Panel interview with senior members of some prominent healthcare AI startups #GiantHealthEvent

Data Science representatives from Eagle Genomics, Babylon Health, Touch Surgery, Myrecovery, Creation were interviewed by Cognition X.

All these companies use machine learning in different ways either to analyse the genome, diagnostic/surgical implications or helping patients directly.

All of the panel agree that AI is coming back after going through a ‘nuclear’ winter.


Eagle Genomics feel that by 2025 there will be over 250 million patients with their genomes sequenced. This constitutes several zetabytes of data. This cannot be analysed by humans but only machines. We have a burgeoning data management crisis that can be solved in no other way. We need to turn big data into actionable insights.


Babylon health say they want to make healthcare affordable to all – that is their vision. They believe in augmenting doctors and making them safer. Babylon feel the biggest need is in Africa. That is why they are working in Rwanda because the needs are so great.

They think that in the next 5 years there needs to be some sort of regulatory mechanism to govern the use of AI diagnostics. They want to improve the productivity of doctors not replace them.


Touch surgery feel that humans alone cannot deal with all the data alone. They feel that it is essential to use that data in order to learn how to make their product as good as they can. Currently they are trying to use machine learning to improve the user experience and training. They believe that the technology should also help the surgeon in the same way that power-assisted steering and GPS enhance the driving experience.


Myrecovery are analysing how their users are using the apps as well to try and predict their recovery. A lot of people complained about the US election predictions. Garbage in = garbage out. Longer term they see it as a core asset to the business. They feel that the present situation is unsustainable. We can’t compete with robots any more, we need to work with them in order to get the job done.


Creation feel that it will be essential in the future but at the present are focusing on building data sets and getting them verified by medical professionals. They talked about developing a system that could analyse a photo via a network – like instagram for medical diagnosis. They feel that doctors use social media quite a lot. Sometimes they have found doctors answering individual patients on social media. They feel the barriers are breaking down. They also sited the microbiome. He talked about machine sentiment analysis and how it is currently largely useless. Most ‘AI’ is still heavily dependant on human interaction to make it work but in the future this won’t be the case.

Then Charlie asked the panel what advice they would give to tech startups thinking about working in this field. Their advice was:

  • Get good at selling yourself.
  • You need to be in it for the long haul. (We are in the middle of a revolution).
  • Be prepared to change your business model several times.

Cognition X on the challenges of applying AI in Health #GiantHealthEvent

Healthcare is rapidly becoming the biggest market in AI. The opportunities are enormous.

Prescriptions, Surgery, Diagnostics, Drug discovery and Nursing will all be involved.

The big challenges are: Acquiring enough data, navigating regulation and privacy are the main obstacles slowing down progress here.


Zero knowledge: minimising the number of systems who have a full view of anything.

Cognition X already has 500 AI related products listed. They provide a vertical search engine for AI to find the right machine learning tools.


GE: New Thinking in the Digital Health Ecosystem: Breaking down the silos #GiantHealthEvent

No company or individual is a silo. The internet has changed everything and it is all now connected


But what will happen when 50 billion machines become connected? Suddenly the data will show us the flow in the hospital, the results will be highlighted. The computers can detect problems and fix them.


This is the collision of the physical and analytical, brilliant machines with industrial amounts of data and people interacting with them.

We are heading towards a ‘colossal clash’ between the consumer health technology and clinical healthcare. The two worlds are both merging but also on a collision course with one another.


We are heading to an age where we want to rate everything. This is a consumerisation of healthcare. It will become the norm to rate your doctor online.

In the future nothing will be redundant. Everything will be rankable and in flux. We are heading towards an outcome driven world.


This is why GE are creating an open ecosystem cloud to host health applications because there is going to need to be a way to scale innovations quickly. This can provide a portal for developers to test their products quickly and get feedback.

There is no going back. Things are moving fast and GE want to be ready.

Cognitive Computing in Healthcare – IBM Watson #GiantHealthEvent

The term AI tends to bring up negative connotations. IBM prefer the term cognitive computing now.


Watson is a service – built to be consumed. It understands, it reasons and then learns.

Watson has no biases but rather creates answers based on evidence.

IBM believe that care is delivered in an archaic way and there are much better ways to deliver it. They see medicine as experts drawing on the experience of generally just one person and they feel that there are going to be better ways to come to clinical decisions.

A computer human team if you like.


atson can help to structure data. It can then analyse and rank that data and provide various different options. They feel that the MDT process can be greatly improved because at present decisions are often made my the most senior people rather than taking in the evidence from all the different perspectives.


Another area they are working in is clinical trials and patient matching. The final major area they are looking at is genomic insights.

The example was given of protein discovery. The Baylor team managed to find 6 new proteins using Watson in only 30 days of use. Prior to this 28 protein targets had been discovered in the past 30 years.


IBM’s next plan is to further open up the Watson API to enable teams to work together on projects.