If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 percent of the time, explained Khosla, adding, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "But the truly exciting thing was when we combined the pathologist's analysis with our automated computational diagnostic method, the result improved to 99.5 percent accuracy," said Beck.
From attributes 8 3 Implementation 8 3.1 Overview of Meta-Rulegen 3.2 Algorithm 10 3.2.1 Approach from object rule 11 3.2.2 Machine learning can be used to formulate new meta-level knowledge. A small MYCIN -like medical diagnosis system was constructed as a starting point. Two heuristic methods are used in a program called Meta-Rulegen to form metarules from the knowledge base in the diagnosis system. In a preliminary study, 63 metarules were formed automatically and, by judiciously selecting a set of metarules, the efficiency of the diagnosis system can be improved significantly without degrading the quality of advice. This study suggests that metarules can be learned automatically to improve the efficiency of rule-based systems. 1 Introduction The value of meta-level knowledge for guiding the invocation, construction, and explanatioi. of object-level rules in an expert system has been demonstrated by Davis . In this paper we explore the use of machine learning methods for ...
Reprinted by permission of the author. Published in the Proceedings of a Symposium on Computers in Medicine, Annual Meeting, California Medical Association, Anaheim, CA., February 1984. Edward H. Shortlitre, M.D., Ph.D. Division of General Internal Medicine, Department of Medicine Stanford University School of Medicine Stanford, California 94305 Alt;iough computing technology is playing an increasingly important role in medicine, systems designed to advise physicians on diagnosis or therapy selection have remained largely experimental to date. Despite diverse research efforts, and a literature on computer-aided diagnosis that has numbered over 1500 references in the last 20 years, clinical consultation programs have failed to achieve wide acceptance. The reasons for attempting to develop such systems are self-evident.
Heuristic Programming Project Report No. HPP 82-37 May 1982 COMPUTER-BASED CLINICAL DECISION AIDS: SOME PRACTICAL CONSIDERATIONS Edward H. Shortliffe, MD, PhD Division of General Internal Medicine Department of Medicine Stanford University School of Medicine Stanford, California 94305 To be presented at AMIA Congress Hyatt on Union Square San Francisco, California 2-5 May 1982 * Dr. Shortliffe is recipient of Research Career Development Award LM00048 from the National Library of Medicine. AMIA Congress 82 E.H. Shortliffe AKTRACT Medical decision making research has tended to emphasize the generation of optimal decisions, an issue which is central to the development of clinically useful consultation programs. This paper stresses the need to consider other theoretical and practical issues that are pertinent if consultation systems are to be accepted by physicians. Since adequate decision making performance remains an essential component of acceptable systems, the paper suggests c-iteria for selecting clinical problems that may be amenable to short-term implementation using state-of-the-art techniques. Introducticn At the beginning of a third decade of research into the development of computer-based diagnostic aids, it is appropriate for medical computer scientists to assess the strides that have been taken, the barriers that remain, and the optimal strategies for furthering the field in the years ahead.
Reprinted with permission from Science, Vol. After twenty-five years of use, the very name -- combining as it does a highly immodest ambition with a suggestion of deceit -- still has the power to provoke controversy. Research in artificial intelligence has several goals. One is the development of computational models of intelligent behavior, including both its cognitive and perceptual aspects. A more engineering-oriented goal is the development of computer programs that can solve problems normally thought to require human intelligence.
Despite diverse research efforts, and a literature on computer-aided diagnosis that has numbered at least 1,000 references in the last 20 years, clinical consultation programs have seldom been used other than in experimental environments. The reasons for attempting to develop such systems are self-evident. Growth in medical knowledge has far surpassed the ability of the single practitioner to master it all, and the computer's superior information poacessing capacity thereby offers a natural appeal. Furthermore, the reasoning processes of medical experts are poorly understood; attempts to model expert decision making necer.sarily New insights that result may also allow us more adequately to teach medical students and house staff the techniques for reaching good decisions, rather than merely to offer a collection of facts that they must independently learn to use coherently.
Reprinted, with permission from HPP 79-20 Proceedings of the IEEE, Vol.67, No.9, pp.1207-1224, September 1979. These include I) clinical algorithms. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. Since that time a variety of techniques have been applied, accounting for at least 800 references in the clinical ard computing literature [1121.
William van Melte Heuristic Programming Project Department of Computer Sc;ence Stanford University Stanford, California 94305 Abstract EMYCIN is a programming system for writing knowledge-based consultation programs with a production-rule representation of knowledge. Several major components of the system, Including an explanation program and knowledge acquisition routines, are described. EMYCIN has been used to build consultation systems in several areas of medicine, as well as an engineering domain. These experiences lead to some general conclusions regarding the potential applicability of EMYCIN to new domains. Keywords: knowledge-based systems, production rules, knowledge representation, automated consultant. This work was supported in part by the NSF grant Advanced Research Projects Agency, contract and the 1 Introduction The focus of much current work In artificial intelligence is the development of computer programs that aid scientists with complex reascning tasks.
These include I) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that consid-Manuscript received December 13, 1978: revised February 20, 1979.
This project was supported by the Bureau of Health Services Research and Evaluation, Computer-Based Consultations in Clinical Therapeutics, Research Grant No. HS01544, and by the Veterans Administration. Requests for reprints should be addressed to Ms. A.C. Scott, TC110, Stanford Univ. ABSTRACT The performance of a computer-based clinical consultation system is evaluated. The program, called MYCIN, is designed to function as an aid for infectious disease diagnosis are therapy selection, with an initial emphasis on bacteremias. The evaluation methodology is discussed, as well as the difficulties encountered in attempting to evaluate clinical judgments.