Chapter 1 Introduction to Medical Applications of Artificial Intelligence Chapter 2 Introduction to Machine Learning Chapter 3 Web-IntelliGenes: A reproducible, replicable, and transparent application of artificial intelligence and machine learning for predictive medicine Chapter 4 A Survey of Machine Learning Prediction of Hypoglycemia for Type 1 Diabetes Chapter 5 MicroRNA Target Prediction: 20+ Years and Two Strings Chapter 6 Lightweight classification of swallowing–respiratory coordination using Bayesian changepoint detection Chapter 7 Application of Reinforcement-Based Learning Feature Selection for
Early Detection of Alzheimer’s Disease using Speech Chapter 8 The role of artificial intelligence in next generation drug discovery Chapter 9 Medical Applications of Artificial Intelligence Chapter 10 Multimodal AI in Precision Medicine: Integrating EHR, Imaging, and Omics Data Chapter 11 Artificial Intelligence in Non-small Cell Lung Cancer: Diagnosis, Prognosis, and Management Chapter 12 Multimodal Artificial Intelligence for Lung Cancer Screening: A Comprehensive Review of Deep Learning Approaches Chapter 13 Trans-amplifying (TA) mRNA Vaccines: A New Paradigm in Combating Infections and Cancers Chapter 14 Label Challenges in Breast Cancer AI: Noise, Bias, Uncertainty, and Limited Annotations Chapter 15 Health Digital Twin Applications: Disease Domains, Techniques, and Related Methods Chapter 16 Digital Health Programs for Sustainable Development: Insights from National and International Initiatives Chapter 17 Alzheimer’s disease prediction using Brain MRI: Emphasis on Ensemble Learning Chapter 18 Learning-Based Image Registration Chapter 19 Representation Learning for ECG Data: Variational Autoencoders, Contrastive Learning, and Transformer Models Chapter 20 Artificial Intelligence in Aesthetic & Procedural Dermatology: Multimodal Imaging, Decision Support, and Outcome Optimization Chapter 21 Comparative Analysis of Linear and Nonlinear Stochastic Models for Real-Time Soft Tissue Characterisation Chapter 22 Artificial Intelligence and Computational Medicine: A Hands-on Approach Chapter 23 Artificial Intelligence in Modern Surgery: Clinical Applications, Education, and Health System Impact Chapter 24 How to get to True Robotic Surgery and Keep the Surgeon in the Loop : Cobotics, Autonomy and Artificial Intelligence Surgery Chapter 25 Importance of clinically adequate labelling in surgical phase detection of laparoscopic cholecystectomy procedures Chapter 26 Capturing Motion and AI in Medical Training, Rehabilitation, Remote Patient Monitoring, and the Diagnosis and Prognosis of Movement Disorders Chapter 27 Applications of Machine Learning and Artificial Intelligence in Personalized Gait Training After Stroke Chapter 28 A systematic review and meta-analysis of the evidence for generative AI chatbots for symptoms of anxiety, depression, and loneliness Chapter 29 Integrating Artificial Intelligence and Machine Learning into Palliative and End-of-Life Care Chapter 30 Resources on Medical Applications of Artificial Intelligence
Biography
Dr Arvin Agah is Charles E. & Mary J. Spahr Professor of Electrical Engineering and Computer Science at the University of Kansas, which he joined in 1997. He served as the Dean of Engineering from 2018 to 2024 and Associate Dean for Research and Graduate Programs from 2012 to 2018. His research interests include medical applications of artificial intelligence (AI), applied AI, and autonomous mobile robots. He has received multiple honors for his teaching excellence, including two university-wide awards.
