As we grapple with the coronavirus (COVID-19) pandemic, the pattern of viral spread may have been identified as early as Dec. 31, 2019, by Toronto-based BlueDot.

The group identified an association between a new form of pneumonia in China and a market in Wuhan, China, where animals were being sold and reported the pattern a full week ahead of the World Health Organization (which reported on Jan. 9) and the U.S. Centers for Disease Control and Prevention (which reported it on Jan. 6).

Dr. Kamran Khan, a professor of medicine and public health at the University of Toronto, founded the company in 2014, in large part after his experience as an infectious disease physician during the 2003 SARS epidemic.

The BlueDot team, which consists largely of doctors and programmers, numbering 40 employees, published their work in the Journal of Travel Medicine.

“Our message is that dangerous outbreaks are increasing in frequency, scale, and impact, and infectious diseases spread fast in our highly interconnected world,” Khan wrote via email. “If we want to get in front of these outbreaks, we are going to have to use the resources available to us — data, analytics, and digital technologies — to literally spread knowledge faster than the diseases spread themselves.”

In the past, BlueDot has been able to predict other patterns of disease spread, such as Zika outbreak in south Florida. Now its list of clients includes the Canadian government and health and security departments around the world. They combine AI with human expertise to monitor risk of disease spread for over 150 different diseases and syndromes globally.

AI for cancer screening

BlueDot, as a company, speaks to the emerging trend of using AI for global health.

In India, for instance, Aindra Systems uses AI to assist in screening for cervical cancer. Globally, one woman dies every two minutes due to cervical cancer, and half a million women are newly diagnosed globally each year: 120,000 of these cases occur in India, where rates are increasing in rural areas.

Founded in 2012 by Adarsh Natarajan, the Aindra team recognized that, in India, mortality rates were high in part due to the six-week delay between collecting samples and reading pathology during cervical cancer screening programs. It was also a human resources issue: in India, one pathologist is expected to serve well over 134,000 Indians.

With the aim of reducing the workload burden and fatigue risk (misdiagnosis rates can increase if the reader is tired and overworked), Aindra built CervAstra. The automated program can stain up to 30 slides at a time and then identify, through an AI program called Clustr, the cells that most appear to be cancerous.

The pathologist then spends time on the flagged samples. Much like traditional global health programs, Aindra works closely with several hospitals and local NGOs in India, and hopes their technology may later be adopted by other developing countries.

“Point of care solutions like CervAstra are relevant to a lot of countries who suffer from forms of cancer but don’t have infrastructure or faculties to deal with it in population based screening programs,” Natarajan says.

Natarajan also points to other areas where AI is relevant in global health, such as drug discovery or assisting specific medical specialists in areas like radiology and pathology. Accenture was able to use AI to identify molecules of interest within 10 months as opposed to the typical timeline of up to 10 years.

Teaching the next generation of innovators

The Vector Institute, based in Toronto, is also plugging into the potential of AI and global health. It works as an umbrella for several AI startups, some with a health focus and all aiming to have a global impact.

Melissa Judd, director of academic partnerships at Vector Institute, points to the United Nations’ sustainable development goals as a framework upon which to help orient AI towards improving global health. Lyme disease, for instance, is a global health issue that also comes up against the topic of climate change, and recently a Vector-supported AI initiative was able to identify ticks that spread of Lyme disease in Ontario.

Last December, the Vector Institute launched the Global Health and AI Challenge (GHAI) — a collaboration with the Dalla Lana School of Public Health to engage students from across the University of Toronto (from business to epidemiology to engineering) in critical dialogue and problem solving around a global health challenge.

The potential of AI for global health is immense. Major academic journals are also taking note. Last April the Lancet launched the Artificial Intelligence in Global Health report. By looking at 27 cases of how AI has been used in healthcare, editors proposed a framework to help accelerate the cost-effective use of AI in global health, primarily through collaboration between various stakeholders.

As well, a recent commentary in Science identified several key areas of potential for AI and global health, such as low-cost tools powered by AI (for instance an ultrasound powered through a smartphone) and improving data collection during epidemics.

Yet, the authors caution against seeing AI as a panacea and emphasize that empowering local, country-specific, technology talent will be key, as inequitable redistribution of access to AI technology could worsen the rich-poor divide in global health.

This warning aside, Khan with BlueDot is optimistic.

“We are just beginning to scratch the surface as there are many ways that AI can play a key role in global health. As access to data increases in volume, variety and velocity, we will need analytical tools to make sense of these data. AI can play a really important role in augmenting human intelligence,” Khan says.

Amitha Kalaichandran, M.H.S., M.D. is a physician, writer and 2019-2020 Asia Pacific Foundation Media Fellow based in Toronto.

Source link