Coronavirus. A couple weeks ago, this virus was virtually unrecognizable at beginning of January, but it has suddenly magnified into an international health crisis. There are already more than 71,000 people in the world infected with the this virus identified as SARS-CoV, and the death toll has risen to 1,873 at the time of publication, according to data from the Center of Disease Control in the United States. [https://www.cdc.gov/coronavirus/2019-ncov/summary.html]
The current strain of coronavirus originated at open market in Wuhan, in which contaminated bat and other mammalian meat that contained the virus was spread to citizens all over Wuhan. Coronavirus is known to behave and be transmitted in a manner similar to that of SARS and pneumonia, which presented a containment issue for health authorities. Due to the initially perceived innocuous nature of this outbreak, essential precautions were neglected, and response time was delayed days behind, which contributed to the devastatingly fast spread of the virus.
Sadly, there were multiple signs and whistleblowers which indicated the devastating nature of the disease that occurred early on, including numerous doctors and smaller artificial intelligence start-up companies. [https://www.nytimes.com/2020/02/06/world/asia/chinese-doctor-Li-Wenliang-coronavirus.html]Early January outbreak of pneumonia-like symptoms from markets in Wuhan. But BlueDot, an artificial intelligence system, captured remarkably days earlier through analysis of airline ticket data, words from online forums and large datasets of the financial markets based in Wuhan. The speed and accuracy of the artificial intelligence allows for machine learning systems to predict the spread of highly infectious diseases, like SARS, avian flu and annual influenza.
The inception of artificial intelligence during the early stages of computing coincided with the post-war development of international health approaches. Artificial intelligence was conceived theoretically in the 1950s during beginning of computing revolution. Machine learning and artificial intelligence relied on feeding data into a repeated vetting process through bots that refine system’s algorithm. The practical concept of artificial intelligence actually took off in the 1990s, in which basic tasks such as self-adjusting models and other modes of regression that are based from the data. [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/]
Concurrent with the development of artificial intelligence, the frameworks of approaching global health were moving towards being more polarized between applying post-war technology to addressing global health issues or investing in sustainable local community development. The community health of developing nations became increasingly tied to the economic and commerce interests of developed nations, which led to massive eradication campaigns that would establish many of the same disease containment frameworks employed in recent outbreaks, such as Ebola or Zika. The focus on sustainable global health was only recently a modern phenomena, which focuses more on organizations working closely with local communities to build health infrastructure that can dampen the impact of highly infectious diseases. [Farmer, Paul et al. Reimagining Global Health, “Colonial Health and Its Legacies”]
Presently, artificial intelligence is employed in imaging and radiology application to predict areas of injury and pneumonia. Natural Language Learning and other modes of word processing is used in organizing electronic health records. Artificial intelligence and basic machine learning principles are already integrated into our phones with nutrition tracking and other similar systems.
Established technology companies, like Google, Babylon Health, and a variety of start ups are also looking to apply artificial intelligence in collecting data and monitoring the collective health of communities. Increasingly big data approaches and more powerful AI approaches would contribute to novel data collection techniques and publicly open source databases used by startups and local NGOs. Additionally, the system of tracking disease spread and preventative measures will be integrated into mobile phone technology.
However, looking into the future of global health, artificial intelligence can produce faster response to epidemics and disease outbreaks, more information available and faster decision-making about high risk areas. Optimizing expenditures of NGOs is another frontier AI can tackle, adapting to immediate needs with cost effective care in rural regions of China, India, and Africa. Cost effective care can come from AI helping clinicians diagnose common disesase, which reduces waiting time and operation costs. This, however, requires an increased development of telemedicine infrastructure in developing areas of Africa, India and Latin America
Google is exploring three areas of implementing artificial intelligence into health care. Diabetes detection is still difficult to implement of a larger scale due to the expensive and time-consuming tests. From Cardiogram, doctors can track the heartbeat pattern of patients as a cost-effective means of screening for diabetes. Artificial intelligence systems can help detection through generating models on patient datasets, which can prove beneficial for the wellbeing of communities in developing nations. [Insert CB Insights on Google] [https://www.cbinsights.com/research/report/google-strategy-healthcare/#aidisease]
The role of tech giants and established corporations is to financially invest and provide infrastructural supports for startups in niche areas of disease intervention. Recent startup companies provide a fresh, innovative vision that has not been influenced by investor’s demands and corporate affairs, which benefits larger corporations by providing a new avenue for diversifying services.
Unlike physical pharmaceutical interventions, such as devices or novel drugs, artificial intelligence systems require an initial investment in electric and computing infrastructure. Logistical costs of transporting medicines and training providers is comparable to installing artificial intelligence infrastructure, as electronic technology costs decrease and processing power increases. Additionally, building artificial infrastructure in developing nations would also accelerate economic development as local industries can shift towards service sectors and increased access to information for students. Human capital and inherent financial capital would increase through investment in electronic infrastructure.
Babylon health’s project focuses primarily on the application of artificial intelligence to assisting clinicians make diagnoses. Babylon Health is looking to create an app powered artificial intelligence systems that streamlines care through to remotely monitoring and connecting patients for an English city of 300,000 individuals. If the system is proven efficient, Babylon Health will expand this project to markets in areas where nationalized health care cannot effectively meet the needs of all citizens, such as Rwanda or Nigeria. Babylon health is most well known for their services in an assistive diagnostic program for providers that improves the accuracy and speed of diagnoses. Babylon’s AI diagnostic system grows more adaptive with more patients served from more diverse backgrounds, making this service highly beneficial for developing nations in short supply of trained practitioners.
Corporations developing artificial intelligence systems several technological, ethical and political challenges face. Implementing artificial intelligence requires the existing digital infrastructure, such as a reliable electrical infrastructure and internet to process large amounts of data. Additionally, monitoring technologies work best in populations with a high level of mobile device ownership, which may not be feasible for areas of individuals predominately living in poverty. [https://www.internationalsos.com/client-magazines/in-this-issue-3/how-ai-is-transforming-the-future-of-healthcare]
As the global health and economic environment is becoming increasingly digitized and interconnected, artificial intelligence is a promising means of improving health systems to efficiently serve a growing patient base for cheaper. New artificial intelligence technology can accurately predict the spread of infectious diseases, as seen with the Coronavirus outbreak, as well as assist clinical diagnostics and monitor population health. It is important that larger established companies recognize and invest in the intellectual capital of recent start up projects, and that government policy facilitates growth and constructive research in artificial intelligence systems.