Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A general architecture for intelligent tutoring of diagnostic classification problem solving.

Rebecca S Crowley1, Olga Medvedeva

  • 1Center for Pathology Informatics, University of Pittsburgh School of Medicine, PA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 20, 2004
PubMed
Summary

This study introduces a general architecture for medical training systems, enhancing diagnostic skills through a novel approach. It integrates intelligent tutoring with a problem-solving method language for modularity and effective learning in pathology.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Medulloblastomas in Pediatric and Adults.

Advances in experimental medicine and biology·2023
Same author

Craniopharyngiomas: Surgery and Radiotherapy.

Advances and technical standards in neurosurgery·2022
Same author

Encapsulated Methionine γ-Lyase: Application in Enzyme Prodrug Therapy of <i>Pseudomonas aeruginosa</i> Infection.

ACS omega·2020
Same author

Second-hit APC mutation in a familial adamantinomatous craniopharyngioma.

Neuro-oncology·2020
Same author

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Cancer research·2017
Same author

Automated annotation and classification of BI-RADS assessment from radiology reports.

Journal of biomedical informatics·2017

Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Cognitive Science

Background:

  • Expertise development in classification problem-solving is crucial for medical diagnostics.
  • Traditional Intelligent Tutoring Systems (ITS) have limitations in adaptability and modularity.
  • A unified architecture is needed to enhance knowledge-based medical training systems.

Purpose of the Study:

  • To present a general architecture for knowledge-based medical training systems.
  • To improve diagnostic classification problem-solving skills in medical professionals.
  • To implement this architecture in a practical application for dermatopathology training.

Main Methods:

  • Developed a general architecture integrating ITS with the Unified Problem-solving Method description Language (UPML).

Related Experiment Videos

  • Utilized domain and task ontologies, and case data to create a dynamic solution graph via abstract problem-solving methods.
  • Implemented an instructional layer based on pedagogic ontologies and student models to filter interactions.

    • Main Results:

    • The architecture supports component modularity and reuse, enhancing system development.
    • A dynamic solution graph facilitates interactive learning based on expert problem-solving methods.
    • The system's implementation in SlideTutor demonstrates its potential for dermatopathology training.

    Conclusions:

    • The proposed architecture offers a flexible and robust framework for developing advanced medical training systems.
    • This approach enhances the learning of diagnostic classification problem-solving skills.
    • Further development and implementation in SlideTutor show promise for specialized medical education.