AIMC Topic: Datasets as Topic

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Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review.

The Lancet. Digital health
During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic r...

Foundation model-driven distributed learning for enhanced retinal age prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However,...

Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-s...

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Bioinformatics (Oxford, England)
SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine...

LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness.

Impact of high-quality, mixed-domain data on the performance of medical language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliab...

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Studies in health technology and informatics
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study propose...