Five critical quality criteria for artificial intelligence-based prediction models
To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the
cardiovascular health domain and thereby improve their impact and relevancy, the editors …
cardiovascular health domain and thereby improve their impact and relevancy, the editors …
[HTML][HTML] AI and machine learning in resuscitation: ongoing research, new concepts, and key challenges
Aim Artificial intelligence (AI) and machine learning (ML) are important areas of computer
science that have recently attracted attention for their application to medicine. However, as …
science that have recently attracted attention for their application to medicine. However, as …
External validation of AI models in health should be replaced with recurring local validation
Clinical prediction models follow a standard development pipeline: model development and
internal validation; external validation; and clinical impact studies. External validation …
internal validation; external validation; and clinical impact studies. External validation …
Implications of the use of artificial intelligence predictive models in health care settings: a simulation study
Background: Substantial effort has been directed toward demonstrating uses of predictive
models in health care. However, implementation of these models into clinical practice may …
models in health care. However, implementation of these models into clinical practice may …
TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Evaluation of clinical prediction models (part 1): from development to external validation
Evaluating the performance of a clinical prediction model is crucial to establish its predictive
accuracy in the populations and settings intended for use. In this article, the first in a three …
accuracy in the populations and settings intended for use. In this article, the first in a three …
Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening
Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition
algorithms are proliferating. Some initial reports claim outstanding accuracy followed by …
algorithms are proliferating. Some initial reports claim outstanding accuracy followed by …
APPRAISE-AI Tool for quantitative evaluation of AI studies for clinical decision support
Importance Artificial intelligence (AI) has gained considerable attention in health care, yet
concerns have been raised around appropriate methods and fairness. Current AI reporting …
concerns have been raised around appropriate methods and fairness. Current AI reporting …
Perspectives on validation of clinical predictive algorithms
The generalizability of predictive algorithms is of key relevance to application in clinical
practice. We provide an overview of three types of generalizability, based on existing …
practice. We provide an overview of three types of generalizability, based on existing …
Evaluation of clinical prediction models (part 2): how to undertake an external validation study
External validation studies are an important but often neglected part of prediction model
research. In this article, the second in a series on model evaluation, Riley and colleagues …
research. In this article, the second in a series on model evaluation, Riley and colleagues …