Artificial Intelligence: A new frontier for Telemedicine and Public Health
Can AI be used to prevent another Ebola outbreak or help deliver medical care to remote outskirts?
From the search engine you use every day to self-driving cars, it should come as no surprise that Artificial Intelligence (AI) is making tremendous breakthroughs in almost every field. The field of Healthcare is no exception as well; AI has proven its potential, with Google’s Lung Cancer detection from CT scans outperforming even Doctors!
Artificial Intelligence, a term too vast for a single definition, can be objectively described as the process of imparting a machine with an ‘Intelligence’ often called ‘models’, such that it can make its own decisions or draw conclusions from given data.
Telemedicine
Now, how exactly can this be extrapolated to ‘Telemedicine’ or the delivery of Healthcare at a distance? Let us start with its definition from the World Health Organization(WHO).
Telemedicine is defined as the delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information for the diagnosis, treatment, and prevention of disease and other medical applications.
Although there is some ambiguity in the term ‘health care services’, the implications are quite clear, for that matter. Wherever and whenever a place, hospital or even a person faces a shortage of any kind of medical facility and are limited by the virtue of location, the science of addressing those needs is what Telemedicine stands for.
Thus, it is a technological extension of the Right to Health as stated by the WHO.
The rise in Telecom and IT has made it possible for even a pregnant mother in the remote outskirts of Mongolia, to receive sufficient maternal healthcare from hundreds of kilometers away. Being able to consult with the specialists from the National Centre for Maternal and Child Health in the capital Ulaanbaatar, this network overcame the barriers of unpaved roads and high transportation costs in the country.
Artificial Intelligence and Cloud: The dynamic duo for Telemedicine
Artificial Intelligence indeed allows powerful models for the detection and diagnosis of diseases, but there are certain problems regarding its implementation.
Training a model and even deploying one on-site requires versatile hardware like GPUs, storage, etc, and the problem is amplified in the context of Telemedicine as you can’t really expect such facilities in a village in Sudan or Ethiopia. That’s where the Internet and Cloud Computing come to the rescue!
Cloud technology, in addition to making it possible to store large amounts of data virtually, enables the deployment of AI models online too. With the rise of Google Cloud, Amazon Web Services and Azure, this technology is more accessible than ever before. So Cloud reduces the financial and technical requirements on the other side of the network and paves the way for AI in Telemedicine.
Data Science and Public Health
Data science is the field of using algorithms and methods to gather knowledge from structured and unstructured data, and is closely connected with the field of “Artificial Intelligence”. In 2016, IBM estimated that 90% of all the world’s data then, had been generated within the previous 2 years! In this data-driven world where even IT firms have the power to change the outcome of National Elections, what does it mean for the fields of Public Policy and Healthcare?
Public Health is the science of protecting and improving the health of families, communities, and entire populations throughout the world. Epidemiology is a focus area within Public Health, which deals with the prevention and assessment of diseases and their outbreaks.
In 2014, Ebola was one of the worst epidemic outbreaks, which caused the death of 11,000 people and bought the socioeconomic collapse of the countries Sierra Leone, Liberia, and Guinea. It was accepted that the lack of sufficient disease surveillance systems in Ebola-affected areas led to the inability to respond locally or even curb its spread.
A digital surveillance system where data in the digital domain like internet search metrics or online news stories can be used to obtain knowledge of such outbreaks. People who contract a disease are likely to search for information regarding their condition, and by monitoring the frequency of those searches it is possible to know the extent to which a community has been affected.
HealthMap, a disease mapping tool that applies the approach discussed above, was actually able to detect Ebola, dubbed as a “mystery hemorrhagic fever”, a week before it spread, although the founders had not realized then the importance of what they found.
While the issues of privacy must be addressed, this “Digitial Epidemiology” may prove to be a viable method and can help save the lives of thousands in the future!
Also read
Do follow us on FB, Instagram as well as on Medium for updates on different sociopolitical issues.
