AIMC Topic: Humans

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Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC)...

Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised ma...

Still Using Only ChatGPT? The Comparison of Five Different Artificial Intelligence Chatbots' Answers to the Most Common Questions About Kidney Stones.

Journal of endourology
To evaluate and compare the quality and comprehensibility of answers produced by five distinct artificial intelligence (AI) chatbots-GPT-4, Claude, Mistral, Google PaLM, and Grok-in response to the most frequently searched questions about kidney sto...

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence.

Molecular pharmaceutics
Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The prefe...

Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.

Gait & posture
INTRODUCTION: The advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) pro...

Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department.

Social science & medicine (1982)
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have star...

Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.

Journal of sport & exercise psychology
Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training int...

Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.

Drugs
Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentra...

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

European journal of heart failure
AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abn...