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 Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K

You might also read

Related Articles

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

Sort by
Same author

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms.

Computational intelligence and neuroscience·2022
Same author

Accident Liability Determination of Autonomous Driving Systems Based on Artificial Intelligence Technology and Its Impact on Public Mental Health.

Journal of environmental and public health·2022
Same author

Research on Legal Constraints of Individual Environmental Data Rights and Interests in Big Data Environment.

Journal of environmental and public health·2022
See all related articles

Related Experiment Video

Updated: Sep 7, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

982

Application of Neural Network Algorithm in Medical Artificial Intelligence Product Development.

Yineng Xiao1

  • 1School of Health Humanities, Peking University, Haidian District, Beijing, China 100191.

Computational and Mathematical Methods in Medicine
|June 20, 2022
PubMed
Summary

This study introduces a risk administrative law framework to assess social risks from medical artificial intelligence (AI). A novel risk assessment model using artificial neural networks (ANN) demonstrates high accuracy, paving the way for safer AI integration in healthcare.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Sep 7, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

982
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Medical Artificial Intelligence
  • Risk Management
  • Administrative Law

Background:

  • The rapid integration of artificial intelligence (AI) into healthcare presents significant social risks, impacting civil rights, social stability, and healthy development.
  • Existing risk regulation theories face challenges in addressing the unique complexities of medical AI.
  • There is a need for a robust theoretical framework to manage the social risks associated with medical AI.

Purpose of the Study:

  • To analyze the social risks inherent in medical AI development and application.
  • To reconstruct a theoretical system for assessing medical AI social risks by integrating risk prevention principles with benefit measurement.
  • To propose a novel risk assessment model for medical AI.

Main Methods:

  • A comprehensive review of existing literature on medical AI ethics and risk.
  • Introduction of artificial neural network (ANN) technologies to construct a medical AI risk assessment index system.
  • Development and application of a backpropagation neural network (BPNN) model using a self-designed dataset for risk assessment.

Main Results:

  • The study systematically analyzes social risks associated with medical AI.
  • A novel risk assessment index system for medical AI was constructed.
  • The developed BPNN model exhibited minimal error, indicating its effectiveness and potential for widespread application.

Conclusions:

  • The proposed risk administrative law approach provides a flexible and systematic framework for medical AI risk assessment.
  • The developed ANN-based risk assessment model is accurate and practical.
  • The research supports the safe and responsible advancement of AI in the medical field.