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

Automated rule-based decision systems in forensic toxicology using expert knowledge: basic principles and practical

R L Cechner1, C A Sutheimer

  • 1Department of Pathology, Case Western Reserve University, Cleveland, Ohio 44106.

Journal of Analytical Toxicology
|September 1, 1990
PubMed
Summary
This summary is machine-generated.

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

Specimen Adulteration and Substitution in Workplace Drug Testing.

Forensic science review·2015
Same author

Detection of drugs of abuse in nails.

Journal of analytical toxicology·1998
Same author

Unusual death attributed to the combined effects of chloral hydrate, lidocaine, and nitrous oxide.

Journal of analytical toxicology·1998
Same author

Diltiazem and pentoxifylline determination in postmortem specimens.

Journal of analytical toxicology·1997
Same author

Evaluation of the Syva ETS-PLUS Ethyl Alcohol Assay with application to the analysis of antemortem whole blood, routine postmortem specimens, and synovial fluid.

Journal of analytical toxicology·1992
Same author

Computerized controlled-substance inventory management in a forensic toxicology laboratory: practical application of a state-change model.

Journal of analytical toxicology·1991

Expert systems (ES) and artificial intelligence (AI) offer practical benefits for forensic toxicology problem-solving. Implementing these technologies in the forensic toxicology laboratory (FTL) reduces errors, improves efficiency, and lowers costs.

Area of Science:

  • Forensic Toxicology
  • Artificial Intelligence
  • Expert Systems

Background:

  • Forensic toxicology laboratories face challenges with data complexity and error reduction.
  • Existing expert systems (ES) and artificial intelligence (AI) literature often uses excessive jargon.
  • There is a need for clear, practical applications of ES/AI in forensic toxicology.

Purpose of the Study:

  • To present the fundamental principles and practical advantages of applying ES/AI in forensic toxicology.
  • To demystify the technical aspects of ES/AI for broader understanding.
  • To assess the current status and potential applications of ES/AI within forensic toxicology laboratories.

Main Methods:

  • Review of the history, functions, and applications of established ES/AI systems.

Related Experiment Videos

  • Assessment of ES/AI implementation in forensic toxicology.
  • Analysis of practical experience with an integrated expert system in a laboratory setting.

    • Main Results:

    • An integrated expert system reduced laboratory errors and detected data inconsistencies.
    • New substance abuse subpopulations were identified.
    • Specimen processing time and instrument wear were minimized, increasing technician efficiency and cost-effectiveness.

    Conclusions:

    • Expert systems and AI provide significant practical benefits for forensic toxicology.
    • These technologies enhance laboratory accuracy, efficiency, and cost management.
    • Successful implementation leads to improved diagnostic capabilities and resource optimization.