10 Articles found
AiCure Articles
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Medication Adherence Data Used Proactively in Clinical Trials
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 ...
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Driving Data-driven Medicine to Advance Personalized Care
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 ...
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Bridging Clinical Research to Real-World Patient Care Through Predictive Analytics
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 ...
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From Predicting Adherence to Health Outcomes – The Power of Predictive Insights
Clinical researchers have begun adopting predictive analytics to anticipate a patient’s engagement in a clinical trial, including if and how a patient will adhere to their treatment plan. As these models are fed more patient-level audio and ...
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Big, Small & Wide: Combining Old and New Data Approaches to Advance Precision Medicine
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 ...
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Open-Source AI Platforms: Driving Success through Diversity
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 ...
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Why open-source AI platforms are the future of patient health tracking in clinical trials
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 ...
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From Black Box to Transparency: Pulling the Curtain Back on Machine Learning
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 ...
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Good Machine Learning Practice: The First Step Toward Transparency & Trust
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 ...
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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. Before ...