AI Medical Compendium

Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.

Neurocritical care Feb 3, 2026

Stroke-associated pneumonia (SAP) is a frequent and severe complication following stroke. Recently, several machine learning (ML) models have been developed to predict SAP. We aimed to evaluate the predictive performance of these models in SAP predic... read more

Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.

Drug delivery and translational research Feb 3, 2026

Inclusion of physiologically relevant clearance mechanisms into organ-on-a-chip models is essential to reproduce tissue exposure and predict therapeutic efficacy, especially for local therapies and drug delivery applications that are already common i... read more

Automatic processing of gastrointestinal endoscopy referrals and patient instructions using large language models.

BMC gastroenterology Feb 3, 2026

Prediction of recurrent ischemic stroke using machine learning from real-world data.

BMC medical research methodology Feb 3, 2026

BACKGROUND: Recurrent ischemic stroke (RIS) is a significant challenge in Malaysia, affecting approximately 33% of patients. However, studies using artificial intelligence (AI) to predict this event using real-world data remain very limited. This stu... read more

Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.

BMC medical informatics and decision making Feb 3, 2026

Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.

BMC endocrine disorders Feb 3, 2026

Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.

BMC medical informatics and decision making Feb 3, 2026

Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.

Journal of cheminformatics Feb 3, 2026

Limited experimental data remains a key challenge in applying machine learning to drug discovery, particularly for cancer-related targets. In this study, we present a data-efficient active meta-deep learning framework to predict mitogen-activated pro... read more

Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.

BMC medical informatics and decision making Feb 3, 2026

Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models.

BMC medical informatics and decision making Feb 3, 2026

Artificial Intelligence Medical Compendium

Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.

Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.

Automatic processing of gastrointestinal endoscopy referrals and patient instructions using large language models.

Prediction of recurrent ischemic stroke using machine learning from real-world data.

Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.

Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.

Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.

Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.

Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.

Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models.

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