AIMC Topic: Humans

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A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

European journal of internal medicine
BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a condition characterized by the abnormal accumulation of hepatic lipids that is clos...

The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.

Rheumatology international
Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their pre...

Gender-based linguistic differences in letters of recommendation for rhinology fellowship over time: A dual-institutional follow-up study using natural language processing and deep learning.

International forum of allergy & rhinology
This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Medical physics
BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray ...

Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.

Computers in biology and medicine
BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as...

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Multiple sclerosis and related disorders
Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though M...

Artificial intelligence in colorectal multidisciplinary team meetings. What are the medicolegal implications?

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: To give an insight into areas for future development and suggestions in the complexities of incorporation of AI into human colorectal cancer (CRC) care while bringing into focus the importance of clinicians' roles in patient care.

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis.

Pathology, research and practice
Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less objective than automated, computer-based evaluations. Artificial Intell...

[OCT biomarkers in diabetic maculopathy and artificial intelligence].

Die Ophthalmologie
Diabetes mellitus is a chronic disease the microvascular complications of which include diabetic retinopathy and maculopathy. Diabetic macular edema, proliferative diabetic retinopathy, and diabetic macular ischemia pose a threat to visual acuity. Ar...