Where Precision Meets Trust in Medical AI
Today’s healthcare providers, especially radiologists, face growing pressure to deliver precise and timely diagnostics, while navigating strict regulatory requirements like the EU AI Act and updated FDA.
Our RATify monitoring tool offers a reliable solution that enables real-time tracking of AI and human outputs, flagging potential errors as they happen. With this tool, radiologists and healthcare teams can ensure higher diagnostic accuracy, minimize risks of oversight, and maintain full compliance with regulatory standards.
Imagine a solution that not only streamlines your workflow but also provides confidence in the accuracy of each diagnostic decision, making patient safety your top priority.
EXPLAINABLE AI | RATify
The updated FDA and EU AI Act (Article 71), mandates post-market surveillance (PMS) and continuous monitoring of AI.
Companies face costly fines (up to €35 million) for breaching the AI Act.
Pain points for AI consumers and developers
- How do I decide which AI tool to implement?
- How do I know it will perform safely and consistently?
- How can I provide feedback?
- How can I collect /evaluate the scientific evidence and metrics of the AI tool in real time?
- How can get my product through regulatory clearance as it is so expensive to run a 6-12 month trial?
- I have the data, how can I build my own AI tool?
In the high-stakes field of radiology and healthcare, accuracy, compliance, and efficiency are essential. RATify real-time monitoring tool is designed specifically to support radiologists and healthcare providers by:
- Enhancing Diagnostic Accuracy: With built-in real-time monitoring, RATify flags any anomalies or inconsistencies, whether they originate from human or AI-based errors, ensuring a second layer of accuracy.
- Supporting Regulatory Compliance: Aligned with the latest EU AI Act and FDA requirements, RATify helps healthcare providers stay compliant, reducing risk and regulatory overhead while enabling them to select AI solutions that meet rigorous scientific standards.
- Boosting Efficiency and Confidence: By automating quality control checks, radiologists can focus more on patient care, knowing that they have a robust tool managing error detection.
- Empowering Informed Decision-Making: RATify platform provides healthcare providers with the necessary scientific evidence to evaluate and choose the best AI solutions available, ensuring that the selected technologies are effective and reliable for clinical use.
- Facilitating Evidence Generation for Regulatory Submissions: For AI companies, RATify assists in generating the scientific evidence required for regulatory submissions, streamlining the path to market and helping to build trust with healthcare providers.
RATify Assurance Tool
RATify facilitates the creation of new AI tools while also validating and monitoring third-party AI solutions. It supplants conventional research methodologies with retrospective evaluation and real-time auditing, enabling the generation of scientific evidence and analysis of various AI solutions for quality assurance. Positioning explainable AI at the forefront, with physicians leading patient care and ensuring the safety of AI applications.
Why should you use RATify?
Responsible and evidence-based AI: 5 years on
Cybersecurity
RADIFY® platform includes advanced cyber security technologies to ensure a safe and secure operating environment. RADIFY® conforms to ISO27001 as well as GDPR for data privacy protection.
All data communicated with or from RADIFY® platform is encrypted in transit and at rest using latest industry standard encryption technologies. All patient data is de-identified from the DICOM files as well as images prior to storage or processing.
All access to RADIFY® platform is restricted to named users with two-factor authentication option available on request. Full auditing is available on all aspects of RADIFY® platform.
Administrative access to all RADIFY® platform components is restricted to authorised users only with full activity logs enforced. RADIFY® platform components are run as non-privileged users and architected as a collection of loosely coupled containerised microservices.
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