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AI in Pathology

Why is Artificial Intelligence Important in Pathology

AI is more important to pathology than you think

As a technology-forward company, Airspace is in love with the potential of artificial intelligence. But why should we care about how AI is helping in other industries, like pathology for instance?

There are more than just a few reasons. First, we are invested in the welfare of people. We ship things that are critical to life, like organs for transplant and medical specimen for testing. Airspace is proud to say that we are partnered with the majority of OPOs within the United States. We enable these life saving companies through the utilization our technology. AI helps Airspace do a better job ensuring that those critical shipments get where they’re supposed to go on time. It also helps Airspace do a better job of accurately notifying our clients precisely when their shipments will arrive, and where they are in the shipping process. So in this way, Airspace is in the life saving business.

In pathology, for example, AI and machine learning help lab techs quickly analyze large datasets to obtain the kind of meaningful insights that can improve patient outcomes. In particular, these technologies help clinicians interpret the extremely large amounts of data obtained by MRI machines, CT scanners, x-rays and biopsies. Having people observe all of this data is much more time consuming and expensive. Additionally, with AI it is less likely that there will be a mistake. A study from Case Western Reserve University showed that a deep learning network identified the presence of invasive forms of breast cancer in pathology images with 100 percent accuracy.

But it’s important to note these technologies are supporting, not replacing, people. They are streamlining pathologists’ decision-making processes. It can also help them save time by enabling them to focus on the results, rather than spending time trying to decipher the scans. 

AI is only just beginning

Colorado State University is developing a way of using machine learning to develop a virtual biopsy tool that will make early detection of melanoma faster, less expensive and more accurate. In addition, the National Institutes of Health is using AI to improve lesion detection. It released a dataset of more than 32,000 medical images, large enough for scientists to train a deep learning neural network and create a large-scale lesion detector with one unified framework.

So there you have it. Helping is what it’s all about, no matter where you are in the healthcare industry.

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