Examples from the COVID-19 pandemic response illustrate how advanced AI tools can enable efficient data use to support areas like forecasting, infectious disease surveillance and monitoring, development of medical interventions, timely diagnosis of infection, disease prognosis, or real-time monitoring of adherence to public health recommendations. New technologies with potentially high impact like air or wastewater real-time monitoring systems have also emerged.

These experiences and advances hold great potential for the future, but additional development and expansion of novel AI-based tools and technologies (including generative AI) is needed, while also further improving and testing existing ones. The use of AI on diverse datasets, as well as on their combinations within and across disciplines, can greatly increase the accuracy of assessments and predictions of medical (pharmaceutical or non-pharmaceutical) interventions in preparedness for, and response to epidemics and pandemics.

Research actions under this topic should include several of the following activities:

• Develop new, or improve existing AI-based tools, methods and technologies, geared towards greater safety, efficiency and impact of medical, societal or logistical countermeasures aiming at the prevention, containment or control of infectious disease epidemics or improved response management of health systems.
• Scout, assemble and prepare appropriate FAIR datasets generated across the EU and Associated Countries (e.g. COVID-19, Influenza, etc.), for the development, training and testing of targeted AI-supported generative assessment and prediction tools, in support of evidence-based policy and decision making for pandemic preparedness and response; in areas like surveillance and monitoring of infectious disease and disease dynamics, facilitating differential diagnosis, triage and risk group predictions, predicting drug response and disease progression, etc.
• Leverage the capacities of the existing and emerging data research infrastructures and the future European Health Data Space (EHDS) and the European Open Science Cloud (EOSC) architectures and research environments, while comprehensively addressing cybersecurity, data privacy, trustworthiness, equity and data quality, interoperability and access modalities.
• Identify and address the current technical, operational, and social limitations related to the (cross-border) access to quality data and to the smooth implementation of AI-driven solutions in the societal and legal context of the EU and Associated Countries.
• Engage with end-users, policymakers, regulatory bodies and authorities, and other stakeholders in the development, improvement, testing and validation of trustworthy and ethical AI-based tools and technologies, to propose options for the validation and uptake of the novel AI tools in real-world settings taking into consideration aspects like training needs, responsible use, users’ trust, energy consumption, etc.

The participation of start-ups, micro, small and medium-sized enterprises (SMEs) is encouraged with the aim of strengthening their scientific and technological foundations, enhancing their innovation potential, and exploring possibilities for commercial exploitation.

Proposals selected for funding under this topic are expected to participate in joint activities as appropriate, which can take the form of project clustering, workshops, joint dissemination activities, etc. Applicants should anticipate budget to cover this collaboration.

This topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts, institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities.