Surveys

Assessment of Correlation Between Diabetic Retinopathy and Metabolic Biomarkers Using Artificial Intelligence.

BACKGROUND: One of the main causes of blindness in the world, diabetic retinopathy (DR) is a dangerous condition that impairs vision in diabetics. Preventing visual loss requires early recognition of DR and prompt treatments. Artificial intelligence (AI) software combined with nonmydriatic fundus cameras has demonstrated encouraging gains in DR screening effectiveness. However, there are not many ...

MedSlice: fine-tuned large language models for secure clinical note sectioning.

OBJECTIVES: Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. This study develops a pipeline for automated note sectioning using open-source large language models (LLMs), focusing on three sections: History of Present Illness, Interval History, and Assessment and Plan. MATE...

A modern look at simplicity bias in image classification tasks.

Simplicity bias (SB), the tendency of neural networks to learn simpler functions, is a key factor in good generalization. Recent studies have examined...

AI assisted, mentor-guided narrative review writing task for medical students, a novel educational strategy to enhance research and academic writing.

INTRODUCTION: The integration of artificial intelligence (AI) tools into medical education presents new opportunities for enhancing students' research...

An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language.

OBJECTIVE: Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrep...

Mitigating data center bias in cancer classification: Transfer bias unlearning and feature size reduction via conflict-of-interest free multi-objective optimization.

Bias in the decision-making processes of trained deep models poses a significant threat to their reliability. Such bias can lead to overoptimistic res...

Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities.

BACKGROUND AND OBJECTIVE: Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved out...

Inferring high-fat dietary patterns from electronic health record data using machine learning.

OBJECTIVES: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) ...

Are we ready for amyloid-related imaging abnormalities (ARIA) monitoring? A European survey among neuroradiologists and neurologists by the ESNR/EAN Working Group.

OBJECTIVES: Amyloid-lowering immunotherapies can cause amyloid-related imaging abnormalities (ARIA), requiring brain MRI for detection and monitoring....

Development of an automatic damage detection methodology using ultrasonic piezoelectric sensors under varying temperature conditions.

This article presents an experimental study of the effects of temperature variations on ultrasonic waves and proposes a methodology to improve the rob...

Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches.

BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lith...

Developing an artificial intelligence-powered question-and-answer chatbot with English-Spanish capabilities for new mothers.

OBJECTIVES: Generative AI chatbots are revolutionizing health education by making complex information more accessible to the public. However, their us...

Active surveillance as the treatment of choice for low-risk prostate cancer: Reliability of results obtained through clinical language processing systems and big data.

INTRODUCTION: Advances in natural language processing (NLP) technologies have gained prominence for extracting relevant clinical information. Savana i...

Machine learning prediction for identifying individuals requiring rotator cuff repair: Shoulder muscle strength and questionnaires as the related factors.

BackgroundRotator cuff tears, a common cause of shoulder pain, often require surgery when conservative treatment fails. Arthroscopic repair is standar...

Is chronic low back pain related to sleep disturbances? A self-organizing maps for unsupervised machine learning-based patient phenotyping.

BACKGROUND CONTEXT: Chronic low back pain (CLBP) is a multifactorial condition and a leading cause of disability worldwide. Among the various contribu...

AI at the Helm: Evaluating Claude 3.5 Sonet and ChatGPT-4.0 in Tympanoplasty Management.

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into health care, offering new possibilities for postoperative management. L...

The development of an image processing model to estimate tooth width and space requirements.

OBJECTIVES: The advent of machine and deep learning has raised the possibility of increased efficiency and indeed improved accuracy in relation to the...

Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.

BACKGROUND AND OBJECTIVE: This study introduces the liver cancer segmentator (LCS), a deep learning model designed for automatic and robust segmentati...

The effects of e-Health and artificial intelligence literacy levels on disease self-management in patients with diabetes.

BACKGROUND: This study was conducted to examine the effects of eHealth and artificial intelligence literacy on disease self-management in patients wit...

OpenFieldAI - new open-source AI based software for tracking rodents and training open field test models.

Open field test (OFT) is one of the widely used pre-clinical models for assessing the exploratory, locomotion and anxiety behavior of rodents. OFT par...