Medical algorithms are gradually becoming part of the diagnostic process, but before using them in clinical settings, their effectiveness must be verified. The L7 Center conducted an independent study of the artificial intelligence system from the company «Celsus», which analyzes mammography images.
What Was Studied
We are discussing an algorithm that assists doctors in detecting breast pathologies using X-rays – specifically, mammograms. Simply put, the system analyzes the image and highlights areas that could indicate a disease.
The L7 Center used a set of mammography examinations for verification and processed them using the algorithm. The task was straightforward: to evaluate how accurately the system identifies problematic areas and whether it misses anything important.
What the Results Showed
The results proved encouraging. The algorithm showed high sensitivity – meaning it accurately recognizes pathology when it is present. Concurrently, the system demonstrated acceptable specificity, indicating it does not produce too many false positives by identifying a problem where none exists.
For medical algorithms, this balance is crucial. If the system generates too many false alarms, doctors will cease to trust it. If it misses actual cases, patients will suffer negative consequences.
Why This Is Needed in Practice
Mammography is a mass screening examination. Radiologists view hundreds of images, and human attention is not limitless. AI can function as a «second pair of eyes» here: it highlights suspicious areas, and the doctor makes the final decision.
This is particularly useful in regions with a shortage of specialists or in large centers with a high volume of patients. The algorithm does not replace the doctor but helps reduce the workload and decrease the likelihood that something important will go unnoticed.
What's Next
The study by the L7 Center is an important step toward the wider use of such systems. Independent verification demonstrates that the technology works on real data, not just under idealized laboratory conditions.
Now the question is how quickly these algorithms will be integrated into routine practice. This requires not only technical readiness but also understanding from medical institutions, staff training, and integration with existing systems.
However, the direction is clear: AI in medical diagnostics is not science fiction but a tool that can help doctors work more accurately and faster right now.