In April last year, a medical device powered by artificial intelligence (AI) received approval from the US Food and Drug Administration (USFDA), marking a historic moment in healthcare globally. The IDx-DR, a software algorithm that uses AI to analyse images of the eye using a camera, achieved an 87.4% accuracy rate while detecting ‘more than mild’ diabetic retinopathy, a condition where high blood sugar levels damage the blood vessels in the retina.
For IT services firms, which are already developing AI and machine language (ML) tools for other uses and industries, extending AI and ML capabilities to healthcare is a fairly non-complex process, and comes with a large upside. Rather than doing it entirely on their own though, these companies are partnering hospital chains and niche players in the field to acquire the required domain expertise. For instance, Japanese technology firm NTT DATA Services tied up with Pune’s Deenanath Mangeshkar Hospital last year to use an AI-based solution to diagnose emphysema, a chronic condition of the lungs.
Over a six month period, the detection rate of its proof-of-concept solution turned out to be 170% higher than traditional systems. “The use of AI to automate insights and tangentially improve the process of care delivery helps healthcare providers who are under increasing scrutiny for quality, often being asked to do more with less time and resources, and in each case, challenged by the amount of digital information that each physician must integrate to make a clinical decision,” said Mitchell Goldburgh, global solutions leader for the company’s enterprise imaging and analytics practice.
NTT’s solution leveraged on its two-decade-long experience in integrating clinical imaging with newer AI tools to analyse images. The company is currently working with partners and running trials in countries including India, the US and Japan. “We have been in the early stages of commercialising the AI components on our platform – providing access to clients for pilots around evaluating the clinical effectiveness of AI in clinical imaging,” Goldburgh said.
Many of its clients are thinking of participating in this AI boom, he said, adding that the programme allows clients to contribute curated data to develop AI solutions in exchange for early access to algorithms and to make money off their data.
“There are various aspects to using tech in healthcare — predictive diagnostics, patient experience and connecting them with caregivers and the ecosystem of service providers. Adding AI to existing equipment can make the process more efficient,” said Rakesh Barik, a partner at consultancy firm Deloitte India. Not too far from Deenanath, home-grown technology services provider Persistent Systems has been using AI and ML in healthcare, specifically predictive diagnostics.
The company has partnered with Prashanti Cancer Care Mission to develop a platform that will identify new markers in patients with triple negative breast cancer, in order to detect the disease early. Separately, Persistent is thinking of partnering another customer in the US to create a solution for early detection of lung cancer. The mid-tier software services company is also working with a US startup to develop a tool that will predict the likelihood of chronic kidney disease in Africans and Asians, and is working to extend that to people of other geographies.
The company has made acquisitions in this space to boost its healthcare solutions. Persistent has the requisite technology expertise, having worked on ML and predictive modeling for over a decade, a company spokesperson said. “These are the two critical areas for healthcare. We do high level work with predictive modeling as a tech company and partner with domain experts like labs to use our tech expertise in these areas,” she said, adding healthcare was an important vertical for the company.
India’s top IT services provider Tata Consultancy Services (TCS), too, has set up a research centre in partnership with Tata Medical Centre to develop technology for clinical trials, risk-adapted treatment, predictive outcomes and biomarkers. TCS provides solutions to develop a data-driven research platform, based on integrated analyses of clinical and laboratory studies.
At its Translational Cancer Research Centre, the focus is on developing partnerships that provide modern, affordable cancer therapy and also develop cost effective models of care. This includes innovatively designed clinical studies, biomarker analyses and basic research integrated into a modular research theme, parts of which TCS will be involved in. Technology behemoth Microsoft is also not too far behind, and has invested in the sector.
Last September, Microsoft partnered with SRL Diagnostics to source one million biopsy samples from patients diagnosed by doctors earlier, in order to train its AI system to detect cancer. It is also working with Apollo Hospitals to build an AI system that can detect heart irregularities in patients, to give them a health score. Cancer focus In India, the high cost of treatment is one reason why companies are focusing their energies on late stage cancers.
The incidence-to mortality rate for cancer here is among the highest in the world. The focus is on cancer treatment, given the enormity of the problem. According to an EY report, 55% of the breast cancer cases in India were detected at a late stage, compared to 11% in the UK. Similarly, for cervical cancer, 85% of the cases were detected at the third or fourth stage, against 25% in the UK. A large number of cancer deaths, especially in these cases, could have been prevented if the disease had been detected on time.
“Screening efforts are very poor and there is very low awareness, and this is a cultural challenge. Having low-cost, less-invasive screening solutions will democratise cancer care,” said Kaivaan Movdawalla, Partner – Performance Improvement Healthcare, EY India. So, tech companies are starting to see potential in cancer diagnosis and prediction, even as a clutch of homegrown startups has already come up with innovative solutions in this space.
Bengaluru-headquartered Niramai Health Analytix is one such startup that has rapidly gained visibility. Its portable technology uses big data analytics, AI and ML to screen breast cancer cases early and accurately.
“A large number of breast cancer deaths can be prevented. Having a less invasive solution using artificial intelligence and thermal imaging is something that’s well suited to diagnostic centres. Our solution is available in over 30 hospitals in India,” said chief executive Geetha Manjunath. The company is now developing AI-based computer-aided software to control the spread of River Blindness, a disease caused by a parasitic worm infection.
While Niramai has brought in an entirely new way of detecting cancer, the Pune-based Optrascan has developed a digital pathology solution that can replace microscopes in laboratories. Optrascan applies AI and ML algorithms to slides that pathologists work on, and has an accuracy level of over 95%.
On the other end of the spectrum, Bengaluru-based OncoStem Diagnostics has developed a solution that makes it easier for patients to test for recurrence of cancer. It potentially reduces cases where chemotherapy may be required, if the test on the tumour tissue shows it is a low-risk patient and not prone to recurrence, according to the company. While these companies may be working to create solutions for a specific disease, most of their tools can be extended to detect other diseases as well.
In January, Mumbai’s Wadhwani AI signed up as the official AI partner for India’s Central Tuberculosis Division. The company will work to address multiple challenges around TB care, such as case-load estimation at the district level using risk and transmission factors, and prioritisation of TB patients for health workers by classifying the risk of them dropping off from treatment.
The company is also creating a smartphone-based virtual weighing machine to allow frontline workers to screen for babies with low birth weight, in order to improve child health. One of the biggest challenges these startups face is gaining scientific acceptance, as most solutions that they develop need USFDA approval. For many startups, market presence and reach also remains a challenge.
Partnerships with large hospitals or corporations seem to be the best option to ensure their solution reaches as many people as possible. “While most of the work in coming up with low-cost, noninvasive solutions is being driven by startups, there are still grey areas. Who is responsible for an incorrect diagnosis because of a glitch – the software developer or the medical practitioner?” Movdawalla of EY India asks.
In such a situation, partnering with larger tech firms will make it easier for startups to gain acceptance and access to clients. For instance, technology leader Google is looking to develop an AI solution that is robust enough to accurately detect diabetic retinopathy using low-quality images.
The company has been working with Indian hospitals to better train its AI system. Last year, the AI solution began giving preliminary results to help doctors make a more accurate diagnosis, after Google partnered leading eye care chains Narayana Nethralaya, Aravind Eye Hospital and Sankara Nethralaya. “Today, we have volumes of data available. We can draw insights from this data and loop it back into the decision-making process.
We now have tools to make sense of both structured and unstructured data,” said Barik of Deloitte India. A lot will, however, depend on how effectively this data can translate into an AI-powered healthcare solution.
