From wildfire triage to refugee immunization apps, AI technologies are rapidly reshaping how the world delivers care during crises, offering speed, scale, and smarter decisions when lives are on the line.
Study: AI in humanitarian healthcare: a game changer for crisis response. Image credit: stockvideo24/Shutterstock.com
Artificial intelligence (AI) is a revolutionary blend of computing technologies that could transform humanitarian healthcare by developing novel solutions to crises. A recent review in Frontiers in Artificial Intelligence examines the scope of AI-assisted healthcare crisis responses, showing how it could make them more resilient and efficient.
Introduction
AI can partner with and power various technologies that could provide more efficient and high-quality healthcare responses in emergency situations, improving decision-making and resource allocation. It can predict natural disasters, ensuring and improving real-time communication. Its use could ensure that populations at risk receive adequate and timely aid. The review analyzed peer-reviewed literature and real-world case studies from 2001 to early 2025, focusing on disease surveillance, disaster response, mental health, and ethical concerns.
The scope of AI in humanitarian healthcare
Some ways in which AI empowers humanitarian healthcare are illustrated below.
Improving precision and speed in disaster responses
AI allows first responders and planners to respond more quickly and accurately to disasters such as floods, earthquakes, hurricanes, and wildfires.
For instance, during the Los Angeles wildfires, AI-powered drones imaged the fire in real time and analyzed the data to predict how the flames would spread. This enabled the identification of the best evacuation routes and helped send medical teams to the right spots. In addition, the AI-driven triage of patients with burns or respiratory symptoms ensured that resources were used to help those in greatest need.
AI is being used in refugee camps on a pilot basis to analyze local conditions and predict disease outbreaks at an early stage. It also drives telemedicine applications in remote spots or when local medical resources are overwhelmed. The Children Immunization App (CIMA), implemented in Jordan’s Zaatari refugee camps, supports vaccination surveillance in refugee populations and has increased the rate of follow-up vaccinations in such groups.
According to a non-randomized controlled trial by El-Halabi et al (2022), the intervention group using CIMA had a 26% follow-up return rate within one week, compared to 22% in the control group, with a 19% relative risk reduction in loss to follow-up.
Infectious disease surveillance
AI can track and predict infectious outbreaks, including malaria, tuberculosis, and dengue, integrating variables related to the climate, human population movement, and socioeconomic factors. This could improve resource allocation and drive preventive policies.
For instance, IBM’s Watson Health is used by ZzappMalaria’s AI-powered app to increase the effectiveness and coverage of malaria elimination strategies while reducing the campaign's time required. The app analyzes satellite and environmental data to identify mosquito breeding sites, enabling more targeted and effective larvicide operations.
Mental health support
AI provides counseling to help victims of natural disasters or displacement who are suffering from stress, depression, and anxiety. Mental health resources are typically unavailable in these settings, enhancing the value of AI tools.
Chatbots like Wysa and Woebot use AI to deliver psychological support through cognitive behavioral therapy (CBT) and mindfulness strategies. They offer immediate, multilingual assistance and help reduce the burden on overwhelmed mental health systems.
AI tools are also used to monitor real-time sentiment on social media and emergency channels, helping identify mental health crises and enabling targeted intervention. In addition, AI-powered simulations are being used to train mental health professionals in crisis counseling scenarios.
Robotic-assisted care
Search-and-rescue robotic devices can help detect and rescue people trapped in debris after earthquakes and other natural disasters. Other robotic devices can help monitor or treat people with infectious conditions such as coronavirus disease 2019 (COVID-19), enhancing care accessibility.
Robotic prostheses and other rehabilitative systems also help survivors regain or improve mobility and recover faster. Such platforms will likely be used even more as digital health technologies develop. For example, the MERON app, developed and piloted by UNICEF, uses AI to screen for malnutrition in children through image analysis, improving diagnosis and care.
Crisis communication
Differences in language often hinder humanitarian aid. AI-driven natural language processing (NLP) tools translate public health guidelines, medical instructions, and emergency alerts into other languages, reaching widely diverse populations and improving compliance with the recommendations.
The review notes that tools such as Google BERT and OpenAI’s large language models are being deployed to perform real-time translations. However, challenges like mistranslation risks and language model training data bias remain.
Supply chain logistics
AI can optimize aid distribution, including medical supplies, food, and equipment. Predicting demand streamlines the process and reduces waste, helping people in need to access the available resources as quickly as possible. AI-powered drones also deliver emergency medical aid and vaccines in remote or cut-off areas.
Organizations like the World Food Programme and Médecins Sans Frontières have begun using AI-powered analytics and drone systems to improve delivery efficiency and minimize waste in emergency supply chains.
Healthcare security
AI can be used along with blockchain to provide disaster victims with secure identities. This lets them access healthcare and medical data anywhere without losing their medical history. This helps both the treating professionals and the patients. Pilot studies are ongoing in refugee camps. According to the authors, these tamper-proof digital identities enable continuity of care and reduce the administration burden associated with displacement.
Climate mitigation
AI can help forecast extreme weather events using environmental and meteorological data. Thus, it can provide early warning of environmental disasters, including drought, floods, heat waves, hurricanes, and locust swarms. This would enable preventive measures, minimizing the loss of life and property damage.
For instance, Google’s Flood Forecasting Initiative is working in Bangladesh and India, among other places, providing local flood predictions based on real-time rainfall and river level data combined with the local terrain. In Africa, AI models are also being used to predict and track locust swarm patterns, helping improve food security by supporting preventative agriculture responses.
Ethical issues
Using AI in humanitarian healthcare is challenging unless urgent limitations are faced and resolved. These include biased algorithms that could skew medical decisions and wrongly allocate resources to the wrong people. This could deprive people in need who lack political or social power or representation of help. AI training requires deliberate use of diverse datasets with constant monitoring and transparency to avoid this.
Data privacy and security are other crucial issues. Another question is accountability: Who is responsible for AI–driven patient care decisions: the doctors, the algorithm developers, or the humanitarian aid providers?
Infrastructural issues could widen healthcare inequities, as AI-powered solutions demand internet connectivity and digital infrastructure, which some populations lack. Regulatory oversight is essential to ensure AI's neutral, impartial, and humane use. This requires collaboration between government and non-governmental stakeholders.
As the paper emphasizes, achieving ethical AI deployment requires inclusive governance, human oversight of critical decisions, and deliberate efforts to avoid digital exclusion in low-resource settings.
Conclusion
While AI-powered interventions promise increased responsiveness, data-driven decision-making, and inclusivity, ethical regulation is vital during the' design and implementation phases of such solutions during humanitarian healthcare crises.
With coordination between governments, NGOs, academic researchers, and technology companies, AI has the potential to become a core enabler of a more responsive, equitable, and human-centered humanitarian healthcare system.
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