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A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma.

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherap...

Patient-specific uncertainty calibration of deep learning-based autosegmentation networks for adaptive MRI-guided lung radiotherapy.

Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour correctio...

Tracking Conditioned Fear in Pair-Housed Mice Using Deep Learning and Real-Time Cue Delivery.

Post-traumatic stress disorder (PTSD) is a complex and prevalent neuropsychiatric condition that ari...

Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter.

As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial...

Assessment of Machine Learning Algorithms to Predict Medical Specialty Choice.

Equitable distribution of physicians across specialties is a significant public health challenge. Wh...

Detecting Adverse Drug Events in Clinical Notes Using Large Language Models.

Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However,...

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperat...

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stro...

An analytic research and review of the literature on practice of artificial intelligence in healthcare.

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, reh...

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at me...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic di...

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction.

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Sol...

Comparative analysis of LLMs performance in medical embryology: A cross-platform study of ChatGPT, Claude, Gemini, and Copilot.

Integrating artificial intelligence, particularly large language models (LLMs), into medical educati...

Artificial intelligence system improves the quality of digestive endoscopy: A prospective pretest and post-test single-center clinical trial.

BACKGROUND: With the assistance of ENDOANGEL, a study was conducted at Hainan General Hospital to ev...

Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments.

Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation,...

The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023.

Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is use...

Development and Validation of Machine Learning Algorithms for Predicting Prolonged Postoperative Opioid Use in Spinal Metastatic Disease.

Introduction: Operative management of spinal metastatic disease is largely for symptom palliation an...

Assessing Large Language Models for Medical Question Answering in Portuguese: Open-Source Versus Closed-Source Approaches.

Large language models (LLMs) show promise in medical knowledge assessment. This study benchmarked a ...

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