AIMC Topic: Adult

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AI versus human-generated multiple-choice questions for medical education: a cohort study in a high-stakes examination.

BMC medical education
BACKGROUND: The creation of high-quality multiple-choice questions (MCQs) is essential for medical education assessments but is resource-intensive and time-consuming when done by human experts. Large language models (LLMs) like ChatGPT-4o offer a pro...

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Scientific reports
Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed u...

Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.

Chinese medical journal
BACKGROUND: Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN a...

Electrophysiological markers of adaptive co-representation in joint language production: Evidence from human-robot interaction.

Quarterly journal of experimental psychology (2006)
This study aimed to assess the extent to which human participants co-represent the lexico-semantic processing of a humanoid robot partner. Specifically, we investigated whether participants would engage their speech production system to predict the r...

Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.

Investigative radiology
OBJECTIVES: Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resol...

Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Journal of clinical monitoring and computing
Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO may improve neurological and non-neurological outcomes. To manipulate rSO an understanding of the variables that drive its...

Artificial Intelligence Job Substitution Risks, Digital Self-efficacy, and Mental Health Among Employees.

Journal of occupational and environmental medicine
OBJECTIVE: Artificial intelligence (AI) becomes increasingly integrated into the workplace, its associated job substitution risks for employees are more evident, resulting in significant repercussions for their well-being. This study tries to elucida...

Thessaly Graft Index: An Artificial Intelligence-Based Index for the Assessment of Graft Integrity in ACL-Reconstructed Knees.

The Journal of bone and joint surgery. American volume
BACKGROUND: Magnetic resonance imaging (MRI) has proven to be a valuable noninvasive tool to evaluate graft integrity after anterior cruciate ligament (ACL) reconstruction. However, MRI protocols and interpretation methodologies are quite diverse, pr...

Enhanced EEG-based cognitive workload detection using RADWT and machine learning.

Neuroscience
Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, brain-computer interfaces, and cognitive mon...