AI has also shown promise in reducing the rate of interval cancers (cancers diagnosed between regular screening intervals) and triaging mammograms by prioritizing those with suspected findings.

Despite these advances, the adoption of AI in mammography is still in the early stages, with many radiologists yet to integrate these tools into their practice. Issues such as costs, ethical concerns, and patient privacy need to be addressed to facilitate broader adoption.


The importance of diverse and robust training datasets
AI systems require large and diverse datasets for training to ensure accuracy across different populations. The diversity of equipment, patient populations, and even the processing algorithms used by different vendors can significantly impact AI performance.

For example, if an AI system is trained primarily on images from one type of machine, it might not perform as well when analyzing images from another. An example of this comes from a study from Scotland, where a software update to image preprocessing significantly increased the cancellation rate of AI-flagged cases because the AI had not been trained on images processed with the new software.

Companies like Volpara, which utilize unprocessed images rather than fully processed ones, have an advantage in this regard. By training AI on unprocessed images, they can avoid the issues associated with varying processing algorithms.

Evaluating AI solutions for breast imaging
Imaging centers should consider several factors to ensure they implement the most effective and reliable technology.

As previously discussed, the size and diversity of the AI dataset used to train algorithms are paramount. A larger and more varied dataset helps improve the algorithm's performance and ensures it can accurately detect cancer across different demographics and breast tissue types.

Third-party validation studies are another essential consideration. Relying solely on studies funded or published by the company that developed the AI tool may not provide a complete picture of its efficacy. In fact, in a recent overview of 100 CE-marked AI products from 54 vendors, 64 of the 100 products reviewed had no peer-reviewed evidence of their efficacy.

Explainable AI features, which provide transparency into how the AI makes decisions, are also important, as trust in AI is often a barrier to its adoption in clinical settings.

As always, compliance support is critical in a heavily regulated field like healthcare. AI vendors must prioritize compliance with all relevant regulations, including the new Mammography Quality Standards Act (MQSA) requirements that took effect effect 10September 2024.

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