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Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

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...

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Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI

Viknesh Sounderajah^1,2, Hutan Ashrafian^3,2, Robert M Golub⁴

¹Department of Surgery and Cancer, Imperial College London, Paddington, UK.

|June 29, 2021

Summary

This summary is machine-generated.

We are developing STARD-AI, a checklist for reporting artificial intelligence (AI) diagnostic accuracy studies. This ensures transparent and complete reporting of AI in healthcare.

Keywords:

health informatics protocols & guidelines quality in health care

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Area of Science:

Medical Informatics
Health Services Research
Artificial Intelligence in Medicine

Background:

The Standards for Reporting of Diagnostic Accuracy (STARD) checklist enhances reporting quality for diagnostic tests.
Current STARD guidelines do not adequately address the unique challenges of AI-driven diagnostic interventions.
There is a need for specific reporting standards for AI in diagnostic accuracy studies.

Purpose of the Study:

To develop a new reporting guideline, STARD-AI, tailored for diagnostic accuracy studies involving artificial intelligence.
To improve the completeness and transparency of reporting for AI-centered diagnostic test accuracy studies.

Main Methods:

A six-stage development process is being employed for STARD-AI.
This includes project organization, item generation via literature review and expert surveys, and a modified Delphi consensus methodology.
The process also involves drafting, piloting with expert users, and developing an explanation and elaboration document.

Main Results:

The STARD-AI checklist development is in progress, utilizing a rigorous consensus-based approach.
Ethical approval has been obtained, and a dissemination strategy targeting multiple stakeholders is planned.
The development process involves expert consensus, piloting, and aims for widespread adoption.

Conclusions:

STARD-AI will provide essential guidance for reporting AI diagnostic accuracy studies.
This new checklist aims to enhance transparency and completeness in a rapidly evolving field.
Effective dissemination will be crucial for the adoption and impact of STARD-AI.