AIMC Topic: Checklist

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Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of consensus on its application and evaluation.

Inpatient Fall Prediction Models: A Scoping Review.

Gerontology
INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decisio...

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms.

Journal of the American College of Radiology : JACR
OBJECTIVE: To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging...

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

Nature medicine
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluati...

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology.

Neuroradiology
Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technica...

Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study.

Journal of clinical nursing
AIMS: The purpose of this study was to construct a model for oral assessment using deep learning image recognition technology and to verify its accuracy.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

European radiology
PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application ...

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.

BMJ open
INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and criti...

The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies.

International journal of medical informatics
This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday car...

Pseudo-pneumatosis of the gastrointestinal tract: its incidence and the accuracy of a checklist supported by artificial intelligence (AI) techniques to reduce the misinterpretation of pneumatosis.

Emergency radiology
PURPOSE: To assess the incidence of erroneous diagnosis of pneumatosis (pseudo-pneumatosis) in patients who underwent an emergency abdominal CT and to verify the performance of imaging features, supported by artificial intelligence (AI) techniques, t...