[HTML][HTML] Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and validation
Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …
studies is often inadequate, making it difficult to understand and replicate such studies. To …
[HTML][HTML] Considerations in the reliability and fairness audits of predictive models for advance care planning
Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend
that models be audited for reliability and fairness. However, there is a gap of operational …
that models be audited for reliability and fairness. However, there is a gap of operational …
[HTML][HTML] Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation
While many deep-learning-based computer-aided detection systems (CAD) have been
developed and commercialized for abnormality detection in chest radiographs (CXR), their …
developed and commercialized for abnormality detection in chest radiographs (CXR), their …
All models are local: time to replace external validation with recurrent local validation
External validation is often recommended to ensure the generalizability of ML models.
However, it neither guarantees generalizability nor equates to a model's clinical usefulness …
However, it neither guarantees generalizability nor equates to a model's clinical usefulness …
[HTML][HTML] Reporting and methodological observations on prognostic and diagnostic machine learning studies
JMIR AI - Reporting and Methodological Observations on Prognostic and Diagnostic Machine
Learning Studies Skip to Main Content Skip to Footer JMIR Publications Select options Career …
Learning Studies Skip to Main Content Skip to Footer JMIR Publications Select options Career …
The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness
H Dickinson, DY Teltsch, J Feifel, P Hunt… - Drug safety, 2024 - Springer
The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of
promise and may enhance patient outcomes. These tools can perform actions such as …
promise and may enhance patient outcomes. These tools can perform actions such as …
Predictive models: important problems and innovative methods
S Bakken - Journal of the American Medical Informatics …, 2022 - academic.oup.com
A 2019 Editorial by Associate Editor, Lenert, 1 on the science of informatics and predictive
analytics concluded that “It is not enough just to build tools that predict and describe them; …
analytics concluded that “It is not enough just to build tools that predict and describe them; …
[HTML][HTML] Monitoring approaches for a pediatric chronic kidney disease machine learning model
Objective The purpose of this study is to evaluate the ability of three metrics to monitor for a
reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric …
reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric …
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Concern about the reliability and fairness of deployed artificial intelligence (AI) models
trained on electronic health record (EHR) data is growing. EHR-based AI models have been …
trained on electronic health record (EHR) data is growing. EHR-based AI models have been …