AIMC Topic: Artificial Intelligence

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Protocol for artificial intelligence-guided neural control using deep reinforcement learning and infrared neural stimulation.

STAR protocols
Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neura...

Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Pathology
Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of the epileptogenic region has been proven as a successful strategy to control seizures in a carefully selected patient cohort. Continuous efforts to s...

Evaluating LLM-based generative AI tools in emergency triage: A comparative study of ChatGPT Plus, Copilot Pro, and triage nurses.

The American journal of emergency medicine
BACKGROUND: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accur...

Clinical and research applications of natural language processing for heart failure.

Heart failure reviews
Natural language processing (NLP) is a burgeoning field of machine learning/artificial intelligence that focuses on the computational processing of human language. Researchers and clinicians are using NLP methods to advance the field of medicine in g...

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Diabetologia
Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals...

A novel case-based reasoning system for explainable lung cancer diagnosis.

Computers in biology and medicine
Lung cancer is a leading cause of cancer death worldwide. The survival rate is generally higher when this disease is detected in its early stages. Advances in artificial intelligence (AI) have enabled the development of decision support systems that ...

Automated detection of posterior urethral valves in voiding cystourethrography images: A novel AI-Based pipeline for enhanced diagnosis and classification.

Computers in biology and medicine
INTRODUCTION: Posterior Urethral Valves (PUV) are rare congenital anomalies of the male urinary tract that can lead to urethral obstruction and increased risk of kidney disease. Traditional diagnosis relies on subjective interpretation of imaging tec...

Local interpretable model-agnostic explanation approach for medical imaging analysis: A systematic literature review.

Computers in biology and medicine
BACKGROUND: The interpretability and explainability of machine learning (ML) and artificial intelligence systems are critical for generating trust in their outcomes in fields such as medicine and healthcare. Errors generated by these systems, such as...

Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma.

Computers in biology and medicine
Machine learning models are widely applied across diverse fields, including nearly all segments of human activity. In healthcare, artificial intelligence techniques have revolutionized disease diagnosis, particularly in image classification. Although...

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Cell
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitat...