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Start Over Search Limiters Full Text Topic artificial intelligence Topic medicine Publisher cambridge university press
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1. Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Beckstead (2020).

Author

Longoni, Chiara, Bonezzi, Andrea, and Morewedge, Carey K.

Subjects
ARTIFICIAL intelligence, DECISION making, MEDICAL personnel, AVERSION
Abstract

In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead's (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Beckstead (2020)

Author

Chiara Longoni, Andrea Bonezzi, and Carey K. Morewedge

Subjects
artificial intelligence, medicine, decision strategies, Social Sciences, Psychology, BF1-990
Abstract

In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead’s (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do.

Published
2020
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3. Simulating dynamic systems using linear time calculus theories

Author

Broes De Cat, Maurice Bruynooghe, Joost Vennekens, Joachim Jansen, Bart Bogaerts, and Marc Denecker

Subjects
FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Theoretical computer science, Computer science, Multitude, Inference, medicine.disease, Theoretical Computer Science, Task (project management), Logic in Computer Science (cs.LO), Set (abstract data type), Computational Theory and Mathematics, Artificial Intelligence, Hardware and Architecture, medicine, Time complexity, Software, Calculus (medicine)
Abstract

Dynamic systems play a central role in fields such as planning, verification, and databases. Fragmented throughout these fields, we find a multitude of languages to formally specify dynamic systems and a multitude of systems to reason on such specifications. Often, such systems are bound to one specific language and one specific inference task. It is troublesome that performing several inference tasks on the same knowledge requires translations of your specification to other languages. In this paper we study whether it is possible to perform a broad set of well-studied inference tasks on one specification. More concretely, we extend IDP3 with several inferences from fields concerned with dynamic specifications. ispartof: Theory and Practice of Logic Programming vol:14 issue:4 pages:477-492 ispartof: location:Vienna status: published

Published
2014

4. A new method for evaluating kinesthetic acuity during haptic interaction

Author

Valentina Squeri, Jacopo Zenzeri, Dalia De Santis, Lorenzo Masia, Maura Casadio, Pietro Morasso, School of Mechanical and Aerospace Engineering, and Robotics Research Centre

Subjects
medicine.medical_specialty, Proprioception, Haptic interaction, business.industry, Computer science, General Mathematics, Healthy subjects, Kinesthetic learning, Computer Science Applications, Science::Biological sciences::Human anatomy and physiology [DRNTU], Physical medicine and rehabilitation, Control and Systems Engineering, Assessment methods, medicine, Robot, Limited capacity, Computer vision, Artificial intelligence, business, Software, Reliability (statistics)
Abstract

SUMMARYAlthough proprioceptive impairment is likely to affect in a significant manner the capacity of stroke patients to recover functionality of upper limb, clinical assessment methods currently in use are rather crude, with a low level of reliability and a limited capacity to discriminate the relevant features of this severe deficit. In the present paper, we describe a new technique based on robot technology, with the goal of providing a reliable, accurate, and quantitative evaluation of kinesthetic acuity, which can be integrated in robot therapy. The proposed technique, based on a pulsed assistance paradigm, has been evaluated on a group of healthy subjects.

Published
2014

5. N3logic: A logical framework for the world wide web

Author

James A. Hendler, Tim Berners-Lee, Yosi Scharf, Lalana Kagal, and Dan Connolly

Subjects
Web standards, FOS: Computer and information sciences, medicine.medical_specialty, Computer science, Social Semantic Web, web, Theoretical Computer Science, RDF, World Wide Web, Computer Science - Networking and Internet Architecture, Description logic, Artificial Intelligence, medicine, Semantic Web, Networking and Internet Architecture (cs.NI), logic, scoped negation, business.industry, Semantic Web Rule Language, computer.file_format, Linked data, N3, Computational Theory and Mathematics, Hardware and Architecture, quoting, business, Web modeling, computer, Software
Abstract

The Semantic Web drives towards the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose courses of action, and answer questions. This logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. The Web has several characteristics that can lead to problems when existing logics are used, in particular, the inconsistencies that inevitably arise due to the openness of the Web, where anyone can assert anything. N3Logic is a logic that allows rules to be expressed in a Web environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing resources on the Web, and functions including cryptographic, string, math. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data. In this paper, we describe N3Logic and illustrate through examples why it is an appropriate logic for the Web., To appear in Theory and Practice of Logic Programming (TPLP)

Published
2008