AiThority Interview with Gene Saragnese, CEO, MaxQ AI

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Tell us about your journey through Healthcare? What made you associate with MaxQ AI?

I’ve always been fascinated by technology and its potential. An engineer by training, I started my career in aerospace working with satellites; I actually worked on team that sent something to Mars. Two decades ago, I moved into healthcare, first with GE Healthcare as GM of MR, then CTO, and finally as VP & GM of Molecular Imaging & CT. Next, I joined Philips as CEO of Imaging Systems. Over the years, I’ve seen first-hand the mounting pressure on the healthcare system – a shortage of healthcare providers attempting to treat more patients with less money per patient. Combining my love with technology and my deep healthcare experience, it was clear that I wanted to lead a company that was leveraging cutting-edge technologies such as artificial intelligence and machine learning to solve some of these big problems. When the opportunity presented itself to join MaxQ AI as CEO, I jumped at the chance to help lead an innovative startup that is leading the way in a new category – Medical Diagnostic AI solutions.

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What are some of the challenges that MaxQ AI faces in integrating Artificial Intelligence into Medical Technology?

To unleash the potential of AI, we must understand the challenges of integrating big data in healthcare. In healthcare, the data is not structured and it is noisy. This is a classic signal to noise problem, and many cases it creates confusion about the best way to use all of the data that is already being generated from disparate sources. For healthcare AI solutions to truly succeed, we must have sufficient and accurate data with ground truth. In addition, for the organizations that are holding onto their data, it is critical to encourage them to make it available for integration in a responsible and secure fashion.

 How can Healthcare benefit from Big Data?

Harnessing Big Data has great potential for healthcare, but again we must build the bridge between the physiological and the mathematical to make the most efficient use of AI technology and data. To be successful, we need solutions that structure data and create algorithms that are scalable. At MaxQ AI, we’re not in the business of building just one algorithm or one application. We’re in the business of building a platform that we can use repeatedly. And our platform is designed to interpret any type of image along with the surrounding patient data. This is key to ensure broader adoption of AI and real, sustainable impact in healthcare.

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Tell us how comprehensive the Accipio platform is?

It is very comprehensive – already the recipient of key regulatory approvals and the rare FDA Breakthrough designation – our AI-powered Accipio™ suite of software utilizes deep learning technologies to analyze medical imaging data such as non-contrast head CT images. Our complete Accipio intracranial hemorrhage (ICH) platform will support the Radiologist, Emergency Room and Neuro-rad teams with a fully-automated solution, providing identification & prioritization (lx), annotation (Ax) and expert-level diagnostic rule out (Dx). By improving the detection of ICH, our solutions enable faster and more accurate treatment of stroke, head trauma and other life threatening conditions. In addition to FDA Breakthrough status, we hold key regulatory approvals including FDA clearance and CE Mark for our Accipio Ix^TM solution, which is the first part of our AI ecosystem. Our solutions are Class II and Class III medical devices with significant clinical evidence burden of proof; and will have great potential across several applications. In addition, our clinical decision support platform can be natively integrated into any PACS systems, medical imaging hardware or healthcare clouds.

In the near future can we expect AI/ML to take over EHR Systems also? If yes, then how?

Again, data in healthcare is noisy and unstructured, and EHR Systems are only one piece of the puzzle. It is all about integrating systems to provide augmented intelligence to empower actual caregivers. For example, today’s diagnostic AI solutions have the potential to have a significant and positive impact on healthcare, but it is important to explore the technological pathway toward a full diagnosis and reaching AI’s full potential. A perfect example of this is the emergency room, which must triage incoming patients and make decisions as quickly as possible. In the case of a suspected stroke or head trauma patient, the clinical team must rapidly identify whether the person is experiencing an ischemic stroke or a haemorrhagic stroke and then determine the most appropriate treatment pathway in a very short time. This requires a much richer and deeper picture of the patient, considering all the data available including everything within the images, but to also consider everything concurrently surrounding the patient such as data from EHR systems, to provide a complete differential diagnosis on the patient.

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Which events and webinars would you suggest to our readers as being the best in grasping information on emerging technologies?

There are some excellent conferences looking at AI and deep learning in healthcare including the recent FORTUNE Brainstorm Health and WSJ Tech Health conferences. For the space we focus on, radiology and acute care, the Radiology Society of North America (RSNA) is running a series of webinars and AI-focused ‘Spotlight’ events that are worth checking out. And, of course, RSNA’s annual conference in Chicago – the week after Thanksgiving – is a critical event for our society. In addition, I’d recommend healthcare informatics shows and conferences such as HiMSS. There is so much hype around AI, it is important to look at the audience and ensure that you are truly engaging a mix of technology providers, providers and actual patients.

What are the biggest challenges and opportunities for AI companies in dealing with rising technology prices?

As we all well know, costs in healthcare are skyrocketing in general, not just in technology. The healthcare sector today is under a level of pressure that is unprecedented, with less money than ever before and a shortage of healthcare providers attempting to serve a growing number of patients. For hospitals, imaging equipment is a major capital investment, and AI-powered solutions that are easy-to-integrate into a providers’ PACS and CT systems have the potential to increase productivity, performance and value for these machines. The important thing is to ensure that hospitals work with AI platform vendors that can deliver more than analysis, that their solutions are providing answers. AI diagnostic solutions need to enhance productivity while improving outcomes and quality of care.

How do you see the future of AI in Healthcare?

I think it will have a profound impact and it will transform the way we deliver care. For example, AI will have a significant and positive impact on radiologists. Today, radiologists are readers of images, providing deep insight by translating this information and providing insightful reports. However, often the actual diagnosis is done by another physician without deep insight into imaging, and without concurrently considering the characteristics of what is seen in the images and the rest of the information around the patient. By embracing AI-driven solutions, I contend that radiologists are poised to become the most sophisticated group of diagnosticians in healthcare.

In addition, by dramatically improving a physician’s ability to make a faster, accurate and more confident diagnosis, I believe that AI solutions such as our Accipio platform hold significant potential to improve the quality of care in emergency rooms in rural and community hospitals across the globe. The potential is massive – if our solution can divert only one patient per year in the 13,000 US/EU acute hospitals to wellness care from stroke care – that would represent $2 billion in savings in the first year alone and a lifetime of difference to the patient and their families.

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What AI-focused start-ups are you keenly following?

Clearly, I follow closely the start-ups in the medical diagnostic AI space. Given the close relationship between imaging and pathology, there are several innovative start-ups that have embraced AI-powered pathology solutions that are doing fascinating work. In addition, I’m interested in seeing what a variety of AI-focused businesses are doing outside of healthcare – in particular, those start-ups that are shaping different sectors from retail to financial services.

Other than its applications in Healthcare, what other spheres of AI interest you?

As a technology enthusiast, I’ve followed closely how Netflix, Amazon and others have integrated AI to be a seamless part of our lives. These companies have effectively changed consumer behaviour, retail experiences and how we consume entertainment. As I mentioned, healthcare has its challenges, but could learn a great deal from how these companies were able to effectively harness data from diverse and disparate sources. In addition, on technical level, there are valuable lessons to be learned as it relates to cybersecurity and data protection issues that the retail sector has had to overcome. In healthcare, we must look at these lessons and ensure that data can be integrated in a fashion that is both responsible, simple and secure.

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Tag the one person in the industry whose answers to these questions you would love to read.

Seema Verma, U.S. Centers for Medicare & Medicaid Services (CMS) Administrator. It would be great to get the CMS perspective given their recent CMS Artificial Intelligence Health Outcomes Challenge.

Thank you, Gene! That was fun and hope to see you back on AiThority soon.