Team Lead: Marcel Salathé (EPFL); Ramesh Krishnamurthy, Senior Advisor, Department of Information, Evidence and Research, World Health Organization (WHO); Sameer Pujari, “Be Healthy, Be Mobile” Project Manager, World Health Organization (WHO)
The Sustainable Development Goals (SDGs) are the 17 global priority goals to be achieved by 2030, agreed upon by the UN member states in 2015. The 17 goals include a more detailed 169 targets and indicators. Achieving the SDGs within the timeframe is an enormous challenge across the scientific and economic community.
Universal Health Coverage (UHC) is one of the SDG targets that aims at ensuring that all people can access quality health services, to safeguard all people from public health risks, and to protect all people from impoverishment due to illness, whether from out-of-pocket payments for health care or loss of income when a household member falls sick.
Artificial Intelligence applications can be a game changer to achieve Universal Health Coverage goals by empowering frontline health workers to enable early stages of diagnostic like Malaria or Cervical Cancer detection or to identify population at risk of developing non-communicable diseases like detecting diabetes/cardiac-risk from the iris, etc. Currently much of this work is done manually, limiting the frequency and scale of coverage. AI can be used also during health and natural disaster emergencies that can significantly increase the efficiency of disaster response and save more lives.
Aim of the track
Potential domains for AI quick-wins for Public Health (workstreams)
- Identify quick-wins areas and types of AI applications that hold high-potential impact on health outcomes and that are feasible and relatively simple to deploy
- Identify bottlenecks and challenges that prevent society from taking full advantage of AI quick-wins in areas such as diagnostic, treatment, prevention or emergency response and how to possibly address those.
- Mobilize community to support a number of proof-of-concepts projects that could demonstrate impact in the short and medium term.
- To make commitment and seek feedback from health community of a global XPrize Challenge to ‘Transform Community Health through AI’ (tbc).
AI for primary care and service delivery
Artificial Intelligence can help make certain types of diagnostics almost ubiquitous, sometimes in combination with off-the-shelf cameras or smartphones. Similar tools can help guide primary-care workers through treatments that they might otherwise not have been able offer.
- Diagnosis and Management of Chronic Cardiovascular, Diabetes, and Respiratory diseases
- Prediction and identification of populations at risk of non-communicable diseases
- Services for high risk pregnancy and early childhood development disorders e.g., detection of hearing impairments or autism.
- Cervical Cancer screening (Cervicography) and skin cancer detection
- Malaria and Tuberculosis diagnosis
- Snake-bite detection
- Pre-diagnostics, self-assessment and remote screening (ear, nose and throat, mental health, dermatology etc.)
- AI-powered health consultations / telemedicine
Outbreaks, Emergency Response and Risk Reduction
Increasingly, the earliest signs of an epidemic are digital, be it through search queries on Google, or through social media, or through patterns of movement, as can be approximated by things such as movements of mobile phones. Artificial Intelligence can help monitor the vast amounts of data that need to be examined in order to pick up the earliest possible signs.
- Analyzing satellite and drones imageries to identify measures for Disaster Risk Reduction or to assess affected areas, level of damage and efforts required
- Mining social media data for early detection of outbreaks and assessing population needs during emergencies
- Better manage dispatcher centers during emergencies by filtering out redundant or less urgent calls, interacting with callers naturally, instantly transcribe and translate languages and analyze the tone of voice for urgency.
- Automated responses to public health related queries for nearest care center for example particularly during epidemics and humanitarian assistance.
Health promotion, prevention, and education
As more information about the health status of individuals becomes digital in a systematic manner, it becomes possible to monitor for conditions at a greater frequency and lower cost. Physiologically, heart rate monitors, etc. can be combined with running times. Additionally, through the use of Natural Language Processing (NLP), chatbots can help people with identifying conditions, as well as with identifying mental conditions.
- Monitor movement and activities to make recommendations to maintain mental and physical health.
- In-home health monitoring and health information access to detect changes in mood or behavior and alert caregivers.
- Personalized health management to mitigate the complexities associated with multiple comorbid conditions and/or treatment interactions.
AI health policy
Personal medical data is of a highly sensitive nature, as such, the use of artificial intelligence in health applications, which is currently still in its infancy, gives rise to a number of ethical questions, such as those relating to data privacy.
- Privacy and data protection
- New WHO Resolution on Digital Health to recognise the potential of digital technology including ehealth, mhealth and emerging tech such as AI - opportunities, challenges, gaps.