AIMC Topic: Primary Health Care

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Classifying real-world macroscopic images in the primary-secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images.

Clinical and experimental dermatology
BACKGROUND: The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Mos...

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial.

The British journal of dermatology
BACKGROUND: Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming ...

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

The Lancet. Digital health
BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challengin...

Building Capacity for Pragmatic Trials of Digital Technology in Primary Care.

Mayo Clinic proceedings
Frontline primary care teams face important challenges in seeking to transform the quality of care delivered to patients and to reduce clerical burden for clinicians. Digital technologies using artificial intelligence hold substantial promise to aid ...

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models c...