AiCure articles

Since AiCure was founded, diversity has been a grounding principle in all that we do – from diversity in our datasets, to diversity in the team behind our solutions. Early on, we realized opening our doors to diverse perspectives and investing in their fulfillment means we can build products that are more inclusive for their customers and create a workplace that best suits the needs of all employees. 

Many women in science, technology, engineering and mathematics (STEM) fie

From clinical trial enrollment through completion, understanding medication adherence is critical to data integrity and AiCure offers a competitive edge where traditional methods fall short. 

To ensure trial participants are complying with dosing regimens, sites often rely on traditional patient reported outcome systems with participant diaries, which can be unreliable. Also in common use are dated technologies such as electronic pill caps and reliable, but often delayed, pharmac

Clinical trials are often designed with the participant’s everyday life in mind – trying to gather as much clinically-relevant data in a controlled manner as possible while allowing individuals to live their regular lives. However, it is true that trials are artificial environments, and participants are actively monitored to ensure their safety and to determine the efficacy of a drug in development. Study participants are expected to remain engaged throughout the trial’s dur

The ability to predict how a clinical trial participant will adhere to their treatment, and even respond to that treatment, has great promise to advance patient-centric clinical research. But, as we look to understand the impact a drug has on patients’ everyday health outside of a research environment, what happens to these unique, predictive findings around dosing and outcomes once a drug enters the real-world? 

As real-world evidence and a deeper understanding of a patien

For decades, data scientists and AI developers have typically followed the belief that “bigger is better” – the bigger the data set, the more analytical freedoms one has, and the more insights one gains. The AI industry still heavily relies on big data analytics, and for good reason – we need to feed AI large amounts of high quality, diverse data to ensure it works effectively for its intended patient population. But, as we strive for precision medicine and advance cli