AI Medical Compendium

Clinical learning experiences and artificial intelligence-related anxiety among midwifery students: a cross-sectional study.

BMC medical education Jun 3, 2026

BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare practice and education, requiring students to adapt to technology-supported clinical environments. Although AI-related anxiety has been examined among nursing students, littl... read more

Accurate prediction of activity cliff compounds based on bioactivity profiles depends on assay nearest neighbor relationships.

Journal of cheminformatics Jun 3, 2026

The definition of activity cliffs (ACs) depends on compound similarity and activity difference criteria and on activity data types. ACs are usually defined as pairs or groups of structurally similar compounds or structural analogues that are active a... read more

Integrating multimodal features with deep learning for protein solubility prediction.

Journal of cheminformatics Jun 3, 2026

Protein solubility prediction holds significant importance in the fields of biotechnology and medicine. With the continual advancements of computational and experimental techniques such as protein design, enzyme mining, and directed evolution, accura... read more

Susceptibility of large language models to hidden nudge injection during simulated medical peer review: a quasi-experimental study.

Research integrity and peer review Jun 3, 2026

BACKGROUND: Generative artificial intelligence (AI) technologies might offer new possibilities for the peer review process; however, AI models' possible vulnerability to hidden nudges designed to elicit positive reviews raises concerns about manipula... read more

Interpretable machine learning for cattle breed classification and SNP prioritization.

Genetics, selection, evolution : GSE Jun 3, 2026

BACKGROUND: The conservation of endangered cattle breeds is an important priority for maintaining biodiversity and keeping unique genetic resources. Traditional conservation methods are often not precise enough for accurate classification into closel... read more

Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.

BMC gastroenterology Jun 3, 2026

OBJECTIVE: To systematically evaluate and quantify the diagnostic accuracy and performance of machine ML techniques for the detection of NAFLD, and to compare the performance of ML when assisting different diagnostic modalities, in order to provide t... read more

Utility of deep learning for degree calculation of aortic arch calcification in chest-X ray.

BMC medical imaging Jun 3, 2026

BACKGROUND: Aortic arch calcification (AoAC) is commonly classified into four grades according to the percentage of calcification observed in clinical practice, and the interpretation is typically based on visual assessment by clinicians. However, th... read more

Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.

BMC medical imaging Jun 3, 2026

Brain tumor segmentation requires precise delineation of hierarchical structures from multi-sequence MRI. However, existing deep learning methods primarily rely on visual features, showing insufficient discriminative power in ambiguous boundary regio... read more

SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.

BMC medical informatics and decision making Jun 3, 2026

PURPOSE: Accurately predicting emergency department (ED) revisits within 72 hours remains challenging due to irregular and short patient visit histories. This study investigates how temporal representations of historical ED utilization and sequential... read more

A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.