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Explore the latest research on artificial intelligence and machine learning in medicine.

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Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal of thoracic imaging
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...

Supervised and unsupervised learning reveal heroin-induced impairments in astrocyte structural plasticity.

Science advances
Astrocytes regulate synaptic activity across large brain territories via their complex, interconnected morphology. Emerging evidence supports the involvement of astrocytes in shaping relapse to opioid use through morphological rearrangements in the n...

Assessing the diagnostic accuracy of ChatGPT-4 in the histopathological evaluation of liver fibrosis in MASH.

Hepatology communications
BACKGROUND: Large language models like ChatGPT have demonstrated potential in medical image interpretation, but their efficacy in liver histopathological analysis remains largely unexplored. This study aims to assess ChatGPT-4-vision's diagnostic acc...

Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety.

BMC psychology
As artificial intelligence (AI) technology quickly grows, college students have new worries and fears. Using Marx's theory of labour alienation, this study explores the complex relationship between college students' job-seeking anxiety (JSA) and care...

Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.

BMC medicine
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.

Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

BMC surgery
BACKGROUND: Clinically relevant postoperative pancreatic fistula (CR-POPF) following laparoscopic pancreaticoduodenectomy (LPD) is a critical complication that significantly worsens patient outcomes. However, the heterogeneity of its risk factors and...

Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.

BMC cancer
BACKGROUND: Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most impo...

Factors influencing short-term and long-term survival rates in stroke patients receiving enteral nutrition: a machine learning approach using MIMIC-IV database.

BMC neurology
PURPOSE: This study aims to evaluate the survival and mortality rates of stroke patients after receiving enteral nutrition, and to explore factors influencing long-term survival. With an aging society, nutritional management of stroke patients has be...

Automated extraction of functional biomarkers of verbal and ambulatory ability from multi-institutional clinical notes using large language models.

Journal of neurodevelopmental disorders
BACKGROUND: Functional biomarkers in neurodevelopmental disorders, such as verbal and ambulatory abilities, are essential for clinical care and research activities. Treatment planning, intervention monitoring, and identifying comorbid conditions in i...