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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

471
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
471
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.2K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.2K

You might also read

Related Articles

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

Sort by
Same author

[Professional skepticism in medical research].

Nederlands tijdschrift voor geneeskunde·2026
Same author

Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial.

Nature medicine·2026
Same author

Reporting Completeness of Diagnostic Accuracy Studies: A Meta-Review of Investigations on Adherence to STARD 2015.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

Cost-effectiveness of time-lapse monitoring with or without the use of embryo selection software compared to routine incubation and selection.

Human reproduction open·2026
Same author

Prognostic factor research: why it matters in orthopaedics and how we do it better.

Bone & joint open·2026
Same author

Cost-effectiveness of osteoporotic fracture risk assessment in people with intellectual disabilities: a UK NHS modelling study.

BMJ open·2026
Same journal

Author Correction: Teclistamab-based induction treatment in transplant-eligible, newly diagnosed multiple myeloma: a phase 2 trial.

Nature medicine·2026
Same journal

Data rights are the missing pillar for modernizing consent in medicine.

Nature medicine·2026
Same journal

Generalizable AI predicts immunotherapy outcomes across cancers and treatments.

Nature medicine·2026
Same journal

Immune aging biomarkers for clinical trials.

Nature medicine·2026
Same journal

Lassa fever countermeasures gather pace.

Nature medicine·2026
Same journal

Why high scores do not mean application readiness for health AI.

Nature medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

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

809

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.

Viknesh Sounderajah1,2, Ahmad Guni1,2, Xiaoxuan Liu3,4

  • 1Institute of Global Health Innovation, Imperial College London, London, UK.

Nature Medicine
|September 15, 2025
PubMed
Summary
This summary is machine-generated.

The new STARD-AI statement offers guidelines for reporting artificial intelligence (AI) diagnostic accuracy studies. It ensures transparent reporting of AI diagnostic tests, addressing bias and generalizability.

More Related Videos

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

4.1K

Related Experiment Videos

Last Updated: Jan 17, 2026

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

809
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.1K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

4.1K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Diagnostic Test Accuracy

Background:

  • The Standards for Reporting Diagnostic Accuracy (STARD) 2015 statement enhances reporting of diagnostic test accuracy studies.
  • Artificial intelligence (AI) in diagnostic testing presents unique reporting challenges.
  • A need exists for specific guidelines tailored to AI-centered diagnostic accuracy studies.

Purpose of the Study:

  • To introduce the STARD-AI statement, a minimum set of criteria for comprehensive reporting of AI-centered diagnostic test accuracy studies.
  • To provide a framework for evaluating the quality, bias, and applicability of AI diagnostic tools.
  • To promote transparency and completeness in the reporting of AI diagnostic research.

Main Methods:

  • Development involved a multistage, multistakeholder process.
  • Key steps included a literature review, expert surveys, and patient/public engagement.
  • A modified Delphi consensus process with over 240 international stakeholders informed the final checklist.

Main Results:

  • The STARD-AI statement includes 18 new or modified items, building upon STARD 2015.
  • It encourages reporting on dataset practices and the evaluation of the AI index test.
  • Emphasis is placed on addressing algorithmic bias and fairness.

Conclusions:

  • STARD-AI facilitates comprehensive and transparent reporting for AI-centered diagnostic accuracy studies.
  • The statement aids stakeholders in assessing the biases, applicability, and generalizability of AI study findings.
  • Adherence to STARD-AI is crucial for advancing reliable AI implementation in diagnostics.