qubits from Springer. Dr. Tyagi has filed 15 U.S. patents/trade secrets in the course of his research.

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