Pilot use case: Better Medicine
19 December 2025
Use-case description: Better Medicine is an Estonian medical technology company founded in 2020 that focuses on the application of artificial intelligence in radiology for the detection of oncological findings. Better Medicine has achieved ISO 13485 certification.
Better Medicine’s product portfolio includes various organ-based artificial intelligence models, including the world’s first certified kidney model, which helps radiologists detect and classify findings from computed tomography scans more quickly, ensuring more efficient and higher-quality work.
In addition to organ-specific models, Better Medicine has also developed its own viewer, which allows radiologists to work comfortably with artificial intelligence findings. Better Medicine also offers automated reporting and data anonymization/pseudonymization capabilities, which makes integrating the product into the clinical workflow seamless.
Location: Tartu University Hospital (Tartu, Estonia)
Solutions this pilot contributes to:Â 2.1 (Need identification, Game plan, Guiding principles), 2.2 (Clinical AI Pathway Guide)
How and why we piloted the Clinical AI Pathway tools
In CAIDX, piloting means testing the Clinical AI Pathway tools in real AI projects before wider roll-out. The purpose is to ensure that the tools genuinely support healthcare professionals, researchers, and companies in developing and integrating AI solutions. By engaging directly with stakeholders across 16 use cases from six partner countries, we can validate what works, identify what needs improvement, and ensure the Clinical AI Pathway Toolbox becomes a practical, relevant guide—from idea to deployment.
To achieve this, we piloted the tools through surveys, interviews, workshops, roundtables, and real-life testing in clinical and development settings. The three pilots covered different stages of the AI pathway—initiation, development, and implementation—and involved input from clinicians, researchers, hospitals, companies, legal experts, and other specialists. Using diverse methods and real AI projects allowed us to refine the tools and confirm their usefulness before final publication.
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