AI Medical Compendium

Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.

Journal of cheminformatics May 20, 2026

Accurately characterizing protein-ligand binding, such as binding site, affinity and kinetics, is critical for accelerating drug discovery. However, many existing computational methods face key limitations, including insufficient integration of compr... read more

Assessing Extracellular Vesicle Proteins as Predictive Biomarkers for Developing Type 1 Diabetes.

Proteomics May 20, 2026

Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for ide... read more

Diagnostic performance of artificial intelligence in fracture detection with ultrasound: A systematic review and meta-analysis.

The ultrasound journal May 20, 2026

BACKGROUND: Bone fractures are common in acute care, and point-of-care ultrasound (POCUS) is an emerging diagnostic tool that can be complementary to or even in some cases an alternative to X-ray imaging. With the rise of artificial intelligence (AI)... read more

Targeting tumor-associated G-protein coupled receptors: beyond single-axis inhibition toward multidimensional regulation.

Cellular oncology (Dordrecht, Netherlands) May 20, 2026

G protein-coupled receptors (GPCRs) serve as central hubs in tumor signal transduction and microenvironment regulation. However, their therapeutic exploitation is confounded by a fundamental complexity: GPCR functions are exquisitely context-dependen... read more

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

Japanese journal of radiology May 20, 2026

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-label and three-label strategies for metastatic differentiated thyroid carcinoma (DTC) on FDG PET/CT i... read more

Large language models and large multimodal models in radiology: opportunities, challenges, and the path toward sustainable long-term clinical integration.

Japanese journal of radiology May 20, 2026

Large language models (LLMs), built on transformer architecture, have emerged as a fundamental tool in natural language processing and contextual reasoning, and have been extended to multimodal data interpretation, which has been termed large multimo... read more

Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics.

European radiology experimental May 20, 2026

OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant osteosarcoma using baseline clinical and magnetic resonance (MRI)-derived radiomics features, with his... read more

Comparative evaluation of treatment recommendations generated by generative AI and breast cancer specialists for advanced and recurrent breast cancer: a multidimensional assessment of evidence interpretation and clinical decision support.

Breast cancer (Tokyo, Japan) May 20, 2026

BACKGROUND: Large language models (LLMs), a form of generative artificial intelligence (AI), are increasingly explored for clinical applications due to their ability to synthesize medical information. In breast cancer care, where therapeutic decision... read more

Model-informed vancomycin precision dosing by population pharmacokinetics combined with machine learning algorithms.

British journal of clinical pharmacology May 20, 2026

AIMS: Achieving the target AUC/MIC remains a critical challenge in vancomycin therapeutic drug monitoring, with traditional empirical dosing regimens often leading to suboptimal outcomes. This study aimed to develop and validate a novel population ph... read more

Development of an integrated multidimensional nomogram for predicting stone-free status after retrograde intrarenal surgery: a machine learning-based approach.

Minerva urology and nephrology May 20, 2026

BACKGROUND: This study aimed to develop and validate a novel preoperative nomogram to predict stone-free status (SFS) in patients undergoing retrograde intrarenal surgery (RIRS) for kidney stones. METHODS: A total of 312 patients who underwent RIRS w... read more

Artificial Intelligence Medical Compendium