Oncospace
Building data-driven radiation oncology treatment planning products.
| Date | Investors | Amount | Round |
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| - | investor investor | €0.0 | round |
| investor | €0.0 | round | |
| N/A | €0.0 | round | |
| investor | €0.0 | round | |
| investor | €0.0 | round | |
* | N/A | Acquisition | |
| Total Funding | 000k | ||
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Oncospace, Inc., established in 2017, is a Baltimore-based company that emerged from research conducted at Johns Hopkins University School of Medicine. The company was co-founded by Praveen Dalal, who serves as CEO, and a team of medical physicists and data scientists, including Todd McNutt, who holds the roles of President and Chief Scientific Officer. McNutt, a Professor and Director of Clinical Informatics in the Department of Radiation Oncology and Molecular Radiation Sciences at Johns Hopkins, brings deep clinical and research expertise to the firm. His background in developing data-driven models for radiation oncology directly informs the company's core technology.
The firm operates in the healthcare technology sector, specifically focusing on radiation oncology. Its business model is centered around a Software-as-a-Service (SaaS) platform, delivered via the Microsoft Azure cloud. Oncospace targets radiation oncology practices as its primary clients, providing them with tools to enhance treatment planning and clinical decision-making. Revenue is generated through subscriptions to its platform.
The core product is an AI-powered, data-driven solution designed to standardize and improve the quality of radiation therapy. A key feature of the platform is its ability to generate AI-driven treatment plans for cancers such as prostate and head & neck, allowing clinicians to predict achievable outcomes based on a vast database of historical treatment data. This technology aims to automate aspects of the planning process, reduce variability between clinicians, and promote the adoption of best practices across institutions. By leveraging machine learning models trained on real-world clinical data, the software provides patient-specific dose predictions, enabling physicists and dosimetrists to create more precise and consistent treatment plans.
Keywords: radiation oncology, medical physics, SaaS, healthcare AI, treatment planning, data-driven healthcare, clinical informatics, oncology software, radiotherapy, medical technology