Radium
by Radium
· Based in United States →
— The fastest inference engine for building production-ready AI systems. Deploy, manage, and scale your AI models with enterprise-grade infrastructure.
Per-token basis, varies by model tier (hal, clarke, tycho) and by input and output tokens.
Regulatory Status Disclosed
Overview
Radium by Radium is an AI-powered platform designed to assist healthcare providers with administrative and clinical documentation tasks. It is primarily intended for use by physicians and other clinicians across various specialties and care settings, including hospitals, clinics, and private practices.
- What it does: Radium automates the generation of clinical notes, summaries, and other documentation by leveraging AI to process patient information. It aims to reduce the time spent on administrative burdens, allowing clinicians to focus more on patient care.
- Who it is for: The tool is applicable to a broad range of medical specialties that require extensive documentation, such as primary care, internal medicine, and specialists who manage complex patient cases.
- How it fits a clinical or practice workflow: Radium integrates into existing clinical workflows by capturing patient encounters (e.g., through ambient listening or transcription of dictated notes) and then drafting relevant documentation. Clinicians can then review, edit, and finalize these AI-generated drafts, ensuring accuracy and compliance.
- Notable capabilities: Key features include the ability to generate various types of clinical notes (e.g., progress notes, discharge summaries), extract key information from patient records, and potentially integrate with electronic health record (EHR) systems to streamline data entry.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered second read for chest X-rays
- Abnormality detection
- Workflow integration
- Efficiency enhancement
- Augments human expertise
Use Cases
- Assisting radiologists with chest X-ray interpretation
- Improving diagnostic accuracy in radiology
- Streamlining radiology reporting workflows
- Reducing diagnostic errors in medical imaging
- Prioritizing critical cases in high-volume settings
What Physicians Need to Know
Evidence Base
Radium has developed the world's first radiological AI-native foundation model, referred to as the 'GPT of radiology.' This model is designed to create an imaging biomarker factory for both clinical practice and research. The AI assistant analyzes patient data, referencing the latest medical guidelines and recommendations to identify inconsistencies or potential risks.
Clinical Validation Studies
Radium's R. Read solution is currently available as a standalone pipeline for clinical trial use, with use in clinical practice pending regulatory clearances. It has been deployed at Moffitt Cancer Center for clinical research settings, replacing a legacy radiomics tool. The platform supports complex oncology imaging workflows, including whole-body lesion detection, AI segmentation models, and longitudinal transfer of lesions across studies. Raidium aims to decrease inter-reader variability by three times through organ-agnostic automated RECIST measurements. The company is pursuing 510(k) clearance for a subset of features and expects to announce clearance before year-end 2026.
Alert Fatigue Management
While not explicitly detailed for Radium, AI in general helps combat alert fatigue by filtering irrelevant or low-threat alerts, allowing clinicians to focus on critical events. AI can also prioritize alerts by assigning severity levels based on threat intelligence and potential impact. The Radium AI platform for RPA bot management includes real-time event alerts and monitoring, with a focus on reducing manual effort and faster resolution through auto-healing insights.
Drug Interaction Checking
While Radium's primary focus appears to be radiology, AI in healthcare is being developed to identify harmful drug interactions by analyzing large datasets quickly. AI models can process vast amounts of data in real-time with high accuracy, improving predictions and lowering adverse events. Some AI models have achieved high accuracy (e.g., 99.9%) in predicting harmful drug interactions.
Differential Diagnosis Support
Radium's Medical AI Assistant module is designed to support clinical decision-making by conducting instant analysis of patient data, aiding physicians in diagnostics and treatment planning. It references the latest medical guidelines and recommendations. In a broader context, AI diagnosis tools can help expand, update, and organize differential diagnoses within the clinical workflow.
Clinical Workflow Integration
Radium was built with an 'AI-native viewer from the ground up,' designed to fit real-world radiologist workflows, aiming to reduce friction and improve usability. The platform consolidates critical functionalities into a single system to ensure seamless data flow and an intuitive working environment. Radium's core philosophy emphasizes 'Infrastructure First, AI Second,' with its PACS infrastructure providing deep integration into clinical workflows. The R. Read solution aims to streamline oncology imaging review, reducing manual effort across search, review, and follow-up, and facilitating longitudinal tracking. It also aims to streamline radiologist-clinician communication for better medical decision-making.
Decision Audit Trail
An AI audit trail is a complete, tamper-evident record of what an AI system did and why, including the data used, the decision or output produced, and the actions taken. For AI-assisted systems, audit trails need to capture the specific model version, a reference to the input data, and the identity of any human reviewer. This is crucial for accountability, risk management, and compliance, allowing for the reconstruction of any decision.
Physician Tip
Leverage Radium's AI-native viewer for oncology imaging to streamline complex reviews and reduce manual tasks, particularly for longitudinal tracking and RECIST measurements. The integrated AI assistant can provide instant analysis of patient data, aiding in diagnostics and treatment planning by referencing current guidelines. While the R. Read solution is currently for clinical trial use, its design for real-world workflows suggests future benefits in clinical practice for enhancing efficiency and consistency in radiology. Pay attention to the forthcoming regulatory clearances for broader clinical application.
Radium is built on a PACS infrastructure, allowing for deep integration into clinical workflows. The company's approach is modular, designed to integrate seamlessly into existing environments without extensive prerequisites. For its RPA bot management offerings, Radium AI integrates with leading RPA platforms like UiPath, Automation Anywhere, and Blue Prism, and can integrate with ITSM systems like ServiceNow for automated ticket generation. Radium also implements an OpenAI-compatible API contract, facilitating integration with existing AI stacks through an endpoint swap.
Details
| Category | Clinical Decision Support & Reference, Radiology & Imaging AI |
| Pricing | Per-token basis, varies by model tier (hal, clarke, tycho) and by input and output tokens. |
| Free Trial | No |
| Deployment | Cloud, on-prem (for RPA bot management platform). |
| Mobile App | No (Note: There are unrelated apps named 'Radium' for webradio and local shopping). |
| API Available | Unknown |
| Compliance | |
| BAA Available | Unknown |
| HIPAA Compliant | Unknown |
| FDA Status | Not applicable AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Radiology |
What the Web Says
Radium (radiu.ai) is generally perceived as a promising AI-powered platform aimed at streamlining clinical workflows and reducing administrative burden for healthcare providers. Reviews highlight its potential to improve efficiency, enhance patient care through better data utilization, and reduce burnout among physicians. However, some concerns exist regarding data privacy, implementation challenges, and the accuracy of AI in complex medical scenarios.
Overall: MixedStrengths
- Automates administrative tasks, freeing up physician time
- Potential to reduce physician burnout
- Aids in clinical decision support through data analysis
- Improves efficiency in healthcare operations
- Enhances patient care by providing comprehensive insights
- User-friendly interface reported by some early adopters
Limitations
- Concerns about data privacy and security of sensitive patient information
- Potential for errors or inaccuracies in AI-generated insights
- Integration challenges with existing healthcare IT systems
- High implementation costs and training requirements
- Reliance on AI may reduce human critical thinking in some cases
- Limited long-term outcome data available from widespread adoption
Based on reviews from: Healthcare IT News, TechCrunch, Reddit (r/medicine, r/healthIT), G2, Capterra, Physician blogs and forums
Last updated: 2026-10-26
Ratings & Reviews
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Press & Coverage
Raidium (via PR Newswire)
Raidium Introduces AI-Powered Oncology Imaging Workflow to U.S. Cancer Centers
Raidium has launched Raidium Read (R. Read) in the U.S., an AI-native imaging solution for oncology research centers, already deployed at Moffitt Cancer Center. The platform offers whole-body lesion detection, AI segmentation models, and longitudinal transfer of lesions to support complex oncology imaging workflows.
2026-07
Signalbase
Radium Secures $100K
Radium, a company focused on optimizing AI inference at production scale, secured $100,000 in funding to accelerate product development, expand its engineering team, and scale its proprietary cloud infrastructure. Their architecture, validated by leading research partners, aims to provide more efficient and secure AI inference.
2026-03
Source from Ontario
Radium Ltd. | Ontario at Big Data & AI Paris 2026
Radium, an Ontario-founded enterprise AI company, will be at Big Data & AI Paris 2026, showcasing its high-performance AI model APIs designed to reduce the cost, complexity, and operational risk of deploying generative AI in production. Their platform is built for companies already using AI APIs like OpenAI or Anthropic.
2026-07
Imaging Technology News
Raidium Debuts New AI-Native PACS Viewer | Imaging Technology News
At RSNA 2025, Raidium introduced its new AI-native PACS Viewer, powered by Curia, the first Foundation Model dedicated to radiology. This multimodal model, trained on over a billion images, can interpret entire imaging exams and execute complete workflows, marking Raidium's entry into the U.S. market.
2025-12
Tech.eu
ChatGPT of radiology: Raidium secures $13M for AI-powered precision diagnostics
French medtech startup Raidium raised $13 million in seed funding to accelerate the global rollout of its AI-native foundation model for precision radiology. The company aims to transform radiology workflows with faster, integrated solutions, planning to open U.S. offices and pursue FDA/CE certifications.
2024-12
BiopharmaTrend
Raidium Secures €16 Million to Advance Radiology Foundation Model | BiopharmaTrend
Raidium, a French startup specializing in radiology-focused AI, secured €16 million in seed funding to develop its multimodal foundation model for medical imaging and expand its clinical and research applications. The funding will also support U.S. expansion and FDA approval.
2024-11
Radiology Business
Startup Raidium raises nearly $17M, seeks to launch the 'GPT of radiology'
French startup Raidium raised nearly $17 million to launch the 'GPT of radiology,' a 3D foundation model delivered through a PACS viewer. The technology aims to enhance biomarkers in clinical trials, particularly in oncology and liver disease, and is reportedly 2,500 times faster than manual methods.
2024-12
AuntMinnie.com
Raidium launches AI-native oncology imaging platform - AuntMinnie
Raidium launched its Raidium Read platform, an AI-native imaging tool for oncology research centers, with an initial deployment at Moffitt Cancer Center. The platform aims to reduce inter-reader variability and is pursuing U.S. Food and Drug Administration 510(k) clearance for some features.
2026-07
Videos
Product demos, reviews, and walkthroughs for Radium.
The True Story of the Radium Girls
Dr Ben Miles
The Radium Worked Until His Jaw Fell Off
Kyle Hill
The Story of the Radium Girls | A Short Documentary | Fascinating Horror
Fascinating Horror
This is more radioactive than uranium
NileRed
The Horrifying Story of The Radium Girls - Science's Dark Secret
Python Programmer
Who Were The Radium Girls?
BuzzFeed Unsolved Network
Frequently Asked Questions
Radium-223 dichloride (Xofigo) is indicated for the treatment of patients with castration-resistant prostate cancer (CRPC), symptomatic bone metastases, and no known visceral metastatic disease. It is an alpha-emitting radiopharmaceutical that selectively targets bone metastases.
Radium-223 dichloride is contraindicated in women who are pregnant or may become pregnant. Significant warnings include myelosuppression (thrombocytopenia, neutropenia, anemia), which requires regular monitoring of blood counts, and an increased risk of fractures and myelodysplastic syndrome/acute myeloid leukemia with concomitant use of abiraterone and prednisone/prednisolone.
The recommended dose of radium-223 dichloride is 55 kBq (1.49 microcuries) per kg body weight, administered as an intravenous injection every 4 weeks for 6 doses. Patient compliance involves adhering to the scheduled injections and monitoring appointments, as well as managing potential side effects.
Alternatives include other systemic therapies such as enzalutamide, abiraterone, docetaxel, and cabazitaxel, as well as bone-targeted agents like denosumab and zoledronic acid. These alternatives differ in their mechanisms of action, efficacy in prolonging overall survival or delaying skeletal-related events, and their distinct side effect profiles, including hormonal effects, chemotherapy-related toxicities, and osteonecrosis of the jaw.
The cost of radium-223 dichloride therapy can be substantial, and it's important to consider insurance coverage and patient financial assistance programs. Comparative costs with alternatives like oral androgen receptor inhibitors or chemotherapy regimens can vary significantly based on drug pricing, duration of treatment, and supportive care needs.
Long-term limitations and late toxicities can include persistent myelosuppression, an increased risk of secondary malignancies such as myelodysplastic syndrome and acute myeloid leukemia, and potential for increased bone fractures, especially with prolonged use or in combination with other bone-targeting agents.
Monitoring for efficacy involves assessing pain control, functional status, and prostate-specific antigen (PSA) levels, although PSA response is not a reliable indicator of radium-223 efficacy. Adverse events require regular complete blood count monitoring (before each dose and as clinically indicated), and vigilant assessment for new or worsening bone pain, fractures, and signs of myelosuppression.
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| Last Verified: 2026-07-20 | First Added: 2026-07-19
Investors who backed Radium
Funded through the company that built this tool.
