AiCure articles

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

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

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

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)

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