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Digital Twins: Synthetic Data Powers Biomedical Research

Large Language Models Synthetic Data Digital Twins Biomedical Research AI Sports Tech Data Availability
March 30, 2026
Source: TechCrunch AI
Viqus Verdict Logo Viqus Verdict Logo 6
Promising but Peripheral
Media Hype 4/10
Real Impact 6/10

Article Summary

Mantis Biotech is tackling a significant bottleneck in biomedical research: the lack of readily available data, especially for rare diseases and complex conditions. The company’s platform uses large language models (LLMs) and physics-based simulations to generate synthetic datasets, creating ‘digital twins’ of human bodies. This approach addresses the ethical and logistical challenges of obtaining sufficient data from real patients, which is often limited by privacy concerns, rarity, or the difficulty of controlled experimentation. The platform’s key innovation lies in its ability to translate disparate data sources—textbooks, motion capture, biometric sensors, training logs—into realistic, physics-driven simulations. By ‘removing’ a finger in a digital twin, for instance, Mantis can quickly generate labeled data for hand pose estimation, a problem that would be extraordinarily difficult to solve using traditional datasets. This capability has potential applications in a range of areas, including clinical diagnostics, drug discovery, surgical training, and the development of personalized medicine. The company’s early successes are focused on professional sports, where the technology is used to model athlete performance and predict injuries, but the broader vision extends to preventative healthcare and pharmaceutical research.

Key Points

  • Mantis Biotech is creating 'digital twins' of human bodies using LLMs and physics-based simulations.
  • The platform addresses the critical data scarcity issue in biomedical research, particularly for rare diseases.
  • The technology has early applications in professional sports (athlete injury prediction) and broader potential in drug development and personalized medicine.

Why It Matters

While the concept of digital twins is gaining traction, the specific application by Mantis Biotech highlights a tangible solution to a persistent challenge in AI-driven healthcare. The ability to generate synthetic data, particularly when grounded in physics-based models, significantly increases the feasibility of training AI models on conditions where real-world data is scarce. This is crucial for accelerating research on rare diseases, where traditional methods are often hampered by the sheer difficulty of collecting enough patient data. The company’s initial focus on sports offers a relatively low-risk entry point, demonstrating the technology’s value and providing a foundation for broader adoption in the healthcare industry. This represents a strategic move toward addressing a substantial market need.

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