Artificial Intelligence Medical Compendium

A universal design framework for high-performance catalysts remains challenging due to diverse structures and active sites. We developed a framework integrating weighted atom-centered symmetry function (wACSF) descriptors with machine learning, micro... read more 

PurposeTo evaluate the utility of CataractBot, an LLM (Large Language Model)-powered chatbot that provides doctor-verified answers to patient questions about cataract surgery. We examine its use by both end-users (patients and attendants) and medical... read more 

OBJECTIVES: To compare machine learning models using different combinations of clinical and imaging variables for classifying ischemic stroke patients as having an onset-to-imaging (OTI) time within or beyond 4.5 h. MATERIALS AND METHODS: We analyzed... read more 

OBJECTIVE: The goal of this study was to curate a prostate MRI dataset from a screening population and to train and evaluate a deep-learning segmentation method on the same data. MATERIALS AND METHODS: An artificial intelligence (AI) system, based on... read more 

OBJECTIVE: Diagnostic reference levels (DRLs) are essential for optimizing radiation dose in CT examinations. However, current DRLs may not reflect the performance of CT scanners or the background of individual facilities, potentially leading to misl... read more 

PURPOSE: To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. METHODS: Pseudo-fully sampled neurovascular 4D flow data (N = 40) and a simulation framework were used to learn phase encoding location... read more 

A major barrier to effective therapy is the limited water solubility of many Biopharmaceutics Classification System (BCS) Class II and IV medications, which results in poor bioavailability and inconsistent patient outcomes. Better solubilization and ... read more 

BACKGROUND: This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [18F]PSMA-1007 PET/CT to predict extracapsular extension (ECE) in prostate cancer (PCa), and to explore easy models integrating DL... read more 

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) provides a standardised framework for thyroid fine needle aspiration cytology (FNAC). Deep learning approaches, particularly transfer learning, have shown potential for cyto... read more 

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensit... read more