AI and ML are making equitable, high-quality healthcare a reality: Qure.ai



The pandemic has seen the rapid emergence of artificial intelligence (AI) and machine learning (ML) to deliver fast, quality healthcare to millions of people.

Healthtech startup Qure.ai has carved out a niche for itself with cutting-edge technology that reads and interprets medical images like X-rays, CT scans and ultrasounds in less than a minute, making healthcare fair and high quality a reality throughout the world.

Its automated medical imaging tools can shorten the time to diagnosis while allowing doctors to triage medical cases more efficiently, especially in urgent situations.

In a conversation with IANS, Rohit Ghosh, founding member of Qure.ai, revealed how their solutions have played a crucial role in positively impacting patient outcomes – whether in public health screening programs , primary care, tertiary care, private care and ministry. of health. Below are edited excerpts from the interview:

Q: The Covid-19 pandemic has cemented the use of AI in public health, from radiology to preventative health checks in India. What sets Qure.ai apart from other companies using AI/ML for radiology?

A: AI-enabled healthcare has become a nascent industry across the world, especially since COVID-19. The use of AI in global health scenarios will become widespread as the technology becomes more nuanced and applied. This is where Qure.ai has the upper hand over its competitors. The biggest difference is that we are present in over 600 locations in over 60 countries in UK, USA, SEA, LATAM, Australia and Africa which makes us the solution to The most deployed AI in medical imaging in the world. That’s the difference.

During COVID, we have worked with partners from all walks of life – including the Oman Ministry of Health, for patient monitoring; NHS Bolton in the UK, one of the first NHS Trusts to adopt AI-assisted technology for COVID detection, and BMC in Mumbai, India, for testing and monitoring COVID-positive cases.

The sheer volume of analytics we were able to process validates the scalability of our team and the ease with which we can deploy AI, regardless of partner’s unique settings or workload. Our unique positioning stems from the fact that we are present at each point of contact in the care pathway. From public health screening programs to primary care, tertiary care, private care and the Ministry of Health, our solutions have played a critical role in positively impacting patient outcomes.

We work with major teleradiology companies like Medica – the UK’s largest teleradiology serving over 200 NHS Trusts as well as vRad which is the largest US teleradiology serving over 2,000 hospitals in the US . Similarly, Australia’s Lumius Imaging uses AI in more than 130 imaging centers. In the UK we have the most deployed AI solutions – over 20 NHS Trust hospitals are using the AI ​​solution. We work with the largest network of GPs in Malaysia while also working with MOH in countries like UAE, South Africa, Philippines, India etc. The diversity and huge scale of the user base is the real advantage we have.

Q: What are the challenges faced by doctors in adopting and leveraging AI in India? How do you work with relevant stakeholders to make this adoption transparent?

A: AI in healthcare is a rapidly evolving field with many players innovating across the board. The biggest challenge is that physicians are unaware of exactly how AI can help them deliver better patient care. Training radiology teams and resources to optimally use AI in their daily workflow will be crucial in driving change. Qure.ai works closely with partners to help their resources integrate the solution as seamlessly as possible and leverage it for the best results. Educating and informing professionals about the benefits of AI is crucial to changing its perception.

Q: How do you ensure that the quality of the data used in your AI and ML models is optimal?

A: Qure’s solutions are trained on one of the largest datasets on the market. Our algorithm is trained to automatically detect sub-optimal scans and report them if necessary. We ensure that the RN is regularly trained with the help of radiologists and specialists to maintain clinical accuracy. We do a double read with a strong ground check mechanism. We also use an ideal mix of optimal and sub-optimal analyzes to improve the sensitivity of our solutions. The conscious decision to include suboptimal and poorly captured analytics in the dataset was intended to make it sensitive to them when deploying to the real world. Our AI can read and report images captured using portable devices like a mobile camera in the case of analog X-ray setups. This makes it extremely relevant in remote deployment sites in developing markets. It is also able to read scans from different machines with similar accuracy, making it machine independent. The variety and amount of data coupled with proper annotation makes AI extremely robust.

Q: Tell us about your plans for expansion in India.

A: We are deeply integrated into the country’s public health infrastructure – our solutions are used across the group – from government PHCs to NGOs and private healthcare providers. About 24,855 rural PHCs and 5,190 urban PHCs are operational in India.

Our goal is to be present in each of them and to provide an equitable level of care, regardless of location. With programs like Ayushman Bharat, our goal is to help strengthen the comprehensive primary healthcare system for all of India. Currently, Qure.ai has been working with NITI Aayog and Municipal Corporation for Greater Mumbai (MCGM) for a few years and continues to be their AI partner in all districts to actively screen and test for TB and other lung diseases.

PATH, an international NGO, was also an early adopter of AI for TB using chest X-rays and other innovations. More recently, we have received funding from USAID under the Samridh program for lung health screening in selected locations in India and have an active grant from IHF which allows us to meet the needs of several networks of rural and semi-urban health care and increase their lung health care pathways.

Q: How do you keep patient data/information secure in the cloud?

A: As a responsible healthcare technology provider, we are committed to ensuring the safety and effectiveness of our AI software. Qure’s solutions are GDPR and HIPPA compliant. Our solutions also meet world-class regulatory standards — FDA and CE.

We have implemented rigorous cybersecurity controls to keep our information system up-to-date and secure. Additionally, we have data protected and encrypted at all levels, at source and in transit ensuring that all data is anonymized before it leaves a client’s premises for processing in the cloud.

Q: What has cloud technology enabled you to do better?

A: Qure.ai tackles AI challenges in healthcare and advances digital healthcare with medical imaging AI solutions. We are deployed through AWS cloud solutions on our sites. On AWS, we use EC2 for heavy processing and easy scalability with better performance. It gives us an SLA of 99.8%, which improves our performance while minimizing downtime.

We’ve also enabled automated backups and real-time failovers. Regarding data security and privacy, our data is stored in S3 for better data security, scalability, performance and availability.

In addition, we have RDS for database reliability. We use CloudTrail and CloudWatch which monitor and log server and account activity across the AWS infrastructure. Additionally, we have AWS WAF, which is a perimeter-level web application firewall to secure our web applications and APIs against malicious traffic, web exploits, botnets, and more.

Being on AWS has many benefits, with cost-effective scalability and compliance being the main ones. We can deploy our solutions in the most remote regions of the world, powered by AWS, while controlling the costs incurred.

Being globally present also comes with the added responsibility of being compliant with the region’s respective data privacy guidelines, AWS also plays a crucial role in this regard.

–IANS

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(Only the title and image of this report may have been edited by Business Standard staff; the rest of the content is auto-generated from a syndicated feed.)

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