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

Ethical Issues01:27

Ethical Issues

963
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
963
Torts III01:26

Torts III

675
Types of Quasi-intentional Torts in Healthcare
Quasi-intentional torts in healthcare involve acts where intent is not directed to harm an individual but results in harm due to careless or reckless speech.
675
Torts I01:14

Torts I

1.2K
Torts in nursing are wrongful acts that can harm patients and potentially lead to civil liability for the involved nurse. These wrongful acts range from unintentional errors to deliberate actions. Depending on the nature and severity of the tort, a nurse found liable may face financial penalties or disciplinary actions. Understanding the distinctions between intentional, quasi-intentional, and unintentional torts is crucial for nurses to mitigate risks and provide safe patient care.
Intentional...
1.2K
Ethical Dilemmas II01:30

Ethical Dilemmas II

1.0K
Resolving an ethical dilemma in healthcare involves a systematic approach that considers every aspect of the issue, respecting both the patient's needs and values and the healthcare professional's ethical obligations. Here are potential steps to resolve an ethical dilemma:
1.0K
Standards of Care II01:19

Standards of Care II

655
Nurses bear specific legal responsibilities under several federal statutes, including:
655
Torts II01:13

Torts II

632
Intentional torts in healthcare refer to deliberate actions that cause harm or infringe on the rights of others. Understanding these torts is crucial for healthcare professionals to avoid legal liabilities and maintain ethical standards in patient care.
632

You might also read

Related Articles

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

Sort by
Same author

The Ireland BioClim dataset for observational and future climate projections.

Scientific data·2026
Same author

Evaluation of Sanafoam Vaporooter II stress on activated sludge performance for carbonaceous removal and nitrification.

Journal of environmental management·2026
Same authorSame journal

Detangling AI Transparency in the Medical Regulation Space.

Journal of law and medicine·2025
Same author

Preparing healthcare organisations for using artificial intelligence effectively.

Australian health review : a publication of the Australian Hospital Association·2025
Same author

Declaration of Computational Neurosurgery.

Advances in experimental medicine and biology·2024
Same author

Neurosurgery, Explainable AI, and Legal Liability.

Advances in experimental medicine and biology·2024

Related Experiment Video

Updated: Jul 4, 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

844

Artificial Intelligence in Medicine: Issues When Determining Negligence.

Paul Nolan1, Rita Matulionyte2

  • 1Barrister, NSW Bar.

Journal of Law and Medicine
|February 9, 2024
PubMed
Summary

Artificial intelligence (AI) in healthcare introduces complex legal liability issues. This analysis explores challenges in medical malpractice cases involving AI, particularly the "black box" problem, and proposes legal solutions.

Keywords:
artificial intelligenceblack box effectlegal liabilitymachine learningmedical carenegligencereasonable foreseeabilityres ipsa loquitur

More Related Videos

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.7K
SECONDs Administration Guidelines: A Fast Tool to Assess Consciousness in Brain-injured Patients
11:05

SECONDs Administration Guidelines: A Fast Tool to Assess Consciousness in Brain-injured Patients

Published on: February 6, 2021

14.7K

Related Experiment Videos

Last Updated: Jul 4, 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

844
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.7K
SECONDs Administration Guidelines: A Fast Tool to Assess Consciousness in Brain-injured Patients
11:05

SECONDs Administration Guidelines: A Fast Tool to Assess Consciousness in Brain-injured Patients

Published on: February 6, 2021

14.7K

Area of Science:

  • Medical Law and Ethics
  • Artificial Intelligence in Healthcare
  • Tort Law

Background:

  • The integration of artificial intelligence (AI) into clinical practice raises novel legal liability questions.
  • Current case law regarding AI in healthcare is limited globally.
  • The "black box" nature of AI presents unique challenges in legal contexts.

Purpose of the Study:

  • To analyze medical malpractice claims involving AI.
  • To identify challenges in establishing breach of duty of care and causation due to AI's "black box" nature.
  • To explore potential legal solutions for AI-related medical liability.

Main Methods:

  • Analysis of medical malpractice principles, focusing on the tort of negligence.
  • Examination of the "black box" AI problem in healthcare.
  • Application of the Civil Liability Act 2002 (NSW) and common law through hypothetical examples.

Main Results:

  • The "black box" characteristic of AI complicates establishing breach of duty and causation in medical negligence.
  • Existing legal frameworks face difficulties in addressing AI's opacity.
  • Hypothetical scenarios illustrate the practical challenges for litigants and courts.

Conclusions:

  • Addressing legal liability for AI in healthcare requires adapting current negligence principles.
  • Solutions are needed to overcome the "black box" problem and ensure accountability.
  • Further legal development is crucial for the safe and effective adoption of AI in medicine.