AIMC Topic: Humans

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Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

Ultrasound in medicine & biology
OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce the impact of the professional competence and personal bias of the diagnostician.

The digital Balint: using AI in reflective practice.

Education for primary care : an official publication of the Association of Course Organisers, National Association of GP Tutors, World Organisation of Family Doctors
Reflective practice is fundamental to postgraduate general practitioner (GP) training and ongoing professional development. However, real-world challenges like time constraints and professional isolation often limit meaningful engagement with this cr...

Influence of a robotic companion on women's food choices: Evidence from an imaginary task.

Applied psychology. Health and well-being
Previous research has demonstrated the influence of commensal dining between humans on food choices, whereas we conducted two studies to examine how the presence of a robot might influence people's choices between meat-heavy and vegetable-forward mea...

Zero- and few-shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials.

Research synthesis methods
Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of availabl...

Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning.

Movement disorders : official journal of the Movement Disorder Society
BACKGROUND: Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS).

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development.

Journal of chromatography. A
BACKGROUND: The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. ...

An optimal fast fractal method for breast masses diagnosis using machine learning.

Medical engineering & physics
This article introduces a fast fractal method for classifying breast cancerous lesions in mammography. While fractal methods are valuable for extracting information, they often come with a high computational load and time consumption. This paper demo...

Venous thromboembolism in the era of machine learning and artificial intelligence in medicine.

Thrombosis research
In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present ut...

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.

Computers in biology and medicine
By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on nor...

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

Annals of clinical and translational neurology
OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to d...