Artificial Intelligence Medical Compendium

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

Showing 48,391 to 48,400 of 224,199 articles

Whose values are driving the design of robot swarms for humanitarian aid?

Medical humanities Feb 20, 2026
Robot swarms hold significant promise for humanitarian aid, offering scalable, autonomous solutions for search and rescue, aid delivery, and disaster response. Their ability to self-organise, adapt, and operate in hazardous or inaccessible environmen... read more 

[Drug repositioning prediction based on dynamic feature learning on heterogeneous graphs].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Feb 20, 2026
OBJECTIVES: To address the challenges faced by existing artificial intelligence methods in modeling complex heterogeneous biological networks, particularly their limitations in capturing collaborative relationships between nodes and in extracting hig... read more 

[Dual role of tea consumption in gastrointestinal disease risks: analysis using a risk prediction model integrating interpretable machine learning and large language model].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Feb 20, 2026
OBJECTIVES: To explore the correlation of tea consumption with risks of gastrointestinal diseases using a risk prediction model integrating interpretable machine learning and a large language model. METHODS: A survey was conducted among the patients ... read more 

Response to Pereañez et al.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists Feb 20, 2026
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Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence.

The Journal of clinical endocrinology and metabolism Feb 20, 2026
CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the "FSH window" and induce multifollicular growth. However, only limited data exist that examine individual follicula... read more 

Assessment of GFR Estimation Methods in Patients With Obesity.

The Journal of clinical endocrinology and metabolism Feb 20, 2026
CONTEXT: Obesity is an independent risk factor for chronic kidney disease, and accurate estimation of the glomerular filtration rate (GFR) is crucial. However, limited data are available on the performance of the European Kidney Function Consortium (... read more 

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

The Journal of clinical endocrinology and metabolism Feb 20, 2026
CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However, recurrence or failure to achieve remission remains relatively frequent, underscoring the need for ... read more 

Facial Analysis in Acromegaly Using Machine Learning: Toward Earlier Diagnosis.

The Journal of clinical endocrinology and metabolism Feb 20, 2026
CONTEXT: Acromegaly is a rare and progressive disorder often diagnosed late due to its insidious onset and gradually evolving facial features. Early detection remains a critical unmet need to reduce disease-associated morbidity and mortality. OBJECTI... read more 

Multi-Score Reinforcement Learning for High-Tg Polyimide Design.

Journal of chemical information and modeling Feb 20, 2026
This study explores strategies to guide the generation of polyimides with high glass transition temperatures (Tg > 750 K) through reinforcement learning. We present a systematic computational framework for analyzing and combining multiple scoring fun... read more 

Next-generation computational strategies for neurodegenerative biomarkers: Multi-omics integration, AI, and molecular modeling.

Computational biology and chemistry Feb 20, 2026
Neurodegenerative diseases (NDs) are progressively debilitating conditions driven by complex molecular perturbations and selective neuronal loss. Conventional approaches to discovering biomarkers, using single-omics or empirical screening, often fail... read more 
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