相關主題
商品描述
Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.
作者簡介
Dr Niklas Lidströmer - Karolinska Institute, MD, MSc, Specialist Physician, Postgraduate researcher in AI in Medicine, Senior Advisor in Medical Investments for investment companies, AI entrepreneur and CEO of an AI powered medical platform, former Head of Medical AI at a row of med-tech companies, and also co-leader of a handful of successful medical startups.
His experience also encompasses wide-spread global clinical work during over twenty years, in eight countries and in many different regions of these. He started with a master thesis in Global Medicine in 1996-1998, followed by clinical work all over the world and as a circumnavigating maritime doctor.
His global working experience, fluency in nearly ten languages, practical familiarization with AI in the medical and pharmaceutical industries and clinical specialist competence in general medicine, has produced a deep interest for translational and educational aspects of artificial intelligence in medicine.Dr Niklas Lidströmer is devoutly focused on bestowing the world with this major reference work in artificial intelligence in medicine.
Dr Hutan Ashrafian - Imperial College, AI Researcher, BSc Hons, MBBS, MBA, PhD, MRCS - is a surgeon, historian, systems biologist, biostatistician, paleopathologist and philosopher, and Clinical Lecturer in surgery at Imperial College London and Surgeon Registrar at Chelsea and Westminster Hospital in London, UK.
His research objectives are to develop innovative and technological strategies to resolve the global healthcare burden of obesity, metabolic syndrome, obesity-related cardiorespiratory disease, musculoskeletal dysfunction and cancer.
His work encompasses clinical and mechanistic studies of metabolic bariatric surgery, complex biostatistical models, networks and evidence synthesis to guide policy decisions, computational physiology and systems medicine, novel health technology assessment and diagnostic accuracy of biomarkers, robotics and artificial intelligence agents in healthcare, ancient history analytics, academic impact & leadership metrics and regenerative strategies including sports-based and bio-inspired bionic therapies.
