Leading the Way in Effective Drug Development
Closing the Gender Gap in STEM Careers: The Role of Networks, Authenticity & Leadership
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.
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 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.
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 observation more like an engineering problem.
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 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.
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 industry.
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 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.