Artificial Intelligence and Global Challenges — Good Health and Well-Being for People

Goal nº 3: “Good Health and Well-Being for People: Ensure healthy lives and promote well-being for all at all ages.”

How Artificial Intelligence technologies can play a central role in the achievement of the Sustainable Development Goals one by one.

The Sustainable Development Goals integrate a universal agenda that seeks to balance human prosperity with the protection of the planet. In 2015, leaders of 193 nations committed to reach 169 targets around 17 main goals by the year 2030. After going through SDG number 1 (No Poverty) and 2 (Zero Hunger) we move to the next one in line. To ensure healthy lives and promote well-being for all we analyze the AI potential to improve and save millions of lives.

“The leading project of the Scientific Revolution is to give humankind eternal life. Even if killing death seems a distant goal, we have already achieved things that were inconceivable a few centuries ago.” (Yuval Noah Harari. Sapiens: A brief history of human kind)

Today, pills, injections and sophisticated treatments can temporarily save us from a flood of illnesses and injuries that one day lead to an inevitable death sentence. Such treatments also protect us from innumerable daily pains and ills that the pre-modern individual has come to accept as part of live. The average life expectancy sprang from 25–40 years to 67 across the globe and it can go to about 80 in developed countries (Harari, 2018).

Each day, 17 thousand less children die than in 1990. Since the year 2000, measles vaccines have prevented approximately 15.6 million deaths around the globe. Worldwide, maternal mortality has fallen by almost 50% since 1990. In East Asia, North Africa and South Asia, maternal mortality has declined by about two-thirds. In 2013, new HIV infections declined to 38% in comparison to 2001. By 2014, there were 13.6 million people with access to antiretroviral therapy; the number was about 800 thousand in 2003. (UN)

In a parallel scenario, still in the same universe, more than 6 million children continue to die every year before their fifth birthday. Despite global progress, a growing proportion of child deaths still occur in sub-Saharan Africa and South Asia. Four out of five deaths of children under five occur in these regions. The rate of maternal mortality, which is the proportion of mothers who do not survive childbirth, is still 14 times higher in developing regions than in the developed ones. Only half of women in developing regions receive the recommended amount of medical care. By the end of 2017, there were 36.9 million people estimated living with HIV worldwide.

Between the extremities of these two completely different scenarios, the Sustainable Development Goal number 3 brings health and well being to the center of the 2030 agenda. To ensure healthy lives and promote well-being for all at all ages, this UN goal is broad and has several fronts: reduce mortality rates, end epidemics, increase disease prevention, ensure access to sex education, achieve universal health coverage, support vaccine development and improve training of health professionals in developing countries.

The improvement of care access through information and knowledge dissemination is also an important part of SDG 3. In an AI age data is powered with potential to become the main medicine in the fight and prevention of diseases. Breaking the oligopoly of data possession is one of the ways to make information widely available to guide new research and treatments. AI technology has the potential to transform health services and systems. Big data and artificial intelligence can open a precedent for remote care and mobile health to significantly change the practice of medicine and create new forms of medical care.

Maternal mortality

One of the key targets of SDG number 3 is by 2030, reduce the global maternal mortality ratio to less than 70 per 100.000 live births. According to a report made by WHO, UNICEF, UNFPA, World Bank Group and the United Nations, 99% of maternal deaths occurred in developing countries. By the end of 2015, 66% of those deaths would take place in sub-Saharan Africa. As the main causes of maternal mortality today are still hemorrhages, infections, complications related to unsafe abortions and pregnancy-related hypertension, most of those deaths could be avoided.

According to the United Nations, this situation could be different if access to quality health care is improved with well-trained and available professionals. We all know something about the AI potential in the creation of tailored treatments and accurate diagnostics but here the solution can be much simpler than that. In fact, we could take the example of a Brazilian startup known as medRoom. Idealized in 2015, the company applies virtual reality technology and gamification strategies to improve training for students and health professionals. According to the company, the program works similar to a flight simulator. While it ensures the user feels immersed in health practice it still allows the monitoring of learning.

While well-trained and available professionals still lack in some of the regions with the highest levels of maternal mortality, there is also another role for AI. AI can improve monitoring systems and quickly analyze data to come up with potential solutions to save mothers and babies. That is exactly the idea behind PeriWatch, an advanced perinatal system powered with technology to monitor the fetus and mother. PeriWatch notifies clinicians about patients whose conditions are worsening, it uses an AI based approach to continuously analyze fetal heart rate, contractions, labor progression, provides maternal vital sign alerts.

Newborns and children

Another target to ensure healthy lives and promote wellbeing for those of all ages is to end preventable death of newborns and children under 5 years of age. It includes the reduction of neonatal mortality to as low as 12 per 1.000 live births and under 5 mortality to as low as 25 per the same ratio. According to UNICEF’s data, in 2017, 2.5 million children died in the first month of life worldwide. This data presents a scenario of approximately 7.000 neonatal deaths everyday — 2 million of those deaths occurred between the first day of birth and the sixth day of life.

It is worth mentioning here an application called Ubenwa. Ubenwa, launched in Nigeria, uses AI to make diagnoses. The application aims to combat birth asphyxia, one of the largest causes of newborn deaths in the world. Ubenwa works by capturing a baby’s crying to analyze patterns in amplitude and frequency to provide an instant diagnosis. This instant identification allows health care professionals to take quick action to prevent death and various disabilities such as cerebral palsy, deafness and muscle paralysis.

When talking about preventing death of children under 5 years old, the current data provided by UNICEF, shows us a scenario where one in 26 children still die before reaching this age. Notwithstanding the progress made so far in reducing child mortality over the past few decades, an estimated 5.4 million children under age 5 died in 2017 — roughly half of those deaths happened in sub-Saharan Africa. Mortality rates among older children and young adolescents (aged 5–14) plunged by more than 50 per cent since 1990, however almost one million children died in this age group in 2017 alone. The global burden of child deaths is a call for imperative and intensive action to improve the survival chances of the world’s children.

Most children under 5 die due to preventable or treatable causes such as complications during birth, pneumonia, neonatal sepsis, malaria and diarrhea. Diarrhea was the third main cause of death and disease in children in Africa, accounting for more than 330.000 child deaths in 2015. If you read the first two articles of this series, you might be familiar with the AI potential to efficiently map poverty and hunger. The Sustainable Development Goals are interconnected and extend the mapping potential with AI technologies, big data and satellite images for target regions where children are most vulnerable to dying from disease can also be a great way to tailor appropriate interventions.

Epidemics

The third key target for SDG number 3 is to end the epidemic of AIDS, tuberculosis, malaria and neglected tropical diseases by 2030. It also includes combating hepatitis, water-borne and other communicable diseases. With an estimate of 36.9 million people still living with HIV worldwide in 2017 it feels that there’s much more to be done. Since 2000, new HIV infections among children declined approximately 58% due to scaled-up efforts to prevent mother-to-child transmission. However, from that 2017 estimate, 3 million were children and adolescents under 20 years old and about 19.1 million were women and girls.

Every day in 2017, about 4.900 people were newly infected with HIV and nearly 2.580 people died from its related causes, mostly because of inadequate access to prevention, care and treatment services. When talking about artificial intelligence and its potential to end the epidemic of AIDS we see researchers working on developing vaccines and advanced treatments and diagnosis using machine learning. With big data, machine learning and powerful AI algorithms we might not be so far from an HIV vaccine. While that doesn’t happen, IBM researchers are contributing to a significant breakthrough in AIDS treatment.

Using machine learning, the new technology can predict how long it will take for the virus to develop immunity to a specific cocktail. It can also forecast the right combination of drugs that works for the maximum period of time and provide doctors with a good idea of when the drugs may stop being effective. The effort is being conducted under the European Union (EU) project, named EuResist that works on optimizing treatments, including that of AIDS. The tool is accessible for free throughout the EU, and hits into data from across both Europe and Africa to discover the right combination of drugs that provides resistance for the maximum amount of time.

To end the epidemic of AIDS, information is essential and AI can be an important tool to also bring awareness and teach prevention through adapted and accessible learning. In what comes to other diseases such as tuberculosis, early detection is a key strategy and potential course of action for AI. In fact, one promising solution involving chest radiography called SemanticMD is able to achieve an earlier detection accuracy for tuberculosis greater than 95%. Empowered with deep learning technologies the equipment is trained on a growing database of thousands of images from the US, India, China, Eastern Europe and South Africa.

When talking about malaria, AI has had already an important role in the discovery of how to inhibit a specific enzyme found in parasites that cause the disease. According to a publication in the journal Scientific Reports, scientists have found that the triclosan, a component already used in products such as laundry soap, toothpaste, furniture and clothing can aid in the treatment of malaria. With the help of a robot called Eve, it was discovered that the triclosan can inhibit the enzyme of two different parasite species that causes the disease including its variants and that might lead to the development of new medications.

When the subject is to tackle neglected tropical diseases we cannot fail to mention AIME (Artificial Intelligence in Medical Epidemiology). In the area of public health, AIME has developed a platform used to prevent the incidence of Dengue, Zika and Chikungunya virus. This platform has already been used in Malaysia and Brazil and works by combining epidemiological research (with public health data and weather forecasting) and AI to predict outbreaks of these diseases in urban settings. The accuracy index of the analysis in Rio de Janeiro was 84.11% where it was possible to predict up to 3 months in advance the sites that would have a higher incidence of the diseases, which helped guide the preventive work to effectively fight the virus.

Role of AI

Innovative treatments for complex diseases, such as the cure for cancer, genetic manipulation, operating robots and bionic limbs. For most of us, this is not yet a reality, but it is not one difficult to imagine. In health systems, the benefits of AI can be numerous. From improved timeliness and accuracy in diagnosis to simplified disease monitoring and surveillance, AI enables quick responses in emergencies as a fundamental in distance and adapted learning. Other examples include the provision of services such as home monitoring and regular communication with patients. AI, done diligently, can enable patient’s access to information and ability to manage their chronic conditions. It can also ensure that the general public has access to timely and efficient advice through a smartphone in health emergencies.

To ensure healthy lives and promote well-being for all people at all ages, the Sustainable Development Goal number 3 set up 9 targets to be tackled by 2030. In this article we explored the top 3 from an AI perspective and potential. However, there remains a lot more to explore. To reach the SDG number 3 we need to ensure that the benefits of new technologies, especially information and communication technologies are available to everyone. AI here must capture the attention of both the public and private sectors towards this greater cause in a decentralized environment.

With a Decentralized Alliance, the potential of AI is widely explored to establish innovative projects that improve health in regions most in need. DAIA maximizes the power of AI to collect, analyze, manage and exchange data in all areas of health, from research on molecular genetics to large-scale humanitarian interventions. In an ecosystem of ecosystems, DAIA promotes sustainable financing mechanisms to allow a solid ground for investments. With the evolution of science and the potential of big data being explored for automated diagnostics and more accurate results, health entered the era of the 4th industrial revolution, we simply need to make sure it arrives for us all.

Text by Camila Froede

How can you get involved?

The vision of DAIA is to foster a world where AI technology and associated data are made open with decentralized, democratic control for the benefit of all sentient beings.

The immense potential of AI means that it can either increase the inequalities of our societies or liberate us from various suffering. We believe the best way forward is to come together and work practically toward creating a better future. We see a massive potential for evolution in the established centralized corporations. We believe these tech giants can contribute immensely toward making the vision of DAIA a reality.

DAIA welcomes the participation of those corporations that are sincere about their aim and goal of democratizing AI. The open access networks that have come together to form DAIA, such as SingularityNET, Effect.AI, Neureal and Toda.Network are the enabling layer for such a democratization process.

To learn more about us and inquire for memberships, please contact us at team@daia.foundation.