AIMC Topic: Humans

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Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI.

Sensors (Basel, Switzerland)
In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language models within the field of human-robot interaction and human-computer interaction. While research in...

Role of artificial intelligence-powered conversational agents (chatbots) in musculoskeletal disorders: a scoping review protocol.

BMJ open
INTRODUCTION: Musculoskeletal disorders (MSDs) represent a significant global health burden that leads to substantial disability with socioeconomic impact. With the rise of artificial intelligence (AI), particularly large language model-driven conver...

Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and Attention-Deficit/Hyperactivity Disorder With Psychological Test Reports.

Journal of Korean medical science
BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD). However, these reports can have several pr...

Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.

Italian journal of pediatrics
BACKGROUND: Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure they receive all the essential nutrients for good health. Unfortunately, many children in Ethiopia ...

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.

BMC medical informatics and decision making
BACKGROUND: Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, endoscopic intervention is the mainstay treatment for hemostasis in high-risk rebleeding lesions. Pr...

Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

BMC medical imaging
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly...

Understanding EMS response times: a machine learning-based analysis.

BMC medical informatics and decision making
BACKGROUND: Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning...

Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).

BMC medical informatics and decision making
OBJECTIVE: In the functional assessment of the esophagogastric junction (EGJ), the endoscopic Hill classification plays a pivotal role in classifying the morphology of the gastroesophageal flap valve (GEFV). This study aims to develop an artificial i...

Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.

BMC medical imaging
BACKGROUND: Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.