The generation of synthetic data in healthcare has emerged as a promising solution to surmount longstanding challenges inherent in the use of real patient data. By replicating the underlying ...

PhD, MBA, CTO at John Snow Labs. Making AI & NLP solve real-world problems in healthcare, life science and related fields. Artificial intelligence (AI) and machine learning applications are widely ...

A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully ...

Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for ...

Hospitals and health systems traditionally have experienced significant challenges in finding insights from data at scale, because their data universes are so complicated. A standard health system has ...

In a time when health systems are struggling to gain meaningful insights from data – and simultaneously aware that safeguarding patient privacy is essential – synthetic data offers a lot of potential.

Cedars-Sinai is adopting a synthetic data platform to enhance research and clinical care, enabling teams to work with AI-generated datasets that mimic real patient data while maintaining privacy and ...

Verse uses synthetic data generation, stress testing, and reinforcement learning to train AI voice and text agents on ...

The Universal "AI for Health" Summit, organized by the AI CoLab, a joint initiative of MedStar Health and Georgetown ...

Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies ...