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

Digital biomarkers can open a world of possibilities in understanding the nuances of a patient’s behavior and response to treatment. Rather than relying on subjective perceptions of how a patient is responding, digital biomarkers treat patient observation more like an engineering problem. Such precise, sensitive measurements can ultimately influence the future of a patient’s care plan or even the future of a trial. But, just like all AI-powered tools, this potential relies on thei

Imagine you’ve just been diagnosed with cancer and you’re enrolled in a clinical trial for a promising new drug. You notice that after chemotherapy you struggle with occasional fatigue. Yet, whenever you visit your doctor every couple of weeks, you feel fine. This is not only a frustrating experience for patients, but it can also compromise the safety data in a clinical trial. The good news is that we may be able to solve this problem with open-source artificial intelligence (AI)

The introduction of Good Machine Learning Practices (GMLP) and increasing buzz around the need for transparency and standardization of machine learning (ML) are significant steps to encourage adoption and trust in these tools across the healthcare industry. To do so, though, requires shifting these ideals from mere concepts into actionable

Machine learning (ML) innovation in healthcare is growing, and the oversight on its development should keep pace to ensure it’s developed in a scientifically sound, safe way. Just as the FDA requires detailed ingredients on the side of cereal boxes to help consumers make informed health decisions, the same transparency should apply to the ML technology our patients and clinicians use every day to make informed care decisions. Instead of nutrition facts, ML should include proof that

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.   Before deploying an algorithm to patients, it should be rigorously evaluated under both common and rare scenarios to ensure it performs as intended and sufficiently takes into account the variety of real-world populations. In order for AI innovation to a