Artificial Intelligence has a potential role to play across the industries in today's world. The ongoing digital transformation has led to its wide adoption. The Healthcare industry has been a great beneficiary of this rapid technology adoption. Disruptive technologies like AI and machine learning have enabled better patient monitoring, faster drug discovery, and improved diagnostics. Several companies and startups have also emerged as healthtech solution providers in recent years.

Speaking with Analytics Insight, Chaith Kondragunta, CEO of AIRA Matrix Pvt. Ltd., sheds light on the various innovations of AIRA Matrix and the impacts of its AI solutions in the healthcare and pharma industry.  He also gives insights on the role of disruptive technologies in detecting and treating many health conditions

Kindly brief us about the company, its specializations, its products, and the services it offers.

AIRA Matrix provides Artificial Intelligence-based products and services for image and data analysis in the Life Sciences industry. We offer solutions that enhance workflow efficiency, provide time and resource savings, increase accuracy and improve objectivity for the healthcare and pharmaceutical industries. Our capabilities include Deep/Machine Learning and Predictive Analytics. Our products and services are applicable in diverse areas like drug discovery, preclinical drug safety assessment, cancer diagnostics, ophthalmology, and environmental monitoring.

Give an overview of your role at AIRA Matrix and your contributions to the industry.

As the CEO, Chaith sets the strategic priorities for AIRA Matrix and focuses on increasing the company's value. He is responsible for driving the company's growth, product portfolio expansion, and revenue generation activities.

In our early years, we necessarily were more R&D-oriented, simply because the challenges were more pure research. Now our challenges are to transform all of the good R&D into meaningful products and solutions. I see this as a large part of my current responsibilities. This change in focus as well as overcoming data challenges have provided us the springboard for growth. While our company is undertaking some highly ambitious AI projects, the bulk of our efforts are devoted to meaningfully improving our core operations. While less transformational individually, a series of such projects can add up to the major changes in a product or process.

Providing meaningful AI products to the marketplace, especially the nascent market we currently encounter, will automatically generate a lot of momentum. It will also help transform the industry from a push-driven to a pull-driven one.

What was the mission with which the company started and what was your journey since its inception?

The mission of the company is encapsulated in our tagline- "Digital Intelligence- Objective Outcomes". Our Deep/Machine learning-based products and services focus on converting data and image information into objective intelligence to improve process outcomes across a range of Life Sciences applications. The company was conceptualized about a decade back and was initially focused on optimizing safety assessment reporting in pre-clinical drug development studies. This is the step before even advancing a drug candidate to clinical trials. We created novel context-based image triaging solutions for analyzing toxicity effects of new drug candidates which helps pharmaceutical companies save time and resources.  We have since diversified into healthcare diagnostics (Oncopathology, Precision Diagnostics, Ophthalmology screening) as well as Environmental monitoring for regulated domains with cleanroom restrictions. Our technology expertise has taken commensurate strides as well – from hand-crafted image analysis solutions to self-learning-based AI inferencing techniques.

How is the company contributing to the AI industry of the nation and how is it benefiting the clients?

Our solutions enable improved outcomes in two key areas affecting healthcare: a) Drug Discovery and Development and b) Healthcare diagnostics.

In the drug discovery and development area, our solutions help bring innovative drugs to market, with a reduced development time and optimized costs. This translates into benefits for drug manufacturers and also for patients. The current healthcare crisis in the country highlights the need for solutions that speed up go-to-market time for drugs and vaccines, especially in unprecedented crunch situations like the present one.

On the health care side, we develop AI-driven solutions that aid precision diagnostics and personalized healthcare. These help physicians make the shift from conventional one-size-fits-all regimens to personalized therapies tailored to individual patients. Our predictive analytics solutions aim at the early detection and diagnosis of cancer, blindness-causing diseases, and neurodegenerative disorders like Alzheimer's. Our goal is to help initiate timely intervention and alleviate healthcare cost burdens for the patients, the healthcare providers, and the nation.

What makes the company innovative and what are the key partnerships and involvements done to drive the innovation?

Our innovation comes from a simple desire – to create leading-edge AI solutions that would be regarded as the best in the world. We have been fortunate to have great support from our investors that enables us to take risks, not fear failure, and relentlessly innovate. We also strive to have a company culture that fosters new ideas. All of us know that everything we are attempting is very novel and we are only going to succeed if we are not afraid to fail.  It also helps that our leadership team has a track record in backing and building novel solutions.

AIRA Matrix collaborates with some of the best minds in the various Life Sciences domains and in AI technology and with leading research and academic organizations across the world. Our customers are also our partners, and are leaders in the pharmaceutical and healthcare industry; they help ensure the relevance and applicability of our solutions in the real world

How do you think disruptive technologies are impacting today's innovation and how has the role of a leader evolved over the years?

Life Sciences with the voluminous data it needs and generates is ripe for disruption. From Cure to Care. Classic disruptive innovation satisfies customers' future needs. Even if it may provide lower performance in some key features at present, it also creates some unique features valued by the market. Today, AI solutions in Life Sciences are largely at this inflection point. For example, Pathology is at the center of diagnosis, and diagnosis underpins a huge percentage of all patient care. Pathology also provides the challenge of analyzing large information sets to arrive at a diagnosis. AI can help automate routine, high-volume tasks, prioritize and triage cases to ensure patients are getting speedy access to the right care, and make sure that pathologists don't miss key information hidden in the enormous volumes of clinical and test data they must comb through every day.

AI technologies have transformed from being evolutionary to becoming more revolutionary. And leadership requirements need to embrace this change. At the same time, AI-driven leaders must help to assess the potential for full-scale implementation before embarking upon pilot projects.

Kindly share your views on the current scenario of AI in healthcare and its future.

To date, AI-based automation has been applied in healthcare processes to offer the much-needed improvement in objectivity, reproducibility, repeatability, and speed with a significant reduction in manual errors. AI applications are now moving from being simple automation tools to sophisticated solutions that enable informed decision-making. AI is able to identify patterns in healthcare data that are not apparent to the human observer and can predict outcomes that surpass the computational ability of the human brain. This has already opened new paradigms in precision diagnostics and personalized medicine.

Going forward, AI holds the promise of playing a significant role in disease prevention, by predicting disease and its progression path, before the symptoms fully manifest. This opens a proactive approach to disease prevention and healthcare. In the near future, AI applications can also help translate intelligence gained from the 'gold standard' but invasive diagnostic procedures like histopathological examination of biopsies to improve performance of less accurate but non-invasive procedures like radiology imaging. This type of AI application provides the promise of easing the procedural burden on the patient, reducing patient morbidity, and making healthcare diagnostics cost-effective.

Kindly mention some of the major challenges the company has faced till now.

Data is obviously one of the biggest challenges. Scarcity of standardized, well-labeled data, in addition to class imbalances in the data, are major problems we face in our product development. Patient-related data adds concerns like data security and integrity and necessitates we pay the utmost attention to these challenges. Attracting the right kind of talent is very important for a company like ours. We spend a lot of time hiring the best, especially in the deep learning and data analytics fields.

The 'yet to be fully addressed' regulatory landscape concerning AI-based health care diagnostic solutions is another major challenge for the rapid adoption of our products and solutions. Fortunately, regulatory agencies like the FDA are now taking positive steps in this direction with publications of references and guidance for the industry and providing a clearer regulatory pathway.

Can you throw light on the future prospects of the company?

In Oncology, we are working on networks for predicting the course and progression of disease in prostate cancer patients. We are also developing solutions that can predict patient response to therapy in lung cancer. In Ophthalmology, one of our projects is aimed at developing a solution for predicting the onset of neurodegenerative disorders like Alzheimer's and Parkinson's. based on non-invasive modalities like retinal scans. In addition, we are working on developing analytics to predict the toxicity of drug molecules at the very early stages in the drug development process to make the process faster and cost-effective. Our focus in the future is hence on predictive applications of Artificial Intelligence that augment workflows and improve outcomes in healthcare.