Is Medical AI the next Breakthrough in Healthcare?

  • Research laboratories, which create algorithms;
  • Companies (most likely — startups), which dream of an exponential growth of their products thanks to the integration of artificial intelligence;
  • Consulting agencies, helping business to understand its needs and integrate new technologies, as well as offering b2b solutions.
Source: PwC AI Predictions 2019. Base: 633. Q: How far along is your organisation with AI? Select one.

Medicine — the Most Promising Sphere for AI Implementation

As for today, medical AI is one of the most popular technologies for investors. Since 2013, medical AI startups managed to attract $4,3 billion in 576 transactions (according to CB Insights). In three years the medical AI market will reach $6,6 billion, increasing every year by 40%. There is nothing strange about it — this technology is to revolutionize almost all medical spheres in the nearest years.

Almost all leading digital corporations are now developing AI-based medical products and services. According to the Venture Scanner research company, there are more than 800 companies worldwide, which are involved in this process.

What will happen?

High Risks and Possible Problems

One of the biggest problems that impedes the start of the full-scale integration of AI-assisted diagnostics into healthcare is the lack of required medical data. Datasets, the large sets of medical images, which are used for the process of neural network training, must be labeled. It means that a qualified medical specialist, which makes the process very time-consuming and costly, must manually mark out any disease or abnormality on medical images. Today there is no specific demand for the labeled datasets from the developers, which makes the development of the whole sector stall.

The Solution

In order to prevent the monopolization of the medical AI market we should pay attention to the projects with distributed structure. One of them is Skychain, which is now extremely close to the moment, when the platform is fully operational.

  • Respiratory diseases (atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, hernia). Average accuracy — 84%.
  • Bone abnormalities recognition (detects fractures, degenerative diseases, lesions and subluxations). Average accuracy: 82%.
  • Skin cancer detection. Average accuracy — 70%.
  • Brain tumors detection. Average accuracy — 87%.
  • Breast cancer detection. Average accuracy — 93%.
  • Retinal diseases (DME, CNV and drusen) detection. Average accuracy — 99%.
Source: PwC Survey



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Blockchain infrastructure aimed to host, train and use artificial intelligence (AI) in healthcare. Our website: