Hiveomics was founded in Israel by Pavel Gavrilov, Daria Selezneva, Uliana Smolnikova, Lalit Gupta, Artem Asinov, Maxim Ivanov, and Vitaly Soloviev, clinicians and developers driven by the idea of minimizing invasive procedures in cancer treatment, bridging pathology and radiology, and ensuring early detection of malignant processes in the healthcare system.
At Hiveomics, we're not just building another AI tool but creating digital twins of human organs that evolve and learn. Our technology begins with the lungs, where our AI agent doesn't simply flag suspicious nodules; it creates a complete digital replica of the patient's pulmonary system. This digital twin track changes over time, understanding the unique patterns of each patient's condition in ways that traditional snapshot analysis cannot match.
The power of our approach lies in its comprehensiveness. While competitors like Optellum focus solely on lung nodule detection, our platform already extends to liver and kidney analysis, with brain mapping on the horizon.
This multi-organ capability isn't just a feature – it's a fundamental shift in understanding cancer progression and treatment response.
In healthcare, AI agents can provide personalized patient care and diagnostics. AI-driven systems can integrate with electronic health records (EHRs) and utilize medical imaging, genomics, pathology data, and comprehensive healthcare solutions.
Hiveomics Oncology AI agent combines data from different sources to provide patient routing, diagnostic recommendations, and a prognosis for patient treatment strategy.
Our competitive advantage stems from three key innovations:
First, the Hiveomics Malignancy Index represents a breakthrough in risk assessment. Unlike black-box AI solutions, our technology provides explainable results that doctors can trust and verify, validated through extensive multicenter studies.
Second, our Digital-Twin-at-Edge technology solves one of healthcare's most pressing challenges: data security. We've created a solution that satisfies clinical and compliance needs by processing sensitive patient information locally while maintaining cloud connectivity for insights.
Third, our predictive modeling capabilities extend beyond diagnosis to treatment planning. By creating digital simulations of how tumors might respond to different treatments, we're helping clinicians make more informed decisions about patient care.
We create a Hiveomics Digital Twins Cloud to replicate the malignancy process of different localizations. These days, lung cancer and lung metastasis are our primary focus. We model lung tumor transformations over time, including response to treatment.
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