Imagine you are an aspiring oncologist with a strong desire to learn about the spontaneous nature of cancer cells or a budding surgeon wanting to improvise and build on aged, primitive, medical procedures. Very, recently new augmented-reality microscopes, backed by Google Cloud, will make cancer detection much more efficient and cost-effective, helping save many more lost lives in the process.
The Breakdown
The microscopes have the ability to show researchers detailed information on potentially cancerous tissue samples, especially for areas that deserve closer observation, as determined by an algorithm trained on vast Defense Department databases of cancer imagery. This helps pathologists better synthesize data in order to make a more accurate and in depth diagnosis. One of the biggest challenges for humans is to be able to process large amounts of information at once - it’s very intensive and time-consuming, said Mike Daniels, the vice president for public sectors at Google Cloud.
How Does it Work?
The algorithm, working with the augmented reality microscope, acts almost like a second pair of eyes, one that’s better trained to spot certain anomalies — but not necessarily diagnose them. That’s still the job of the human. The project uses TensorFlow, open-source AI software libraries, as well as the Google Cloud Healthcare API, to ingest vast amounts of Defense Department medical imagery and strip it of patient-identifying information.
Promise For The Future
Medical researchers have been talking about the promise of neural networks for spotting cellular anomalies, including cancer, since the early 1990s. The emergence of large medical datasets, cloud-computing capabilities, and new models have made such networks more useful for doing research and predicting things like patient survivability.
The hope is that combining AI and augmented reality will increase accuracy and efficiency of diagnosis and thus make artificial intelligence much more relevant not just to researchers but to actual doctors caring for patients. Enormous amounts of healthcare data also presents a rare opportunity for companies to train new machine learning tools, it’s only about time before technology revolutionizes the way we think about diseases and diagnosis.
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