Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

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...

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

Standards of Care II

Standards of Care II

Nurses bear specific legal responsibilities under several federal statutes, including:

Current Trends in Nursing I

Current Trends in Nursing I

Current trends in nursing include:

Ethical Dilemmas II

Ethical Dilemmas II

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:

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

You might also read

Related Articles

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

Sort by

Same author

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

Journal of Alzheimer's disease : JAD·2026

Same author

Large language models for Alzheimer's disease drug discovery.

Journal of Alzheimer's disease : JAD·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Quo Vadis, AI-Empowered Doctor?

Gary Takahashi¹, Laurentius von Liechti¹, Ebrahim Tarshizi¹

¹Shiley-Marcos School of Engineering, University of San Diego, 5998 Alcalá Park, San Diego, CA, 92110, United States, 1 503 847 3079.

JMIR Medical Education

|August 15, 2025

Summary

This summary is machine-generated.

Physicians are increasingly using artificial intelligence (AI) in healthcare, impacting their roles and productivity. Integrating AI into medical education is crucial for understanding its potential and challenges.

Keywords:

AI AI in medicine LLM artificial intelligence clinical medicine decision support large language models

More Related Videos

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Sep 11, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Workflow Optimization

Background:

Physician professional autonomy has decreased with the shift to employed status and mandatory electronic health record (EHR) integration.
The introduction of large language models (LLMs) and generative AI presents new opportunities and challenges in clinical practice.

Purpose of the Study:

To explore the impact of generative AI on clinical practice and physician roles.
To advocate for the systematic integration of AI into medical education.

Main Methods:

Review of current trends in AI adoption in healthcare settings.
Analysis of the potential effects of generative AI on clinical efficiency, diagnostic precision, and physician productivity.
Consideration of emerging liability issues associated with AI implementation.

Main Results:

Generative AI offers potential for enhanced clinical efficiency and diagnostic precision.
AI adoption is reshaping physician roles, productivity expectations, and introducing new liability concerns.
Physician and stakeholder input is essential during the early stages of AI integration.

Conclusions:

AI is a sophisticated clinical tool that requires a comprehensive understanding.
Systematic incorporation of AI into medical curricula is advocated to prepare future physicians.
Proactive engagement with AI development and integration is necessary to navigate its transformative potential in healthcare.