ARTIFICIAL INTELLIGENCE IN MEDICINE.
Contributor(s): Szolovits, Peter.
Publisher: [Place of publication not identified] : ROUTLEDGE, ©2019Description: 226 p.Content type: text Media type: computer Carrier type: online resourceISBN: 9780367172084.Subject(s): Information storage and retrieval systems -- Medicine | Artificial intelligence -- Medical applications | Medical informatics | Artificial intelligence -- Medical applications | Information storage and retrieval systems -- Medicine | Medical informaticsGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | R859.7.A78 C66 2019 (Browse shelf) | Available | AU00000000017020 |
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| R859.7.A78 A76 2016 Artificial intelligence in behavioral and mental health care / | R859.7.A78 A78 2020 Artificial intelligence in precision health / | R859.7.A78 C54 2020 Machine learning in medicine - a complete overview / | R859.7.A78 C66 2019 ARTIFICIAL INTELLIGENCE IN MEDICINE. | R859.7.A78 D44 2020 Deep learning in medical image analysis : challenges and applications | R859.7 .A78 E83 2017 Strategies in biomedical data science : driving force for innovation / | R859.7.A78 G73 2022 The doctor and the algorithm : promise, peril, and the future of health ai / |
Preface -- Artificial Intelligence and Medicine -- Representation of Expert Knowledge for Consultation: The CASNET and EXPERT Projects -- Consultation, Knowledge Acquisition, and Instruction: A Case Study -- The Development of Clinical Expertise in the Computer -- Heuristic Methods for Imposing Structure on III-Structured Problems: The Structuring of Medical Diagnostics -- Modeling Knowledge of the Patient in Acid-Base and Electrolyte Disorders.
This book introduces the field of artificial intelligence in medicine, a new research area that combines sophisticated representational and computing techniques with the insights of expert physicians to produce tools for improving health care. An introductory chapter describes the historical and technical foundations of the work and provides an overview of the current state of the art and research directions. The authors then describe four prototype computer programs that tackle difficult clinical problems in a manner similar to that of an expert physician. The programs presented are internist, a diagnostic aid that combines a large database of disease/manifestation associations with techniques for problem formulation; expert and the Glaucoma Program which use physiological models for the diagnosis and treatment of eye disease; mycin, a rule-based program for diagnosis and therapy selection for infectious diseases; and the Digitalis Therapy Advisor, which aids the physician in prescribing the right dose of the drug digitalis and also explains its actions.
Peter Szolovits is an associate professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. A specialist in artificial intelligence (AI) with an emphasis on medical applications, he is currently concerned with fundamental issues of representation and reasoning, including protocol analysis to discover how clinicians reason about probability and causality, and with programs which model human expert performance in some areas of medical diagnosis and care.
Peter Szolovits is an associate professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. A specialist in artificial intelligence (AI) with an emphasis on medical applications, he is currently concerned with fundamental issues of representation and reasoning, including protocol analysis to discover how clinicians reason about probability and causality, and with programs which model human expert performance in some areas of medical diagnosis and care.
Peter Szolovits is an associate professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. A specialist in artificial intelligence (AI) with an emphasis on medical applications, he is currently concerned with fundamental issues of representation and reasoning, including protocol analysis to discover how clinicians reason about probability and causality, and with programs which model human expert performance in some areas of medical diagnosis and care.