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

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
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...
6.1K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Nursing Clinical Information System01:27

Nursing Clinical Information System

1.2K
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
1.2K
Cognitive Learning01:21

Cognitive Learning

970
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
970

You might also read

Related Articles

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

Sort by
Same author

Using Artificial Intelligence to Improve Timeliness of Follow-Up in Breast Cancer Screening.

Journal of the American College of Radiology : JACR·2026
Same author

Persistent Delays in Diagnostic Evaluation Timeliness After an Abnormal Screening Mammogram in the Years following the Onset of the COVID-19 Pandemic.

Academic radiology·2026
Same author

Outcomes of Density-Targeted Supplemental Breast Magnetic Resonance Imaging Screening by Breast Cancer Risk: Long-Term Health and Economic Considerations.

Annals of internal medicine·2026
Same author

Advanced Versus Invasive Breast Cancer Risk in a Screening Population: Implications for Risk-based Prevention and Screening Strategies.

Journal of general internal medicine·2026
Same author

Performance across different versions of an artificial intelligence model for screen-reading of mammograms.

European radiology·2026
Same author

Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review.

Journal of the National Cancer Institute·2026
Same journal

ACR Appropriateness Criteria® Myelopathy: 2026 Update.

Journal of the American College of Radiology : JACR·2026
Same journal

ACR Appropriateness Criteria® Chronic Knee Pain: Update 2026.

Journal of the American College of Radiology : JACR·2026
Same journal

Reply.

Journal of the American College of Radiology : JACR·2026
Same journal

Radiation Sensibilities: The American College of Radiology Dose Index Registry Empowers Stakeholders in Radiation Dose Optimization.

Journal of the American College of Radiology : JACR·2026
Same journal

Supply Chain Vulnerabilities in Breast Imaging: Site- and Network-Level Strategies for a Concentrated Consumable Market.

Journal of the American College of Radiology : JACR·2026
Same journal

Prostate MRI Practices and PI-RADS Use in China's Mainland: A Nationwide Assessment and Opportunities for Standardization.

Journal of the American College of Radiology : JACR·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

986

A Learning Accelerator Framework: Scalable Clinical Artificial Intelligence Development and Delivery.

Diana S M Buist1, Annie Y Ng2, Bryan Haslam2

  • 1Data-Driven Strategies for Medicine & Biotechnology, Mercer Island, Washington.

Journal of the American College of Radiology : JACR
|December 14, 2025
PubMed
Summary
This summary is machine-generated.

A new vertically integrated model accelerates artificial intelligence (AI) in healthcare. This framework improves cancer detection rates and patient outcomes through iterative learning and adaptive clinical services.

Keywords:
Artificial intelligencelearning accelerator frameworklearning health systemmedical imagingvertical integration

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

747

Related Experiment Videos

Last Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

986
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

747

Area of Science:

  • Healthcare technology
  • Artificial intelligence in medicine
  • Clinical informatics

Background:

  • Developing and implementing artificial intelligence (AI) in healthcare presents significant challenges.
  • Traditional approaches often face inefficiencies and delays in AI translation.
  • A vertically integrated model is needed to bridge healthcare providers and technology developers.

Purpose of the Study:

  • To introduce a vertically integrated model, the Learning Accelerator Framework, designed to accelerate AI development and delivery in healthcare.
  • To address challenges in translating AI innovations into clinical practice.
  • To improve patient and healthcare outcomes through AI integration.

Main Methods:

  • The Learning Accelerator Framework comprises four core components: an integrated data registry, a continuous technology development stack, adaptive clinical services, and an iterative learning and development loop.
  • A case study detailing the application of the Framework throughout the AI lifecycle was utilized.
  • The framework guided the development and national delivery of a multi-stage AI breast cancer screening workflow.

Main Results:

  • The AI breast cancer screening workflow progressed from clinical validation to national delivery, impacting millions of patients.
  • Iterative learning loops, informed by real-world clinical feedback, enhanced the AI workflow's effectiveness.
  • The AI workflow demonstrated a significant absolute increase in cancer detection rate (0.99/1000 exams) and positive predictive value (0.55/100 recalls).
  • Equitable benefits were observed across diverse patient subpopulations, including variations in breast density, race, and ethnicity.

Conclusions:

  • The Learning Accelerator Framework offers a novel approach to mitigate challenges in AI translation within healthcare.
  • This model facilitates AI innovation for developers and provider systems, accelerating the adoption of AI solutions.
  • The breast AI case study highlights the Framework's effectiveness in ensuring AI implementation, building clinician trust, and improving patient outcomes and health equity.