Leading the Way in Effective Drug Development
Mitigating Bias in AI: Holding Innovation to a Higher Standard
When it comes to building equitable, quality AI, prioritizing diverse data sets needs to be embedded in a developer’s DNA – rather than a “nice to have,” it should be a deliberate framework in which AI is built.
Why Diversity is one of AiCure’s Founding Principles
AI has great potential to transform drug development and patient care as we know it – from elevating a clinical trial’s data to speed up research, to supporting clinical decision-making to drive confident care.
AI in Clinical Research is On the Rise (and More from DIA 2021)
Each year, thousands of professionals from across the pharmaceutical industry come together to share data and ideas at the Drug Information Association (DIA) conference. This year, AiCure wanted to leverage the collective experience of those attending DIA to gain insights into how the industry views its evolving use of advanced machine learning and artificial intelligence (AI) technologies.
Personalization, Patients And Product Management — An Experiment In Caring
I’ve worked in product management for 20 years or so and I’ve grown accustomed to people not understanding what I do. I’m pretty sure my father in-law thinks I code websites. My friends think I build mobile apps. I stopped trying to explain my job to my wife and kids except in the vaguest terms.
Testing the Power of Predictive Analytics and AI
There are many reasons why patients might be nonadherent – whether it’s study fatigue, forgetfulness, or juggling other responsibilities and stressors. Despite the best efforts of site teams to identify the most qualified and motivated patients, ensuring treatment plan compliance over the course of an entire study has continued to impact data quality and study performance.
Using AI to Provide New Insights into Intentional Dose Non-Adherence
Inadvertent and intentional dose non-adherence in clinical trials has been a major drug development challenge for decades. Studies in the literature have demonstrated that a high percentage of study volunteers — as high as 75% — conceal or fabricate information and provide inaccurate self-reports of adherence.