POLICY BRIEF

Integrating AI into Health Systems to Achieve
the Health-Related SDGs and Climate Goals

Artificial Intelligence (AI) has emerged as a cornerstone of modern health system transformation, offering innovative solutions to long-standing challenges. By enabling predictive analytics, real-time monitoring, and operational efficiency, AI enhances the delivery of healthcare services and optimizes workforce capacity. Its potential extends to addressing the social determinants of health, advancing the health-related Sustainable Development Goals (SDGs), and mitigating the health impacts of climate change. This Fast-Track Health policy brief highlights best practices through five case studies, discusses challenges and opportunities, and provides actionable recommendations for municipal, national, and regional health authorities.

HIV Care Optimization in Botswana: In Botswana, where the HIV prevalence rate remains among the highest globally, integrating AI into care delivery has demonstrated measurable improvements. A mobile application powered by AI was introduced to support adherence to antiretroviral therapy (ART). This app delivered tailored medication reminders, enabled virtual consultations, and allowed symptom tracking for patients living with HIV. By analyzing usage patterns and individual patient data, the AI system adapted its interactions to better meet the needs of users, fostering engagement and adherence. The initiative led to a 25% increase in adherence rates and enhanced communication between patients and healthcare providers. However, the program highlighted the importance of culturally adapting AI tools to local languages and ensuring connectivity in underserved regions. These adaptations were critical for the success of the initiative and underscore the importance of inclusive AI design (Botswana Ministry of Health, 2022).

AI for Climate-Related Health Alerts in India: India faces significant health challenges due to the increasing frequency and intensity of heatwaves, exacerbated by climate change. AI played a pivotal role in addressing this issue through predictive models developed to analyze meteorological data and identify areas at high risk for heat-related illnesses. These models informed public health agencies to issue timely alerts, allocate medical resources, and deploy cooling measures to vulnerable communities. This approach reduced heat-related hospital admissions and improved emergency response times. The success of this initiative emphasized the importance of intersectoral collaboration between meteorological departments and public health agencies to ensure actionable AI-driven insights. Additionally, it highlighted the need for capacity building among health workers to interpret and respond to AI outputs effectively (Singh et al., 2023).

Cancer Diagnosis in Brazil: Brazil’s health system has struggled to ensure timely access to cancer screening and diagnosis, particularly in rural and underserved regions. AI-enhanced imaging tools addressed this gap by expediting the analysis of mammograms, identifying early signs of breast cancer with greater accuracy and speed. These tools reduced diagnosis times by 40% and increased early detection rates, which are critical for improving survival outcomes. A significant lesson learned was the need for extensive training of healthcare providers to interpret AI-generated insights and to foster trust in the technology. The initiative also highlighted the role of public-private partnerships in financing and scaling AI interventions in resource-limited settings (da Silva et al., 2021)

Maternal Health Monitoring in Kenya: In Kenya, AI has been deployed to tackle maternal mortality, a persistent challenge in many low- and middle-income countries. Wearable devices powered by AI were introduced to monitor vital signs of pregnant individuals in real time, flagging risks such as pre-eclampsia or abnormal fetal heart rates. The AI system automatically alerted healthcare workers to potential complications, enabling timely interventions. This technology contributed to a 30% reduction in preventable maternal deaths and demonstrated the value of integrating AI tools with existing healthcare delivery systems. However, the initiative underscored the need for strong partnerships with local communities and governments to ensure equitable access to the technology and its long-term sustainability (Mwangi et al., 2022).

AI-Driven Opioid Crisis Management in the United States: In response to the opioid epidemic, certain U.S. states have implemented AI tools to predict overdose risks and optimize resource allocation. For instance, machine learning models analyze electronic health records, prescription data, and social determinants of health to identify individuals at high risk of opioid misuse. This approach enables targeted interventions, such as early referrals to substance use treatment programs and the strategic deployment of naloxone in high-risk areas. Studies have shown that such AI-driven strategies can lead to reductions in opioid-related deaths and improved access to treatment services (BMJ Open, 2024).

Integrating AI into health systems faces several challenges that impede its full potential. Limited digital infrastructure, particularly in low-resource settings, restricts the implementation and scalability of AI tools, creating disparities in access to innovative health solutions. Ethical concerns, including data privacy, algorithmic bias, and transparency, often deter adoption and erode public trust. Healthcare workers may resist AI integration due to a lack of training or concerns over job displacement, highlighting the need for comprehensive capacity-building initiatives. Furthermore, the absence of robust regulatory frameworks complicates the certification and deployment of AI technologies, delaying their widespread use. Financial constraints, particularly in low- and middle-countries, add another layer of difficulty, as the high costs of implementing and maintaining AI solutions often exceed available budgets.

Despite these challenges, AI offers transformative opportunities to improve health outcomes and system efficiency. AI-powered tools can enhance diagnostic accuracy, predict disease outbreaks, and personalize treatment plans, addressing gaps in healthcare delivery. By automating routine tasks, AI allows healthcare workers to focus on patient-centered care, improving workforce efficiency. Community-focused AI solutions, such as mobile apps and telemedicine platforms, extend healthcare access to underserved populations, fostering equity. Integrating AI into climate-resilient health systems amplifies its impact, enabling real-time monitoring and rapid responses to climate-sensitive health issues. Collaborative efforts between governments, private sector actors, and academic institutions can catalyze innovation, while public-private partnerships and outcome-based financing models can overcome resource constraints, ensuring the sustainable deployment of AI in health systems globally.