Prevention of medical errors

Tumor detection on bronchoscopic images by unsupervised learning.

The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians with insufficient experience may lead to misdiagnosis or misjudgment of tumors. To address this issue, a datasets for intratracheal tumor detection has been constructed to simulate the diagnostic level of experienced specialists, and a Knowledge Dis...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention of suicide. The purpose of this review was to explore the use of LLM tools in various aspects of suicide prevention. PubMed, Embase, Web of Science, ...

Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions.

Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical ne...

Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management.

Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performa...

Learnable color space conversion and fusion for stain normalization in pathology images.

Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such st...

Mask R-CNN assisted diagnosis of spinal tuberculosis.

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to m...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects t...

Reimagining AAC designs for children during dynamic social situations by leveraging smart device design.

Social interaction plays a pivotal role in human development, influencing cognitive, emotional, and communicative growth across all stages of life. In...

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.

Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding prag...

Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.

Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real ...

Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial.

OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide preventio...

Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models.

Effective communication of government policies to citizens is crucial for transparency and engagement, yet challenges such as accessibility, complexit...

Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.

In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recog...

Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education.

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Proce...

Ethical Application of Generative Artificial Intelligence in Medicine.

Generative artificial intelligence (AI) may revolutionize health care, providing solutions that range from enhancing diagnostic accuracy to personaliz...

Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.

The medical metaverse is a combination of medicine, computer science, information technology and other cutting-edge technologies. It redefines the met...

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention.

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors li...

Integrating GPT-Based AI into Virtual Patients to Facilitate Communication Training Among Medical First Responders: Usability Study of Mixed Reality Simulation.

BACKGROUND: Training in social-verbal interactions is crucial for medical first responders (MFRs) to assess a patient's condition and perform urgent t...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. ...

From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management.

The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issu...