Latest AI and machine learning research in clinical trials for healthcare professionals.
Ninety percent of drugs fail during clinical trials, mainly due to lack of clinical efficacy. Recent developments in in vitro models such as 3D tumor heterospheroids have led to improvements in failure rates, but the relative lack of standardized evaluation methods for 3D cultures limits their utility in high-throughput screening. Optical coherence tomography (OCT) shows significant promise for hi...
BACKGROUND: Early discontinuation (ED) in clinical trials (CTs) is frequent and deleterious for the patients, the care team, and the study duration. ED comprises screening failure or discontinuation during the first month of the treatment phase, and is often difficult to predict by clinicians. We aim at predicting ED by automatic analysis of patient's clinical record using language models (LMs). M...
BACKGROUND: Anxiety disorders are highly prevalent among adults with autism, with 20%-65% experiencing at least one diagnosable anxiety disorder. Whil...
BACKGROUND: AI is increasingly used to support clinical diagnosis, but the appropriate allocation of responsibility between clinicians and AI remains ...
BACKGROUND: Web-based surveys involving self-reported questionnaires are vulnerable to fraudulent responses. Advancements in artificial intelligence a...
BACKGROUND: Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather...
OBJECTIVES: Hospital artificial intelligence (AI) is increasingly embedded in electronic health record workflows, cloud inference pipelines, imaging, ...
Artificial intelligence is rapidly entering clinical practice, yet many physicians-especially those in solo or small-group settings-lack the guidance ...
The replacement of animal testing in cosmetic safety evaluation remains an urgent scientific and regulatory imperative. Next Generation Risk Assessmen...
BACKGROUND: Rapid evidence synthesis during emerging infectious and re-emerging disease outbreaks is critical, yet traditional systematic reviews rare...
BACKGROUND: Despite the rapid growth of digital entrepreneurship and increasing adoption of artificial intelligence (AI), existing explanations of dig...
PURPOSE: POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase 3 trial comparing first-line durvalumab with or without tremelimumab i...
Clinicians currently lack practical tools to quantify muscle-tendon forces outside of research laboratories, limiting load-management decisions during...
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a poor prognosis. The absence of effective targeted therapies and end...
INTRODUCTION: Although deep learning methods for EEG analysis are rapidly advancing, architectures developed for human multichannel recordings may not...
Multiple sclerosis (MS) is marked by heterogeneous disease activity, progression, and therapeutic response. Here, we developed a prognostic score base...
Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that...
BackgroundThe increasing integration of artificial intelligence (AI) in high-risk industries has transformed occupational processes; however, its impa...
BACKGROUND: Generative artificial intelligence (GenAI), particularly large language models (LLMs), is being integrated into healthcare documentation, ...
BACKGROUND: Traditional simulation-based nursing education is often constrained by high costs, resource intensity, and limited scalability. AI-powered...