ABSTRACT
Núñez et al.'s (2019) negative assessment of the field of cognitive science derives from evaluation criteria that fail to reflect the true nature of the field. In reality, the field is thriving on both the research and educational fronts, and it shows great promise for the future.
Subject(s)
Cognitive ScienceABSTRACT
OBJECTIVE: To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS: We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent agents capable of simulating human-like abilities. The agents, which have a simulated mind and, if applicable, a simulated body, are intended to operate as members of multi-agent teams featuring both artificial and human agents. The agent architecture and its underlying knowledge resources and processors are being developed in a sufficiently generic way to support a variety of applications. RESULTS: We show how several types of inconsistency can be detected and leveraged by intelligent agents in the setting of clinical medicine. The types of inconsistencies discussed include: test results not supporting the doctor's hypothesis; the results of a treatment trial not supporting a clinical diagnosis; and information reported by the patient not being consistent with observations. We show the opportunities afforded by detecting each inconsistency, such as rethinking a hypothesis, reevaluating evidence, and motivating or teaching a patient. CONCLUSIONS: Inconsistency is not always the absence of the goal of consistency; rather, it can be a valuable trigger for further exploration in the realm of clinical medicine. The OntoAgent cognitive architecture, along with its extensive suite of knowledge resources an processors, is sufficient to support sophisticated agent functioning such as detecting clinically relevant inconsistencies and using them to benefit patient-centered medical training and practice.
Subject(s)
Artificial Intelligence , Decision Support Systems, Clinical , Humans , Reproducibility of ResultsABSTRACT
This paper presents an overview of a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent agents capable of simulating human-like abilities. The agents, which have a simulated mind and, if applicable, a simulated body, are intended to operate as members of multi-agent teams featuring both artificial and human agents. The agent architecture and its underlying knowledge resources and processors are being developed in a sufficiently generic way to support a variety of applications. In this paper we briefly describe the architecture and two applications being configured within it: the Maryland Virtual Patient (MVP) system for training medical personnel and the CLinician's ADvisor (CLAD). We organize the discussion around four aspects of agent modeling and how they are utilized in the two applications: physiological simulation, modeling an agent's knowledge and learning, decision-making and language processing.
Subject(s)
Artificial Intelligence , Computer Simulation , Decision Making, Computer-Assisted , Clinical Medicine , Cognition , Humans , Knowledge Bases , Models, Biological , Terminology as Topic , User-Computer InterfaceABSTRACT
This paper briefly describes four cognitively-related aspects of modeling a virtual patient: interoception, decision-making, natural language processing and learning. These phenomena are treated within the Maryland Virtual Patient simulation and training environment.
Subject(s)
Cognition , Computer Simulation , Patients/psychology , User-Computer Interface , Decision Making , Humans , Learning , MarylandABSTRACT
The patient authoring interface for each disease in the Maryland Virtual Patient simulation environment reveals the conceptual substrate of the disease model. Revealing the disease model to the community both explains how the interactive simulations work and invites collaboration from the wider community.
Subject(s)
Cognition , Computer Simulation , Computer-Assisted Instruction , Education, Medical/methods , Patient Simulation , Cooperative Behavior , Esophageal Diseases , Humans , Maryland , User-Computer InterfaceABSTRACT
The Maryland Virtual Patient (MVP) Project seeks to create realistically functioning virtual humans endowed with automatic physiological and cognitive function that can be used in the training of medical personnel. Physiologically, the state of an MVP changes in response to internal pathophysiological stimuli and external stimuli, the latter initiated by either by the patient or the trainee. Cognitively, the MVP can communicate with trainees about current symptoms, lifestyle, history, adherence to prescribed treatments, etc. We will demonstrate simulation in the MVP environment using the example of patients suffering from gastroesophageal reflux disease (GERD).
