AIMC Topic: Humans

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Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans.

Scientific reports
Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict the...

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

Scientific reports
With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly develop...

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

Journal of electrocardiology
BACKGROUND: Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, ex...

Effects of robot-assisted gait training on cardiopulmonary function and lower extremity strength in individuals with spinal cord injury: A systematic review and meta-analysis.

The journal of spinal cord medicine
CONTEXT: Robot-assisted gait training (RAGT) has been increasingly adopted in many rehabilitation facilities for walking function and activity in individuals with spinal cord injury (SCI). However, the effectiveness of RAGT on lower extremity strengt...

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Critical care clinics
Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers ...

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.

International journal of radiation oncology, biology, physics
PURPOSE: Real-world evidence for radiation therapy (RT) is limited because it is often documented only in the clinical narrative. We developed a natural language processing system for automated extraction of detailed RT events from text to support cl...

An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study.

Reproductive biomedicine online
RESEARCH QUESTION: Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after fr...

Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

Journal of digital imaging
The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from...

Robot-assisted lateral pelvic lymph node dissection in patients with advanced rectal cancer: a single-center experience of 65 cases.

Journal of robotic surgery
The treatment of lateral pelvic lymph node (LPLN) metastasis of rectal cancer has evolved because of technical difficulties from open surgery to laparoscopy and, recently, robot-assisted surgery. This study aimed to evaluate the technical feasibility...

Imaging of Neck Nodes in Head and Neck Cancers - a Comprehensive Update.

Clinical oncology (Royal College of Radiologists (Great Britain))
Cervical lymph node metastases from head and neck squamous cell cancers significantly reduce disease-free survival and worsen overall prognosis and, hence, deserve more aggressive management and follow-up. As per the eighth edition of the American Jo...