Prevention of medical errors

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

BACKGROUND: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinic...

Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.

BACKGROUND: Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lung cancer.

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.

Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these...

A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.

This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior...

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

INTRODUCTION: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinic...

AI-generated cancer prevention influencers can target risk groups on social media at low cost.

BACKGROUND: This study explores the potential of Artificial Intelligence (AI)-generated social media influencers to disseminate cancer prevention mess...

The promise of AI in healthcare: transforming communication and decision-making for patients.

By addressing communication gaps, the integration of AI tools in healthcare has a greater ability to improve decision-making and to empower patients w...

Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.

OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. O...

Machine Learning-Aided Diagnosis Enhances Human Detection of Perilunate Dislocations.

BACKGROUND: Perilunate/lunate injuries are frequently misdiagnosed. We hypothesize that utilization of a machine learning algorithm can improve human ...

An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimiz...

Integrating Artificial Intelligence in Medical Writing: Balancing Technological Innovation and Human Expertise, with Practical Applications in Lower Extremity Wounds Care.

Artificial Intelligence (AI) is revolutionizing medical writing by enhancing the efficiency and precision of healthcare communication and health resea...

Artificial Intelligence-Driven Translation Tools in Intensive Care Units for Enhancing Communication and Research.

UNLABELLED: There is a need to improve communication for patients and relatives who belong to cultural minority communities in intensive care units (I...

Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.

The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknes...

Evaluation of Multilingual Simplifications of IR Procedural Reports Using GPT-4.

This study assessed the feasibility of large language models such as GPT-4 (OpenAI, San Francisco, California) to summarize interventional radiology p...

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indi...

Artificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which ca...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...

OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging.

BACKGROUND: Knee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary scr...

Imagining alternative futures with augmentative and alternative communication: a manifesto.

This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are ma...