Latest AI and machine learning research in surveys for healthcare professionals.
BACKGROUND/AIMS: This study aimed to develop a multimodal artificial intelligence (AI) system that integrates fundus imaging and patient questionnaire data to achieve clinician-level diagnostic accuracy for diagnosing retinal detachment (RD). METHODS: Ultra-widefield fundus images and comprehensive patient questionnaires were collected from patients with RD and healthy controls at Tsukazaki Hospit...
BACKGROUND: Clinicians are the interface between artificial intelligence (AI) applications and patient care. To maximize benefits and minimize risks of AI, clinicians must be "AI-ready"-that is, willing and able to understand, evaluate, and appropriately use AI tools in practice. Prior literature suggests that clinicians lack fundamental competencies in the use of AI. These gaps could be especiall...
BACKGROUND: Behavioral health concerns are common in pediatric practice, with pediatricians reporting a lack of skills related to providing effective ...
BACKGROUND: Technological advancements and widespread internet connectivity have fundamentally transformed data collection across academic disciplines...
INTRODUCTION: Artificial intelligence (AI) has demonstrated transformative potential in medical education and assessment, with large language models a...
OBJECTIVE: To comparatively evaluate the reliability, quality, and readability of responses generated by widely used large language model (LLM)-based ...
BACKGROUND: Oral medications are commonly used in the treatment of breast cancer (BC), despite high rates of nonadherence. As adherence is fundamental...
BACKGROUND: With the growing aging population, technology that supports independent living is increasingly important. Web search systems are well esta...
BACKGROUND: Despite the growing emphasis on open science and equity in research, qualitative data capturing diverse human experiences and perspectives...
Magnetic Resonance Imaging (MRI) offers superior soft tissue contrast compared to Computed Tomography (CT), making it highly valuable in external beam...
Dental age estimation plays a critical role in clinical and forensic applications. Because teeth are highly resistant to environmental degradation, de...
BACKGROUND: Predicting mortality among people living with HIV enables clinicians to implement timely, targeted, and preventive interventions at the st...
BACKGROUND: Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been...
We present a universal modular deep-learning framework and demonstrate its application to low-latency, streaming-compatible heart rate variability (HR...
BACKGROUND: Athletes frequently experience potentially traumatic events related to sports injuries, significantly increasing their risk of developing ...
Against the backdrop of accelerated reconstruction of the design-education ecosystem by artificial intelligence, this study focuses on the core issue ...
BACKGROUND: Artificial intelligence (AI) supported imaging is increasingly used across plastic surgery, including burn care, to assist with assessment...
BACKGROUND: Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning to...
BACKGROUND: Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality th...
PURPOSE: Convolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi-b DW images wit...