Enables readers to understand the future of medical applications with generative AI and related applications
Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data.
The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context.
The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices.
Topics covered include:
- Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders
- Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems
- Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring
- Education-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making
Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
Aditya Khamparia, Assistant Professor, Department of Computer Science at Babasaheb Bhimrao Ambedkar University, India. His research areas include Artificial Intelligence, Intelligent Data Analysis, Machine Learning, Deep Learning, and Soft Computing.
Deepak Gupta, Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. His research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.
