{"id":29086,"date":"2026-03-30T16:59:38","date_gmt":"2026-03-30T16:59:38","guid":{"rendered":"https:\/\/microvibenews.com\/?p=29086"},"modified":"2026-03-30T16:59:38","modified_gmt":"2026-03-30T16:59:38","slug":"mantis-biotech-is-making-digital-twins-of-humans-to-help-solve-medicines-data-availability-problem","status":"publish","type":"post","link":"https:\/\/microvibenews.com\/?p=29086","title":{"rendered":"Mantis Biotech is making ‘digital twins’ of humans to help solve medicine’s data availability problem"},"content":{"rendered":"


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Large language models trained on vast datasets could speed genomics research, streamline clinical documentation, improve real-time diagnostics, support clinical decision-making, accelerate drug discovery, and even generate synthetic data to advance experiments.<\/p>\n

But their promise to transform biomedical research often runs into a bottleneck: beyond the structured data healthcare relies on, these models struggle in edge cases like rare diseases and unusual conditions, where reliable, representative data is scarce.<\/p>\n

New York-based Mantis Biotech<\/a> claims it\u2019s developing the solution to fill this data availability gap. The company\u2019s platform integrates disparate sources of data to make synthetic datasets that can be used to build so-called \u201cdigital twins\u201d of the human body: physics-based, predictive models of anatomy, physiology, and behavior.<\/p>\n

The company is pitching these digital twins for use in data aggregation and analysis. These digital twins could be used for studying and testing new medical procedures, training surgical robots, and simulating and predicting medical issues or even patterns of behavior. For example, a sports team could predict the likelihood of a specific NFL player developing an Achilles heel injury based on their recent performance, training load, diet, and how long they\u2019ve been active, Mantis\u2019 founder and CEO Georgia Witchel explained to TechCrunch in a recent interview.<\/p>\n

To build these twins, Mantis\u2019 platform first takes data from a variety of sources such as textbooks, motion capture cameras, biometric sensors, training logs and medical imaging. Then, it uses an LLM-based system to route, validate, and synthesize the various data streams, and runs all that information through a physics engine to create high-fidelity renders of that dataset, which can then be used to train predictive models.<\/p>\n

\u201cWe\u2019re able to take all these disparate data sources and then turn them into predictive models for how people are going to perform. So anytime you want to predict how a human being is going to be performing, that is a really good use case for our technology,\u201d Witchel said.<\/p>\n

The physics engine layer is key here, Witchel told TechCrunch, because it helps the platform enhance the available information by grounding the generated synthetic data and realistically modeling the physics of anatomy. <\/p>\n

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Techcrunch event<\/p>\n

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\n\t\t\t\t\t\t\t\t\tSan Francisco, CA<\/span>
\n\t\t\t\t\t\t\t\t\t\t\t\t\t|<\/span>
\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n

\u201cIf I asked you to do hand-pose estimation for someone who is missing a finger, it would be really, really hard, because there are no publicly available datasets of labeled hand positions of someone who is missing a finger. We could generate that dataset really, really easily, because we just take our physics model and we say, remove finger X, regenerate model,\u201d she said.<\/p>\n

Since Mantis\u2019 platform fills gaps in data sources, Witchel thinks there\u2019s potential for it to be used widely across the biomedical industry, where information on procedures or patients can be difficult to access, is unstructured or siloed into various sources. She stressed edge cases or rare diseases, where data is hard to obtain since there are often ethical and regulatory constraints around including patients\u2019 data in public datasets, or using it for training AI models.<\/p>\n

\u201cYou know how when you see a three-year-old running around, and they have a Barbie, and they\u2019re holding it by one leg and smashing it against a table? I want people to have that mindset with our digital twins,\u201d she said. \u201cI think that\u2019s going to open up people to this idea that humans can be tested on when you\u2019re using virtual humans. I feel currently, people operate with the exact opposite mindset, which totally makes sense, because people\u2019s privacy should be respected. In fact, I don\u2019t really think people\u2019s data should be exploited at all, especially when you have these digital twins.\u201d<\/p>\n

For now, Mantis has seen success in professional sports, presumably because there is a need to model high-performing athletes. Witchel said one of the startup\u2019s main clients is an NBA team. <\/p>\n

\u201cWe create these digital representations of the athletes, where it basically shows here\u2019s how this athlete has jumped, not just today, but for every single day in the past year, and here\u2019s how their jumps are changing over time compared to the amount that they\u2019re sleeping, or compared to how many times they lift their arms above their head,\u201d she explained.<\/p>\n

The startup recently raised $7.4 million in seed funding led by Decibel VC, with participation from Y Combinator, a few angel investors, and Liquid 2. The funding will be used for hiring, advertising, marketing and go-to-market functions.<\/p>\n

The next step for Mantis, Witchel said, is to continue building out the tech, and eventually release the platform to the general public, targeting preventative healthcare. The company is also working to cater to pharmaceutical labs and researchers working on FDA trials, aiming to deliver insights into how patients are responding to treatments.<\/p>\n<\/div>\n


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Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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