machine learning; medical devices; biomedical research; algorithms ; ethics; governance; artificial intelligence; innovation; bioethics; public health; digital health
Blasimme Alessandro, Ferretti Agata, Vayena Effy (2021), Digital Contact Tracing Against COVID-19 in Europe: Current Features and Ongoing Developments, in Frontiers in Digital Health
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Amann Julia, Blasimme Alessandro, Vayena Effy, Frey Dietmar, Madai Vince I. (2020), Explainability for artificial intelligence in healthcare: a multidisciplinary perspective, in BMC Medical Informatics and Decision Making
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Blasimme Alessandro, Vayena Effy (2020), What's next for COVID-19 apps? Governance and oversight, in Science
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Digital health is a commonly used umbrella term encompassing the whole spectrum of ways in which digitalization, data collection and data analytics are pushing medicine in as yet uncharted territories. The digital transformation of medicine, far from being a form of spontaneous technological innovation, is a complex socio-technical phenomenon that must be accompanied by equally innovative governance approaches. In order to ensure the ethically and socially robust development of digital health, policy makers will need to take evidence-based decisions and set up governance mechanisms inspired by ad hoc models. Good governance in this burgeoning field of technological innovations must align the needs of patients, developers, healthcare professionals and payers and must accommodate deeply felt societal expectations for responsible digital innovation. Drawing on the applicants’ expertise in digital health, health ethics and policy, public health and big data our project will produce a national policy roadmap for digital health innovation in Switzerland. To this aim, this project will rely on a recently developed adaptive governance framework, that offers a systemic oversight approach based on six principles: adaptivity, flexibility, inclusiveness, reflexivity, responsiveness and monitoring (AFIRRM). The AFIRRM framework provides the foundations for ethically sensitive and socially responsible governance of big data applications in the biomedical domain. Evidence collection will be based on established qualitative research methods in social sciences, such as semi-structured interviews with key stakeholders and focus groups.The operationalization of the AFIRRM model will integrate innovative methods in science policy, namely, scenario planning, foresight workshops with experts across a broad array of relevant disciplines, and innovation policy roadmapping.