AI Medical Compendium Topic

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Validation Studies as Topic

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Pitfalls in training and validation of deep learning systems.

Best practice & research. Clinical gastroenterology
The number of publications in endoscopic journals that present deep learning applications has risen tremendously over the past years. Deep learning has shown great promise for automated detection, diagnosis and quality improvement in endoscopy. Howev...

Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging.

PloS one
BACKGROUND: The development of deep learning (DL) algorithms is a three-step process-training, tuning, and testing. Studies are inconsistent in the use of the term "validation", with some using it to refer to tuning and others testing, which hinders ...

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.

Acta orthopaedica
Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models ...

Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study.

Critical care medicine
OBJECTIVES: Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential s...

Artificial intelligence in mammography: a systematic review of the external validation.

Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia
OBJECTIVE: To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography.

Leveraging Foundational Models in Computational Biology: Validation, Understanding, and Innovation.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Large Language Models (LLMs) have shown significant promise across a wide array of fields, including biomedical research, but face notable limitations in their current applications. While they offer a new paradigm for data analysis and hypothesis gen...

ClinValAI: A framework for developing Cloud-based infrastructures for the External Clinical Validation of AI in Medical Imaging.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Artificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algor...