Minimum Information about Clinical Artificial Intelligence Modeling
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Minimum Information about Clinical Artificial Intelligence Modeling
metasource: Fairsharing.org
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MI-CLAIM checklist
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Names
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Minimum Information about Clinical Artificial Intelligence Modeling
metasource: Fairsharing.orgversion: None
MI-CLAIM checklist
metasource: Fairsharing.orgversion: None
Other names: MI-CLAIM checklist
Names
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Minimum Information about Clinical Artificial Intelligence Modeling
metasource: Fairsharing.orgversion: None
MI-CLAIM checklist
metasource: Fairsharing.orgversion: None
The MI-CLAIM checklist is a guideline intended to improve transparent reporting of AI algorithms in medicine. MI-CLAIM was created to enable a direct assessment of clinical impact (including FAIRness), and to allow rapid replication of the technical design process of any legitimate clinical AI study.
Description
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The MI-CLAIM checklist is a guideline intended to improve transparent reporting of AI algorithms in medicine. MI-CLAIM was created to enable a direct assessment of clinical impact (including FAIRness), and to allow rapid replication of the technical design process of any legitimate clinical AI study.
metasource: Fairsharing.orgversion: None
Webpage:
https://github.com/beaunorgeot/MI-CLAIMWebpage
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Publications
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Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
PMID: 32908275
metasource: Fairsharing.org
version: None
Publications
More detailed information about this field from each metasource.
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
PMID: 32908275
metasource: Fairsharing.org
version: None
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Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
PubMed citations: 33.
33 articles citing: Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review. PMID:35321760
Challenges in translational machine learning. PMID:35246744
Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review. PMID:35238322
Data Integration Challenges for Machine Learning in Precision Medicine. PMID:35145977
Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device. PMID:35084352
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. PMID:35026997
How Can We Ensure Reproducibility and Clinical Translation of Machine Learning Applications in Laboratory Medicine? PMID:35019992
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. PMID:35013569
Developing the Total Health Profile, a Generalizable Unified Set of Multimorbidity Risk Scores Derived From Machine Learning for Broad Patient Populations: Retrospective Cohort Study. PMID:34842542
Critical Appraisal of a Machine Learning Paper: A Guide for the Neurologist. PMID:34728938
The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. PMID:34725005
Lessons learned in transitioning to AI in the medical imaging of COVID-19. PMID:34646912
A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. PMID:34600518
Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging. PMID:34587794
Biomarkers of Coagulation and Inflammation in COVID-19-Associated Ischemic Stroke. PMID:34428931
Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. PMID:34426417
Artificial intelligence for the next generation of precision oncology. PMID:34408248
DOME: recommendations for supervised machine learning validation in biology. PMID:34316068
Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography? PMID:34274276
Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. PMID:34214626
Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients. PMID:34104894
Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects. PMID:34063324
An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. PMID:33990806
Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review. PMID:33986900
Artificial intelligence for clinical oncology. PMID:33930310
Enhancing trust in AI through industry self-governance. PMID:33895824
Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. PMID:33860829
Requirements and reliability of AI in the medical context. PMID:33721700
Multiblock Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. PMID:33662459
Challenges and advances in clinical applications of mesenchymal stromal cells. PMID:33579329
Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. PMID:33478654
Artificial intelligence in breast ultrasonography. PMID:33430577
Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System. PMID:33364604
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