Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare

Human history is filled with inventions and other innovations that resulted in a significant and lasting change in our civilization’s course of development. From gasoline-powered vehicles to transistor-based electronics or jet airplanes, things we now take for granted often appeared suddenly and unexpectedly. Yet after their introduction, our world changed forever. Over the past two decades, artificial intelligence (AI) and machine learning (ML) have been stealthily increasing their presence in our everyday lives. This “randomly systematic” adoption process is exposing humanity to something we never previously directly faced: an intelligence that may (and likely will) exceed our own. Despite this, most people are not fully aware of current (and future) benefits, limitations, and threats related to AI/ML. Within health care, there is little awareness of what AI/ML is capable of and how these new capabilities are being implemented or utilized. It is this current state that serves as our “starting point” in the emerging debate on AI/ML in medicine and surgery, including its integration, projected influence, and many other considerations that are not that different from other past technology adoption paradigms. This book discusses both current trends and future developments in AI and ML across health care.

Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. - Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning - Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists - Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations - Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes - Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology

This book constitutes the refereed proceedings of the 14th Conference on Artificial Intelligence in Medicine, AIME 2013, held in Murcia, Spain, in May/June 2013. The 43 revised full and short papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: decision support, guidelines and protocols; semantic technology; bioinformatics; machine learning; probabilistic modeling and reasoning; image and signal processing; temporal data visualization and analysis; and natural language processing.

This book constitutes the refereed proceedings of the 13th Conference on Artificial Intelligence in Medicine, AIME 2011, held in Bled, Slovenia, in July 2011. The 42 revised full and short papers presented together with 2 invited talks were carefully reviewed and selected from 113 submissions. The papers are organized in topical sections on knowledge-based systems; data mining; special session on AI applications; probabilistic modeling and reasoning; terminologies and ontologies; temporal reasoning and temporal data mining; therapy planning, scheduling and guideline-based care; and natural language processing.

The knowledge-based management of medical acts in NUCLEUS -- Knowledge Acquisition, Representation & Learning -- Knowledge Representation and Modelling in HYBRIKON -- Knowledge Organisation in Medical KBS Construetion -- A Framework for Modular Knowledge Bases in the Domain of Hypertension Diseases -- KAVAS-2: Knowledge Acquisition, Visualisation and Assessment System -- KAVAS's Framework for quality assessment of medical knowledge -- KAVAS's Conditioning of the Induction Algorithm -- Clinical decision-support in the field of TETANUS serology using an associative storage model implemented in LISP -- Model based learning support to knowledge acquisition: A clinical case study -- MODELS FOR MEDICAL KNOWLEDGE REPRESENTATION AND MEDICAL REASONING IN A C.A.I SYSTEM -- Case Based Reasoning in Clinical Evaluation -- Object-oriented mentality: the most suited paradigm for medical knowledge-based systems -- Applications Based on Neural Nets -- Classification of protein patterns using neural networks: pixel based versus feature based approach -- Evaluation of an epiderniological data set as an example of the application of neural networks to the analysis of large medical data sets -- A Neural Network Modular System for Object Classification in Brain MR Images -- A Neural Network Identifies Faces with Morphological Syndromes -- Grading of Gliomas in Stereotactic Biopsies with Neural Networks -- Self Organizing Maps for the Evaluation of High Resolution ECG -- AUTHOR INDEX

Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. - Provides history and overview of artificial intelligence, as narrated by pioneers in the field - Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence - Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

This book constitutes the refereed proceedings of the 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003, held in Protaras, Cyprus, in October 2003. The 24 revised full papers and 26 revised short papers presented together with two invited contributions were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on temporal reasoning, ontology and terminology, image processing and simulation, guidelines and clinical protocols, terminology and natural language issues, machine learning, probabilistic networks and Bayesian models, case-based reasoning and decision support, and data mining and knowledge discovery.

This book constitutes the refereed proceedings of the 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003, held in Protaras, Cyprus, in October 2003. The 24 revised full papers and 26 revised short papers presented together with two invited contributions were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on temporal reasoning, ontology and terminology, image processing and simulation, guidelines and clinical protocols, terminology and natural language issues, machine learning, probabilistic networks and Bayesian models, case-based reasoning and decision support, and data mining and knowledge discovery.