Digitalization leads our world in a new are of technology.

Technology in Health-care is extremely important.

Fouad Al-Noor will teach you how to build a successful medical start up step by step: so that you don‘t have to fall in the Medtech Valley of Death.

Fouad has a masters in Electric Engineering with Nanotechnology from the University of Southampton. He worked as a research assistant at Imperial College London before joining Entrepreneur First.

He wrote his thesis on paper-based medical diagnostics using image processing.

The University of California, San Francisco is developing and training an AI model that could help diagnose tears in knee cartilage, or the meniscus.  A meniscus tear can lead to long-term health challenges  and lifestyle changes, ranging from debilitation to limits on activity. One of the keys to mitigating the consequences of meniscus tears is identifying and treating tears in the meniscus early. Here’s an interesting look at the research currently going on.

While this goal is pretty simple, the path forward is rather complicated. To diagnose a torn meniscus, clinicians need to review and interpret hundreds of high-resolution 3D magnetic resonance imaging (MRI) slices showing a patient’s knee from different angles. Radiologists then assign a numerical score to indicate the presence of a tear and its severity. This labor-intensive, time-consuming process relies heavily on the skills and availability of clinical specialists, and the interpretation of the images themselves can be rather subjective.

As use of Machine Learning (ML) in medicine becomes more common, it has the power to transform healthcare. One particularly disruptive example is Cancer Detection and Analysis. Here’s a closer look at how ML helps in this area.

by examining biological data such as DNA methylation and RNA sequencing can then be possible to infer which genes can cause cancer and which genes can instead be able to suppress its expression.

Machines can help doctors by spotting abnormalities in X-rays or MRA scans that the physicians themselves may have missed. AI can also help physicians by analyzing data and, through the use of algorithms, produce possible diagnoses.

The hope is that this freed up time, as doctors make their rounds, can help physicians establish better connections with their patients, which in turn can lead to better treatment plans.

Here’s another story of how big data and high performance computing and TensorFlow is reshaping medicine as we know it.

Virtual drug screening has the potential to accelerate the development of new treatments. Using molecular docking, molecular dynamics and other algorithms, researchers can quickly screen for new drug candidates. This saves the enormous expense and time that would have been required to make the same conclusions about those candidates in the lab and in clinical trials.

AI is set to disrupt every field and every industry. Healthcare, in particular, seems primed for disruption. Here’s an interesting project out of Stanford.

“One of the really exciting things about computer vision is that it’s this powerful measuring tool,” said Yeung, who will be joining the faculty of Stanford’s department of biomedical data science this summer. “It can watch what’s happening in the hospital setting continuously, 24/7, and it never gets tired.”

Current methods for documenting patient movement are burdensome and ripe for human error, so this team is devising a new way that relies on computer vision technology similar to that in self-driving cars. Sensors in a hospital room capture patient motions as silhouette-like moving images, and a trained algorithm identifies the activity — whether a patient is being moved into or out of bed, for example, or into or out of a chair.