Why Synthetic Data is the Antidote to Clinical Trials
To address the clinical burden and enhance R&D, companies are turning to virtual solutions. This involves synthetic data, digital twin models, and AI to speed analysis.
To address the clinical burden and enhance R&D, companies are turning to virtual solutions. This involves synthetic data, digital twin models, and AI to speed analysis.
The healthcare industry is contending with a difficult question: how to properly wield AI without taking on too much risk? Inherent in this battle is the role of humans. Here's how Merck's chief data officer is viewing AI.
Small practices play a critical role in healthcare delivery, but they cannot continue to absorb ever-increasing administrative demands without consequences.
AI-powered platforms can collect data sets from disparate sources and check them for quality, duplication, relevance, and more. These platforms can then create even more extensive, understandable findings to help healthcare professionals make decisions.
This rapidly growing field marries bioinformatics and pharmacology and represents a transformative new era of precision medicine and highly personalized treatments.
Life sciences companies are using “digital twins” for everything from drug discovery to manufacturing. A panel of experts at MedCity News’ INVEST Digital Health conference discussed how this capability to run virtual simulations is changing practices now while also shaping the industry for the years to come.
An increasingly compelling benefit of digital transformation, across numerous industries, is the ability to construct a digital twin—that is, a full in silico replica of a real-life structure, instrument or process.
How to turn analytics into actual policy outcomes.