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| November 9, 2018

Doc.AI Acquires In Effort To Make AI More Accessible

Nqaba Matshazi

Nqaba has been working as an investigative journalist for the last 10 years. He has written for various media outlets across the world. Nqaba has been working as an investigative journalist for the last 10 years. He has written for various media outlets across the world., a blockchain based artificial intelligence (AI) that performs deep learning computations on quantified biology for predictive analytics and personal health insights, has acquired, a data science platform aimed at swift deployment of artificial intelligence. says it has rewritten the Crestle platform for “greater scalability to ensure it meets the needs of the data science community,” which will allow physicians to join the AI revolution. seeks to make AI accessible to wider medical community

With the acquisition, said it plans to combine Crestle, which has 11,000 registered data scientists with a user growth rate of 10% per month, with training for individuals in healthcare with the hope of empowering physicians and medical practitioners to use AI for predictive analysis based on available data. said was now providing data scientists with a “one-click” access to “scalable, powerful, inexpensive GPU and CPU infrastructure.”

At the heart of’s acquisition of is a belief that AI will soon be one of the “main languages” for both individual participants and the healthcare industry to connect.

“There’s a huge demand for physicians and data scientists working with medical data. This is where we want to focus to make sure the connection happens and symmetry is being built between the different stakeholders in health care,” chief operating officer, Sam De Brouwer, told Venture Beat.

Towards the end of October, started offering the new platform to students of the new Live course.

“Along with Live, will support new deep learning courses in 2019 for physicians and bioinformaticians, as well as the next Practical Deep Learning for Coders MOOC (massive open online courses),” said.

The Live deep learning course is seven weeks long and it targets beginners. hopes the course will make AI more accessible to the wider scientific community.

Jeremy Howard, the chief science officer at, who is leading the course, said: “We have to make sure that all the scalability works as students use it, but at the moment it’s looking like the best option for the one-click Jupyter Notebook approach.”

In addition, the new course to be launched in January 2019 targets medical professionals and will be led by’s advisor and Harvard University Assistant Professor of bioinformatics, Chirag Patel and faculty member Arjun Manrai.

With the courses hopes it can plug a gap in the healthcare industry. While there has been rapid growth in the use of AI, believes that more can be done, as in the health care sector, “there are only a few people in the world who are able the comprehend the data and professionally work with it using limited medical terminology to get the necessary insights to solve real-world problems.”

AI knowledge gaps in health sector

In identifying the knowledge gap, said that most data scientists in healthcare were either coming from a software engineering background or academia and while they may be skilled in machine learning and statistics, they lacked experience in the health sector.

On the other hand, physicians were not quite knowledgeable in machine learning, statistics, mathematics, or computer science.

“That’s why it’s crucial for the industry to build an ecosystem where AI intelligence and the healthcare domain meet. With our infrastructure, we are aiming to create an environment for highly skilled AI physicians through providing educational courses and an easy to use Deep Learning infrastructure,” said on its blog.

In the long run, hopes that the amount of medical data “will help create targeted and advanced educational patient tracks for physicians and offer the community highly skilled AI professionals with a unique domain knowledge.”

With the use of AI and blockchain technology, wants to use the vast amounts of medical data it gathers to formulate predictive models. The company’s first such trial was in 2016, where it sought to use AI to predict seasonal allergies.

The trial had more than 1,700 participants and was done with support from Anthem., which raised $10 million in the fall of 2017 through a token sale supported in part by Ethereum cofounder, Anthony Di Iorio, did not reveal the amount of the acquisition.


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