This workshop aims to capacitate participants with a critical understanding of how AI and digital transformation technologies can transform public health practices and decision-making. Applications of AI and data-driven methods within public health frameworks are introduced and critically discussed. Participants will gain critical insights into the WHO digital health transformation strategy in public health, in addition to various digital tools, such as interoperability between Electronic Health Records (EHRs) nationally and globally, mobile health applications, predictive analytics, and machine learning for disease management, disease surveillance, optimising health communication through AI-enabled effective strategies, and efficient resource allocation using data analytics. Legal, Social, Ethical, and Professional (LSEP) considerations are critically reflected upon for responsible AI and digitally enabled public health. Through selected case studies and group-based interactive activities, public health stakeholders will collaboratively develop strategic plans for integrating digital and AI solutions into public health frameworks, enhancing their knowledge and strategies to drive impactful solutions in their respective areas of practice in public health.
This workshop aims to guide participants in developing frameworks for digitally- and AI-driven capacity building within the public health workforce. Geared towards enhancing public health responses, this workshop provides an interactive and group-based platform, using selected case studies, where public health stakeholders acquire essential skills and competencies, deepen knowledge of the WHO digital transformation strategy and associated processes, and practice necessary tooling for strengthening resilience in the workforce. Participants will be involved in hands-on exercises for developing digital and AI capacity-building frameworks, and understanding the implications of current policy and infrastructure, assessing current competencies, establishing effective training methodologies, and maximising resource allocation maintain a culture of continuous improvement, meeting not only contemporary but also forthcoming public health challenges for readiness and adaptability in a rapidly changing public health landscape.
This workshop is aimed at capacitating public health stakeholders with the foundational knowledge and practical skills necessary to utilise descriptive data analytics to gain critical and pivotal data-driven insights into improving decision-making in public health. Participants will be immersed in hands-on case studies to gain a critical understanding of how to analyse, interpret, and communicate descriptive statistics for effective public health practice. Participants will learn to convert raw data into actionable insights for deriving essential health outcomes and informed policy decisions. The workshop is also focused on key statistical concepts and data visualisation techniques, with real-world applications in public health. By the end of the workshop, participants will have learned effective management of public health data sources and how to apply descriptive analytics using software tooling to assess health trends, identify patterns, and support evidence-based decisions relevant to their fields of interest in public health. Ethical considerations surrounding data analytics, data integrity, and privacy are critically discussed in an interactive and group-based collaborative platform.
This workshop aims to capacitate public health stakeholders with advanced knowledge and practical skills essential for the effective application of predictive data analytics in public health, focusing on developing predictive models to forecast health outcomes, identify at-risk populations, and optimise resource allocation. Using selected case studies in collaborative group-based environment, participants will engage with state-of-the-art AI predictive methods, techniques, and software tooling at improving public health planning, disease prevention, and intervention strategies. During the delivery of this workshop, participants will acquire theoretical knowledge and practical skills predictive analytics competencies empowering them to make informed decisions for enhancing health outcomes and improve the overall effectiveness of public health initiatives. Ethical considerations and requirements surrounding public health predictive data analytics, including data privacy and informed consent, will be thoroughly examined in interactive group-based settings.
This workshop aims to introduce public health stakeholders to the applications of generative artificial intelligence (AI) and Large Language Models (LLMs) within the public health domain, for enhancing health outcomes and improving public health programmes effectiveness. Throughout this interactive group-based workshop, participants will explore how generative AI can support key public health initiatives beyond descriptive and predictive modelling, to include health communication, policy simulation, and community engagement. Data ethics and responsible AI are critically discussed, exploring the ethical implications of using generative AI in public health, including biases inherent in AI algorithms, data privacy, and informed consent issues. Participants will be engaged in practical cases studies using generative AI tools and LLMs, for developing AI-driven communication strategies using chatbots for health information dissemination and public health engagement. Discuss how LLMs can simulate policy impacts and assist in analysing the potential outcomes of public health interventions. Discussions on how LLMs can be utilised to simulate policy impacts for analysing potential outcomes of public health interventions.
This workshop is aimed at capacitating public health stakeholders, data scientists, and researchers with the knowledge and skills necessary to utilise real-time data streams for epidemic intelligence and response. The workshop addresses the utilisation of AI technologies on data generated from various sources—such as public health surveillance systems, social media, and IoT devices—for rapid epidemic detection and effective response strategies. Participants will explore ethical issues, frameworks, and methods for developing AI-enabled platforms utilising these data streams to protect and improve public health outcomes. The workshop deepens the understanding of the role of real-time AI-enabled data stream intelligence in epidemic surveillance; thus, participants will learn strategies to inform public health actions, improve epidemic preparedness, sustain resilience, and enhance community health in a rapidly changing, data-driven world. Accordingly, the workshop contributes to cultivating a workforce skilled at handling the challenges of rapidly changing public health issues.
The workshop aims to critically explore the transformative role of Artificial Intelligence (AI) and generative AI technologies in shaping global health governance, enhancing health outcomes, and refining public health policy responses. Targeted at public health stakeholders, participants will be engaged in an interactive and collaborative group-based environment, using selected case studies (including the efficacy of AI in managing the COVID-19 pandemic) to learn how generative AI can facilitate data-driven decision-making, improve epidemic and pandemic response strategies, and enrich and empower international collaboration in tackling pressing global, regional, and national health issues. Through predictive modelling applications, health authority professionals will learn to forecast disease outbreaks effectively and identify at-risk populations. The ethical implications of deploying AI will be discussed and critically reflected upon, with emphasis on issues surrounding data privacy, algorithmic fairness, and addressing the public health divide. This workshop provides a unique opportunity to explore and inspire critical thinking about ethical practices in data-driven and AI-enabled global public health.
This workshop is aimed at empowering public health stakeholders to plan and implement AI-based automated evidence synthesis to inform reviews of current and emerging public health policies, while ensuring equitable health outcomes across diverse populations. Through selected critical case studies within an interdisciplinary collaborative framework in interactive, group-based settings, participants will engage in learning AI methods, frameworks, and processes to automate literature searches, data extraction, and meta-analyses, thereby phasing out traditional manual processes. Notable case studies from the COVID-19 pandemic will be critically examined, focusing on the timely application of AI-assisted evidence synthesis and its impact on expediting policy recommendations in pandemic situations. Furthermore, the workshop addresses the methodological and ethical challenges associated with automated AI integration, with particular reference to transparency, rigor in algorithmic applications, bias, data integrity, and the importance of maintaining human oversight in automated processes. Through this initiative, participants will not only enhance their skills but also contribute to a more equitable, robust, and responsive global public health research landscape.
This workshop provides an advanced platform for public health stakeholders to critically engage with the evaluation of digital health initiatives in alignment with the World Health Organization's (WHO’s) digital health transformation guidelines and the United Nations Sustainable Development Goals (UNSDGs). The workshop addresses effective evaluation frameworks amidst the rapid advancement of digital health technologies. Participants will explore a multitude of practical case studies in interactive, collaborative, and group-based settings, focusing on the adoption of AI in disease surveillance and the effectiveness of telehealth programmes in rural settings. Essential metrics and Key Performance Indicators (KPIs) will be introduced to empower participating public health stakeholders to establish measurable outcomes in their fields of interest that accurately reflect health improvements and resource efficiencies, which are crucial for adhering to UNSDG subgoals related to health equity and accessibility. Ethical requirements regarding data privacy and informed consent will be addressed, ensuring responsible AI-enabled and data-driven practices in evaluating digital health technologies, aimed at advancing systemic improvements in global health outcomes and contributing to the sustainable health transformation envisioned by the UNSDGs.