Effy Vayena

Last Name

Vayena

First Name

Effy

ORCID

0000-0003-1303-5467

Organisational unit

00085 - Bereich VP Wissenstrsf. & Wirtsch.bez. / Domain VP Knowl. Transfer & Corp. Rel.

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Publications 1 - 10 of 100

In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology
Ferretti, Agata; Vayena, Effy (2022)
Epidemics
The COVID-19 pandemic witnessed a surge in the use of health data to combat the public health threat. As a result, the use of digital technologies for epidemic surveillance showed great potential to collect vast volumes of data, and thereby respond more effectively to the healthcare challenges. However, the deployment of these technologies raised legitimate concerns over risks to individual privacy. While the ethical and governance debate focused primarily on these concerns, other relevant issues remained in the shadows. Leveraging examples from the COVID-19 pandemic, this perspective article aims to investigate these overlooked issues and their ethical implications. Accordingly, we explore the problem of the digital divide, the role played by tech companies in the public health domain and their power dynamics with the government and public research sector, and the re-use of personal data, especially in the absence of adequate public involvement. Even if individual privacy is ensured, failure to properly engage with these other issues will result in digital epidemiology tools that undermine equity, fairness, public trust, just distribution of benefits, autonomy, and minimization of group harm. On the contrary, a better understanding of these issues, a broader ethical and data governance approach, and meaningful public engagement will encourage adoption of these technologies and the use of personal data for public health research, thus increasing their power to tackle epidemics.
What makes clinical machine learning fair? A practical ethics framework
Hoche, Marine; Mineeva, Olga; Rätsch, Gunnar; et al. (2025)
PLOS Digital Health
Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning in clinical workflow have attracted considerable attention including due to complex ethical implications–algorithmic bias being among the most frequently discussed ML models. Here we introduce and discuss a practical ethics framework inductively-generated via normative analysis of the practical challenges in developing an actual clinical ML model (see case study). The framework is usable to identify, measure and address bias in clinical machine learning models, thus improving fairness as to both model performance and health outcomes. We detail a proportionate approach to ML bias by defining the demands of fair ML in light of what is ethically justifiable and, at the same time, technically feasible in light of inevitable trade-offs. Our framework enables ethically robust and transparent decision-making both in the design and the context-dependent aspects of ML bias mitigation, thus improving accountability for both developers and clinical users.
Fostering inclusive co-creation in digital health
Blasimme, Alessandro; Landers, Constantin; Vayena, Effy (2025)
npj Digital Medicine
Research on responsible digital health innovation has typically focused on technical aspects such as the reliability and trustworthiness. More recently, work in responsible digital health innovation has started to recognize that, to address those concerns, stakeholder involvement is key. Aligning technological advancements with stakeholders’ needs requires deliberate and inclusive processes. Such processes must incorporate diverse perspectives, including those of the users of digital health technologies, such as healthcare practitionerss and patients.
Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging
Blasimme, Alessandro; Nittas, Vasileios; Daniore, Paola; et al. (2023)
PLOS Digital Health
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study
Ferretti, Agata; Ienca, Marcello; Rivas Velarde, Rivas; et al. (2021)
Journal of Empirical Research on Human Research Ethics
Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.
Digital health: Implications for the doctor-patient relationship
Amann, Julia; Vayena, Effy; Blasimme, Alessandro (2019)
33rd European Conference on Philosophy of Medicine and Healthcare (ESPMH 2019): Book of Abstracts
On the responsible use of digital data to tackle the COVID-19 pandemic
Ienca, Marcello; Vayena, Effy (2020)
Nature Medicine
How Interactive Visualizations Compare to Ethical Frameworks as Stand-Alone Ethics Learning Tools for Health Researchers and Professionals
Sleigh, Joanna; Ormond, Kelly; Schneider, Manuel; et al. (2023)
AJOB Empirical Bioethics
Background Despite the bourgeoning of digital tools for bioethics research, education, and engagement, little research has empirically investigated the impact of interactive visualizations as a way to translate ethical frameworks and guidelines. To date, most frameworks take the format of text-only documents that outline and offer ethical guidance on specific contexts. This study’s goal was to determine whether an interactive-visual format supports frameworks in transferring ethical knowledge by improving learning, deliberation, and user experience. Methods An experimental comparative study was conducted with a pre-, mid-, and post-test design using the online survey platform Qualtrics. Participants were university based early-stage health researchers who were randomly assigned to either the control condition (text-only document) or the experimental condition (interactive-visual). The primary outcome variables were learning, (measured using a questionnaire), deliberation (using cases studies) and user experience (measured using the SED/UD Scale). Analysis was conducted using descriptive statistics and mixed-effects linear regression. Results Of the 80 participants, 44 (55%) used the text-only document and 36 (45%) used the interactive-visual. Results of the knowledge-test scores showed a statistically significant difference between participants’ post-test scores, indicating that the interactive-visual format better supported understanding, acquisition, and application of the framework’s knowledge. Findings from the case studies showed both formats supported ethical deliberation. Results further indicated the interactive-visual provided an overall better episodic and remembered user experience compared with the text-only document. Conclusions Our findings show that ethical frameworks formatted with interactive and visual qualities provide a more pleasing user experience and are effective formats for ethics learning and deliberation. These findings have implications for practitioners developing and deploying ethical frameworks and guidelines (e.g., in educational or employee-onboarding settings), in that the knowledge generated can lead to more effective dissemination practices of normative guidelines and health data ethics concepts.
Machina non deus: being in charge of AI
Vayena, Effy (2024)
The Lancet
Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis
Scheibner, James; Raisaro, Jean L.; Troncoso-Pastoriza, Juan R.; et al. (2021)
Journal of Medical Internet Research
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.

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Effy Vayena

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