Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.
|Articles|June 4, 2020
Flywheel Launches Medical Imaging Solution for Life Sciences
Advertisement
Flywheel has announced its launch of Flywheel for Life Sciences, an enterprise-scale research data management solution helping organizations realize their digital transformation strategy and improve R&D efficiency. Features include high speed bulk loading, horizontal and vertical scaling, and more.
For more information, click
Advertisement
Related to this article
In this video interview, Carole Berini, PhD, of Ontada, explains how validated proxies for variables like ECOG performance status can recover not just sample size but the representativeness of hard-to-reach patient populations in real-world oncology studies.
A biomarker test now decides who enters a precision medicine trial, yet results often go unused and assays are not ready when screening opens.
In today's ACT Brief, we examine proportionate analysis frequency in RBQM, how to build trust into real-world evidence foundations for cancer trials, and how rare disease developers monetize regulatory success.
Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.
In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.
In today's ACT Brief, we examine how AI surfaces design flaws before enrollment, why site training readiness depends on learning science, and the priorities defining next-generation weight-loss therapies.
Site training effectiveness depends on readiness at first-patient-in, requiring learning-science design principles around performance focus, cognitive load reduction, progressive sequencing, and spaced retrieval practice rather than training completion metrics or content volume.
Advertisement
Advertisement
Trending on Applied Clinical Trials Online
1
Real-Time Data, Right-Time Decisions: Rethinking Analysis Frequency in RBQM
2
What It Takes to Harmonize AI Across Fragmented Oncology Health Systems
3
FDA Releases Transparency Roadmap Outlining Disclosure Priorities Across Drug Development and Regulatory Review
4
ACT Brief: FDA Transparency Roadmap, Radiopharmaceutical Trial Operations, and AI-Powered Enrollment Prediction
5