Once Again AI Test Saves a Patient

NHS hospital has obtained Mia, an AI testing tool that has successfully discovered subtle indications of breast cancer in 11 women which hadn’t been seen by doctors.

Mia was tested alongside NHS clinicians and then further analyzed over 10,000 mammograms for female patients. The majority of the women were cancer-free, but Mia was able to flag the women with symptoms – an additional 11 doctors failed to identify.

During the earliest stages of cancer, the cancerous cells are typically small and difficult to spot.

One of the 11 patients, Barbara, had a cancerous tumor spotted by Mia, while radiologists at the hospital were not able to flag. The tumor was 6mm and because it was caught early, Barbara only needed an operation along with 5 days of radiotherapy. Typically, breast cancer patients who have a tumor that is less than 15mm (about 0.59 in) and its discovered have a 90% survival rate over the span of five years.

Without the AI testing tool, the tumor detected in Barbara would have only been able to detect it three years later during her next mammogram. Another benefit for having AI testing tools is the fact that Mia’s waiting time is reduced to three days unlike the normal waiting time, which is 14 days (about 2 weeks), stated Kheiron the developer.

Although having a tool such as Mia is deemed a breakthrough, experts say that human supervision of the tests remains essential where two radiologists individually observe and analyze the scan.

The benefits of AI testing tools are that, if trained with very specific data, then it can spot symptoms at an early stage. This means that the developers of the tool should have unlimited access to several anonymized images of the symptoms of the disease. This also expands the range of detection, but the only challenge is that patients’ data could be hard to obtain due to confidentiality and privacy concerns.

AI testing tools for healthcare are spreading across the UK, including another AI tool, owned by the Presymptom Health firm, used to analyze blood samples in order to seek indications of sepsis before symptoms emerge.

Other tools are still in the early stages of development.

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