AI Medical Compendium

Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.

International journal of obesity (2005) Jun 1, 2026

Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked to type 2 diabetes and obesity. It creates new opportunities to shift from reactive to proactive, da... read more

Artificial Intelligence-Augmented Mixture Toxicology: Reframing Unresolved Risk at Camp Lejeune.

Toxicological sciences : an official journal of the Society of Toxicology Jun 1, 2026

Persistent uncertainty regarding health risks associated with contaminated drinking water at Camp Lejeune reflects a broader limitation in toxicology: The difficulty of evaluating complex chemical mixtures in genetically heterogeneous populations. Hi... read more

Application of delta radiomics based on cone-beam computed tomography in predicting radiotherapy efficacy for nasopharyngeal carcinoma.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Jun 1, 2026

PURPOSE: To investigate the value of cone-beam computed tomography (CBCT)-based delta radiomics for predicting short-term radiotherapy (RT) response in nasopharyngeal carcinoma (NPC). METHODS: A total of 132 pathologically confirmed NPC patients rece... read more

From traditional classroom to AI-enhanced flipped classroom: a three-year pedagogical evolution for international students in pharmacology.

BMC medical education Jun 1, 2026

BACKGROUND: Pharmacology education faces challenges in developing higher-order thinking, especially for international students to overcome cross-cultural barriers. While flipped classrooms enhance interaction, their learning optimization remains unce... read more

Construction of liver organoid models by hepatobiliary differentiation from human induced pluripotent stem cells: state of the art, challenges and improving strategies.

Stem cell research & therapy Jun 1, 2026

Physiologically relevant liver models are essential for advancing hepatic disorder research, especially for disease modeling and drug development, yet current in vitro systems fail to adequately recapitulate the architecture and function of the liver... read more

A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.

BMC anesthesiology Jun 1, 2026

BACKGROUND: Deep learning integrated with ultrasound systems may assist in predicting difficult airway, a life-threatening complication in anesthesia. The study aimed to assess the feasibility of AI-based ultrasound for predicting difficult video lar... read more

Predicting adolescent suicidal tendency in Chinese secondary school students: a machine learning approach with XGBoost and SHAP interpretation.

BMC public health Jun 1, 2026

BACKGROUND: Adolescent suicide is a critical public health issue globally. Early detection of suicidal tendency remains challenging due to its concealed and multidimensional nature. This study aimed to develop and validate an interpretable machine le... read more

Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a large-scale Middle Eastern study.

BMC medical imaging Jun 1, 2026

BACKGROUND: Multiple sclerosis (MS) affects 2.8 million individuals worldwide, with Middle Eastern populations remaining underrepresented in neuroimaging research despite elevated regional prevalence rates. Large-scale comparative studies between MS ... read more

Comparative analysis of generative pre-trained transformers for text- and image-based cephalometric prompts using a novel Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI).

BMC medical informatics and decision making Jun 1, 2026

INTRODUCTION: The aim of this study was to compare the response capabilities of generative pre-trained transformers (GPTs) for identical lateral cephalograms (LCs) based on text- and image-based prompts, using the newly developed Artificial Intellige... read more

Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.

BMC health services research Jun 1, 2026

BACKGROUND: Empirical evidence on how employees across different service domains within a single multi-sector public organization perceive AI adoption is limited. This study, therefore, examined differences in self-reported AI usage, perceived compet... read more

Artificial Intelligence Medical Compendium