Our Services
Software as a Medical Device (SAMD) R&D
- Bringing Software as a Medical Device (SaMD) to market requires more than just cutting-edge AI—it demands rigorous compliance, validation, and clinical integration. We help healthcare companies, medical device manufacturers, and startups design, develop, and scale AI-powered medical software that meets FDA, CE, and MDR regulatory requirements
- Our expertise covers end-to-end SaMD development, including:
- Machine learning model integration for clinical diagnostics and decision support.
- Cloud-based and on-premise software solutions for real-time data processing.
- Secure and scalable deployment using containerized environments and serverless architectures.
- We build SaMD solutions that turn AI research into clinical reality, ensuring safety, reliability, and compliance at every stage.
Full-stack SaMD development for AI-driven medical applications, built with compliance and scalability in mind.
Sub-services:
- AI-Powered Clinical Software – Building regulatory-compliant AI applications for medical diagnostics.
- Cloud & On-Premise Deployment – Secure, scalable software solutions for clinical environments.
- Machine Learning Model Integration – Embedding AI-powered decision-making into medical software.
- End-to-End Software Development – From prototyping to full-scale production of SaMD applications.
- Automated Reporting & Decision Support – AI-driven insights for physicians and medical staff.
Case Studies
Real-World Impact
Explore our successful healthcare AI case studies.
Algorithm for Collaboration & Distribution
Updating disparate and unorganized AI algorithm code into cohesive and easily sharable notebooks.
Read More
Clinical UI Design for Radiology AI
Creating intuitive software interfaces that empower doctors and accelerate AI innovation
Read More
Developing 3D Radiology AI Solutions
Moving University Client to the Cloud with Open Source Medical AI Models
Read MorePublications
Explore Our Work
See our 35+ Peer-Reviewed Publications & Patents.
Improved Active Shape Models for Segmentation of the Prostate on MR Imagery
Toth, R. “Improved Active Shape Models for Segmentation of the Prostate on MR Imagery,” M.S. Thesis, Rutgers University, 2010.
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., Ou, Y., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Qiu, W., Gao, Q., Edwards, P.E., Maan, B., van der Heijden, F., Ghose, S., Mitra, J., Dowling, J., Barratt, D., Huisman, H., Madabhushi, A. “Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge.” Medical Image Analysis 25:18(2), Dec 2013 pp. 359-373. doi: 10.1016/j.media.2013.12.002
HistostitcherTM: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework
Toth, R.J., Shih, N., Tomaszewski, J.E., Feldman, M.D., Kutter, O., Yu, D.N., Paulus, J.C., Paladini, G., Madabhushi, A., “HistostitcherTM: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework.” Journal of Pathology Informatics 5(8), Mar 2014. doi: 10.4103/2153-3539.129441
A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI
Toth, R., Traughber, B., Ellis, R., Kurhanewicz, J., Madabhushi, A., “A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI.” Neurocomputing 144(20) Nov 2014. pp. 3-12 doi: 10.1016/j.neucom.2014.01.058
Professor Anant Madabhushi
Executive Director for the Emory Empathetic AI for Health Institute
Assistant Professor Andrew Janowczyk
Emory University
Mike Galvin
Co-Founder & Chairman of LivAI
Reach Out To Theta Tech Today
Let's get your organization the cutting-edge AI you need to succeed and start saving lives together.
