The euphoric assumption that powerful computers fed with sophisticated software programmes may serve as a substitute for human knowledge and decision making has been replaced by a more realistic concept of how computers may help in collecting data and their interpretation on the basis of human knowledge and experience. The computer is now used as a dedicated tool to support man in overtaking cumbersome and monotonous processes and tedious calculations. Running 24 hours a day, a specific feature of the computer is that depending on unequivocal software programmes it does neither forget or alter commands and information. Computer programmes imitating human thinking and information processing are called expert or knowledge based systems. These are especially useful when multiple possible combinations of data make a given task very complex. This review presents several systems used in different medical disciplines to describe fundamental ideas, different problem-solving methods, techniques and possible working fields including anaesthesia. It is made clear why computers have found widespread use in all administrative areas. In contrast, no system comparable in potency has been developed for use in clinical medicine in spite of 25 years of research in expert systems. This review starts with a definition of expert knowledge and the appropriate transformation of this knowledge to the computer. In addition, a general survey about the structure of expert systems and a state of the art in some current problem solving methods is given. Additional aspects and restrictions including technical, psychological and legal problems which seem to be unimportant from the outside but are essential for the development of expert systems are presented.
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http://dx.doi.org/10.1055/s-2007-996692 | DOI Listing |
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFExpert Opin Drug Deliv
January 2025
Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California, USA.
Introduction: Cyclic antimicrobial peptides (CAMPs) are gaining attention as promising candidates in advanced drug delivery systems due to their structural stability, resistance to proteolytic degradation, and versatile therapeutic potential. Their unique properties enable applications that extend beyond combating multidrug-resistant (MDR) pathogens. Their amphipathic and cell-penetrating properties allow them to efficiently transport drugs across cellular membranes.
View Article and Find Full Text PDFEur J Surg Oncol
January 2025
Dipartimento di Scienze Chirurgiche, University of Turin, Italy.
Approximately 1.5 million women suffer from gynecological malignancies every year. Around 60 % of these cancers are classified as rare in the European population.
View Article and Find Full Text PDFExpert Rev Pharmacoecon Outcomes Res
January 2025
Department of Health Technology Assessment and Health Economics, Institute for Clinical Effectiveness and Health Policy (IECS), Buenos Aires, Argentina.
Background: Lung cancer (LC) is a leading cause of cancer mortality in Argentina. Low-dose computed tomography (LDCT) had demonstrated higher efficacy than chest radiography as a screening method for early detection and reducing LC mortality. This study estimates the Budget Impact of implementing annual LDCT screening for individuals aged 55-74 with at least 30 pack-years of smoking in Argentina.
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