Clinical Trials

OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler.

The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery processes across diverse domains. However, the development of an operating system for material acceleration platform faces challenges in simultaneously managing diverse experiments from multiple users. Specifically, when it is utilized by multiple us...

Machine learning integration with response surface methodology to enhance the removal efficacy of arsenate (V) through sulfur-functionalized mxene coated QPPO/PVA AEM.

Arsenic, a poisonous and carcinogenic heavy metal in drinking water, presents severe health risks to humans, including skin lesions, neurological damage, and circulatory disorders. Despite extensive research efforts have been carried out on removing arsenate (As(V)) using membrane technologies, however, there remains a critical need to further enhance membrane removal efficacy to achieve maximum r...

Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However...

Integrating AI into Breast Reconstruction Surgery: Exploring Opportunities, Applications, and Challenges.

Background: Artificial intelligence (AI) has significantly influenced various sectors, including healthcare, by enhancing machine capabilities in assi...

Use of Text-to-Image Artificial Intelligence Model in Preoperative Counseling for Lip-Lift Procedures.

Text-to-image models powered by artificial intelligence offer a promising tool for enhancing patients' comprehension of cosmetic surgery outcomes and ...

Artificial intelligence in clinical practice: Quality and evidence.

A revolution is taking place within the field of artificial intelligence (AI) with the emergence of generative AI. Although we are in an early phase a...

Artificial Intelligence (AI)-Based simulators versus simulated patients in undergraduate programs: A protocol for a randomized controlled trial.

BACKGROUND: Healthcare simulation is critical for medical education, with traditional methods using simulated patients (SPs). Recent advances in artif...

The effect of the use of artificial intelligence in the preparation of patient education materials by nursing students on the understandability, actionability and quality of the material: A randomized controlled trial.

AIM: This study was conducted to examine the effect of nursing students' use of artificial intelligence (AI) tools while preparing patient education m...

Managing Postembolization Syndrome Through a Machine Learning-Based Clinical Decision Support System: A Randomized Controlled Trial.

Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome i...

Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.

There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapi...

Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows.

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers p...

Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.

This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) ...

Enhancing self-directed learning with custom GPT AI facilitation among medical students: A randomized controlled trial.

OBJECTIVE: This study aims to assess the impact of LearnGuide, a specialized ChatGPT tool designed to support self-directed learning among medical stu...

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typic...

Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a...

Enhanced drug classification using machine learning with multiplexed cardiac contractility assays.

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotox...

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

UNLABELLED: The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine le...

Multi-label material and human risk factors recognition model for construction site safety management.

INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practi...

Learning to match patients to clinical trials using large language models.

OBJECTIVE: This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrie...

Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.

Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by info...