The responses have been given by Rohit Ghosh, Founding member, Qure.ai
All around the world, people are facing unprecedented challenges and uncertainties as a result of the COVID-19 pandemic. As InnovatioCuris (IC), we are always on a lookout for healthcare innovations that are affordable and provide quality care. In the wake of this need of the hour, InnoHEALTH magazine scouted and interviewed some innovative startups to build an army of health transformers to mobilize and address this global health crisis.
Kritika Arora and Varsha Prasad interviewed Rohit Ghosh, Founding member, Qure.ai on behalf of InnoHEALTH magazine.
By interviewing Qure.ai, we aim to understand and review the role of AI as a decisive technology to analyze, prepare us for prevention, and fight with COVID-19 (Coronavirus) and other such pandemics.
What are the reasons behind the company name , any story behind it?
Rohit, who is the founding member of Qure.ai, very honestly said “This is funny as there is no specific reason as such for naming the start-up as qure.ai. We were looking for a short and subtle name. It was basically that we were not able to get our domain name as cure.ai and that’s how we landed up with Qure.ai” !
What made you start the company ? Tell us briefly about your journey.
The primary idea initially was to apply the concept of machine learning and data science in the field of healthcare. Back then, deep learning was just a newly coined term, not hyped like now. So we were not looking at buzzwords but more on the real impact that we could create using technology.
Initially, we were exploring the different domains in healthcare to focus upon but very soon we decided to opt radiology as our field of interest. The primary reason was, we saw a problem that there is a huge gap in the availability of radiologists, globally. Moreover, I still believe the value that can be created in radiology is not only higher but more universal in terms of global protocols.
How is your company using AI? Tell us about the specialisations where it is being used.
As an AI for radiology company, we have launched two major flagship products in the last four year –
- qXR is AI for chest x- rays (qXR)
- qER is AI for head CT scans
On the specialisation part, qXR enables the detection of major abnormalities which are associated with chest x-rays out (of which TB is commonly used one in India and SE Asia and now COVID-19 as well). qER helps in automated reading of emergency findings from non-contrast head CTs. AI enables radiologists to analyse these X-rays and CT scans in comparatively shorter duration. It’s almost as good as radiology team having an extra albeit virtual member in their team working round the clock.
Explain us a typical day in office. How does an AI expert spend their day ?
Nowadays, it’s very tough to stay in office as the nature of work involves a lot of travel. But things were drastically different a few years back.
Very early on, he used to spend a good part of the day reading a lot of medical literature understanding basic medicine concepts and also research papers related to application of AI not only in healthcare and also in other domains.
Later on things changed as he started devoting more time to developing solutions for the problem at hand. In addition to this , he also spends a lot of time on discussions with radiologists understanding different nuances. For example, understanding if they are getting false positives or negatives then what is the likely reason behind it and how it can be rectified. Working closely with clinicians was very helpful and is still of help, says Rohit. He also shared a personal learning by saying that he himself can actually read a head CT with a fair amount of accuracy!
Tell us the challenges you have faced or are currently facing in the development or implementation of AI.
There are a lot of challenges associated with development and implementation of AI in medical imaging. The first challenge is understanding medical concepts and grasping them in a relatively short time when you’re not from a medical background. Then the availability of data is the other big challenge for any AI organisation – in medical imaging it becomes even more pronounced. All of these are basically the primary challenges which they had to face in their first few years.
According to Rohit, the challenges which they faced after production of their AI products were different – one major challenge was how do they tackle the difference between the medical images across multiple sites. For example X-ray of chest would vary from demographics, machines etc. In addition to this , how these images are taken adds a lot of variability to these images.
Tackling all these challenges were critical to get to a successful working solution.
How can you overcome these challenges? Do you want to share any instances in the past where your team is able to overcome any particular challenge?
To this, very interestingly Rohit said “I would like to tell a very interesting story of myself only. I was working on detection of fractures via head CT scan. In the case of the skull fractures, it becomes very difficult to locate the fracture because there are several confounding impressions on CT and it’s extremely difficult to build an AI software for the same. What I did at that point of time was that I went to a radiologist to enhance my understanding about how he identifies fractures from a head CT scan and tried to mimic that in our AI tool. This helped in drastically improving the performance of software. So as I mentioned earlier, as a non-medicine folk trying to solve problems, the best way to overcome challenges is to work closely with an expert or an advisor. The solutions for other challenges were also unique in their own way.”
How reliable are these AI tools from a clinical perspective ?
The two things which determine the reliability of any AI based tool – the publicly available peer reviewed publications (as everyone can claim their own version of truth) and the testing of one’s own data (it works for others is not enough). So based on both the parameters one can easily get an idea of performance of any AI tool in a clinical perspective. Nothing more, nothing less.
What are the regulatory approvals your organization has?
Currently Qure.ai has CE approval for both the products. Rohit shared that they were the first organisation to have CE approval for screening 15+abnormalities from chest X-rays. The current CE certifications include all the abnormalities which can be detected by both the products. It was also exciting to know that they recently extended their qXR software capabilities to COVID -19 and are also having CE certification for the same .
Share the customer benefit with your solutions and the role AI plays in it.
The most important benefit because of AI is reducing the time in diagnosis of any kind of medical imaging. This can trickle-down in many ways to the end-user – sometimes this improves the productivity of the radiology team whereas other times it directly improves patient health.
Talking about COVID-19 since that’s the most interesting thing right now. The AI software helps in continuous and better monitoring of lung involvement for COVID-19. This helps in better decision making around treatment as well as discharge decision. In some places, the software is also used for screening of suspects (especially asymptomatic ones) in a mass screening set up. Mass screening setups can’t use RT-PCR as it’s scarce and expensive – that’s where Qure’s AI (qXR) along with Chest X-rays can help tremendously in saving lives.
In case of critical conditions like stroke or internal bleeding as well, the AI product for CT heads (qER) is a life-saver. As time plays a critical role in treatment of such patients, AI plays an instrumental role in reducing delays altogether.
Are there any general issues associated with AI products and services ?
Generalisability , interpretability and long-term accuracy are the main issues related to AI products.
What differentiates you from your competitors ?
On this, Rohit said “The major thing that differentiates Qure from our competitors is the quality of research which we carry out and thereby bring transparency to the overall process by publishing those results in peer reviewed journals consistently. This transparency makes us a favourite among clinicians who truly care for quality, helping us reach an incredible amount of global user base in just 4 years.”
Where do you see your organization in 10 years ?
The goal with which the start-up started with still stays and will stay for the next decade- to make healthcare more accessible and affordable for patients, more so in places with lack of infrastructure.
Any brief message for our readers ?
There lies a huge scope for innovations in the field of healthcare, medical imaging is just a minor part of it. Counterintuitively, in low-middle income countries, the opportunities are much bigger as there are a lot of things that need to be fixed there as opposed to other places.
Here the catch is, that you just have to start looking for one problem that you think needs to be solved and want to work upon – from there multiple solutions emerge themselves. One last thing which comes from Rohit’s own story is that if you primarily don’t belong to the healthcare industry but are driven by the desire to impact, don’t be afraid. Give it time, be curious and it would work out. The only real skill is learning fast (whatever the topic be), rest everything falls in line.
With this, we wish all the luck and hope that in the coming decade, people see Qure as a path-breaking Indian AI company and that it creates a major global impact.
Interviewed by Kritika Arora and Varsha Prasad
