Responsible Innovation in Health (RIH) represents an emerging Science, Technology and Innovation (STI) approach that could support not only the Sustainable Development Goal (SDG) “Good health and well-being” but also other SDGs. Since few studies have conceptualized the relationships between RIH and the SDGs, our goal was to inductively develop a framework to identify knowledge gaps and areas for further reflections. Our exploratory study involved: (1) performing a web-based horizon scanning to identify health innovations with responsibility features; and (2) illustrating through empirical examples how RIH addresses the SDGs. A total of 105 innovations were identified: up to 43% were developed by non-profit organizations, universities or volunteers; 46.7% originated from the United States; and 64.5% targeted countries in Africa, Central and South America and South Asia. These innovations addressed health problems such as newborn care (15.5%), reduced mobility and limb amputation (14.5%), infectious diseases (10.9%), pregnancy and delivery care (9.1%) and proper access to care and drugs (7.3%). Several of these innovations were aligned with SDG10-Reduced inequalities (87%), SDG17-Partnerships for the goals (54%), SDG1-No poverty (15%) and SDG4-Quality education (11%). A smaller number of them addressed sustainable economic development goals such as SDG11-Sustainable cities and communities (9%) and SDG9-Industry and innovation (6%), and environmental sustainability goals such as SDG7-Affordable and clean energy (7%) and SDG6-Clean water and sanitation (5%). Three examples show how RIH combines entrepreneurship and innovation in novel ways to address the determinants of health, thereby contributing to SDG5 (Gender), SDG10 (Inequalities), SDG4 (Education) and SDG8 (Decent work), and indirectly supporting SDG7 (Clean energy) and SDG13 (Climate action). Further research should examine how alternative business models, social enterprises and social finance may support the STI approach behind RIH.

Background: Several artificial intelligence (AI) governance frameworks have emerged to help health systems (HS) address AI-related risks. However, most fail to capture the multidimensional and evolving nature of real-world governance. Objective: This systematic review aimed to synthesize existing AI governance frameworks for HS and to propose an integrative AI governance model identifying key components to guide AI-related policy, practice, and research in HS. Methods: A comprehensive search was conducted in 8 academic databases (PubMed, MEDLINE, Embase, ACM Digital Library, Web of Science, Scopus, Social Sciences Abstracts, and PsycINFO), gray literature databases, and international organization web portals in October 2024 (updates: July 2025 and March 2026) and limited to studies published from November 2014 to March 2026 in English, French, Spanish, or Portuguese. Eligible documents included peer-reviewed articles and reports proposing AI governance frameworks for HS. Two reviewers independently selected the frameworks, assessed their quality using the Appraisal of Guidelines for Research and Evaluation for Health Systems, and extracted data. Results were synthesized using thematic analysis. Results: The research retrieved 10,175 records, among which 19 AI governance frameworks were identified. Most were published between 2022 and 2024 (n=13, 68%), half (n=10, 53%) were developed by authors based in North America, and only one-third (n=6, 32%) were derived from primary studies. The frameworks focused on 4 levels of AI governance: international (n=3, 16%), national (n=5, 26%), local (n=3, 16%), and organizational (n=8, 42%). All of them underline the crucial role of multidisciplinary bodies in the structure of AI governance in HS. Six key AI governance processes in HS emerged as critical: (1) need and/or problem identification (n=14, 74%), (2) data governance (n=17, 89%), (3) risk assessment and management (n=17, 89%), (4) validation and/or evaluation (n=18, 95%), (5) maintenance and monitoring (n=16, 84%), and (6) integration (n=9, 47%). Additionally, 4 pivotal relational mechanisms were identified: (1) ethical principles and/or values (n=17, 89%), (2) education and training (n=14, 74%), (3) communication (n=12, 63%), and (4) standards and regulations (n=13, 68%). Conclusions: Our study provides a comprehensive synthesis of existing AI governance frameworks for HS across 4 levels (local, regional, national, and international), underpinned by a quality assessment of the 19 identified frameworks. It differs from existing studies that concentrate on specific dimensions or settings by contributing an integrative AI governance model for HS comprising 2 dimensions and 4 relational mechanisms across the 4 levels, explicitly modeling their interactions. Future research should test and operationalize the proposed model to enhance its practical applicability. Strengthening the methodological rigor of AI governance frameworks will be essential for the responsible integration of AI in HS. As the framework is primarily grounded in Global North and English-language literature, validation in other contexts is warranted.

Organizations and practices that contribute to the resolution of major societal challenges are key to achieving a transition towards sustainable and resilient food systems. Previous research identified contextual elements that affect the emergence of organizations and practices with responsibility characteristics, but how this process unfolds remains poorly articulated. Our study thus focuses on how contextual dimensions may contribute to or constrain the emergence of responsibility in food systems. We applied a multiple case study design and conducted 34 semi-structured interviews with 30 organizations in the province of Québec (Canada) and in the state of São Paulo (Brazil). Our across-case analyses clarify how multiple contextual dimensions both contribute to and constrain the emergence of responsibility. More specifically, our findings show that while contextual dimensions shaped by the dominant food system constrain the emergence of responsibility, the same dimensions also contribute to it when they embed responsibility principles. One key contribution of our study is to show that interpersonal relations are an important mediation mechanism that helps to modify contextual elements, so they can contribute to the emergence of responsibility. This study’s findings can inform research and policy aiming to design institutional environments that promote a transition towards more responsible food systems.

Além de questões relacionadas à eficácia e à segurança dessas soluções, a rapidez com que elas têm sido desenvolvidas e disponibilizadas à população levanta também um conjunto importante de questões éticas, legais, sociais, econômicas e ambientais. Por exemplo, os testes diagnósticos, os medicamentos e as demais tecnologias contribuem para reduzir ou aumentar as desigualdades em saúde? As soluções propostas para monitorar os cidadãos violam seus direitos fundamentais à privacidade e à autonomia? Quais são os impactos dessas soluções para o meio ambiente? E em que medida elas são compatíveis com a sustentabilidade dos sistemas de saúde?