Topic: Ethics of AI Lab

Artificial Intelligence (AI) is one of the most significant and exciting developments of the present. It is also seen as a promising innovation in the medical field. The application of medical AI opens complex questions concerning, for instance, informed consent, biases, trustworthiness, responsibility, and liability, as well as risks associated with data privacy and protection.

Ethics of AI considers the ethical and societal implications of AI use and the potentially far-reaching consequences of AI in society – and in the field of medicine.

The mission of the Ethics of AI Lab in BBMRI is to provide reliable, feasible and practical knowledge on ethical and social/societal matters of AI related to biobanking and biomedical research for the immediate benefit of the community. In bringing together experts, building on state-of-the-art research and learning from best practices, the Lab produces in-depth research and develops practical guidance.

The Ethics of AI Lab uses reliable research to develop practical knowledge in

Ethics of AI in Medical Imaging

Check out this video featuring senior scientist Melanie Goisauf of BBMRI-ERIC as she discusses the EuCanImage project. EuCanImage is a 4-year research project building a European cancer imaging platform that will enhance the potential of Artificial Intelligence in oncology.

WEBINARS

Ethics of AI in Imaging: ​ Ethical and Societal Implications. Speakers: Melanie Goisauf and Mónica Cano Abadía

AI applications in medicine are hoped to improve healthcare and advance health equity. While the technology carries the potential to improve health services, the ethical and societal implications need to be carefully considered to avoid harmful consequences for individuals and groups, especially for the most vulnerable. Cano Abadía and Goisauf problematise approaches such as ‘trustworthy AI’ and ‘explainable AI’ that shape the ethics discourse on AI. The webinar concludes with a reflection on the topics identified that shape the understanding of ‘Ethics of AI’ and the gaps in the discourse. 

PUBLICATIONS

Unlocking the potential of big data and AI in medicine: insights from biobanking. Kaya Akyüz, Mónica Cano Abadía, Melanie Goisauf and Michaela Th Mayrhofer

In this paper, we argue that the ‘data turn’ in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities.

Ethics of AI in Radiology: A Review of Ethical and Societal Implications. Melanie Goisauf and Mónica Cano Abadía

In this paper, Mónica Cano Abadía and Melanie Goisauf contribute to the discourse on the ethics of AI in radiology by reviewing the state-of-the-art literature and discussing the findings from a philosophical and social science perspective. Our analysis was guided by two key research questions: (1) What types of ethical issues are raised by the use of AI in medicine and biomedical research, and (2) how are these issues being tackled in radiology, especially in the case of breast cancer

Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges

Some members of the Ethics of AI Lab (Kaya Akyüz, Mónica Cano Abadía, and Michaela Th. Mayrhofer) explore with other colleagues the ethical implications of the confluence of artificial intelligence with polygenic risk score applications in medicine.

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

This paper argues that the “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly. With the contribution of Michaela Th. Mayrhofer.

PROJECTS

INTERVENE

INTERVENE seeks to advance AI-facilitated analyses of complex medical data to develop genetic risk scores, which summarize the estimated effect of an individual’s genetic makeup on the risk of developing a particular disease.

EuCanImage

EuCanImage will build a highly secure, federated and large-scale cancer imaging platform, with capabilities that will greatly enhance the potential of Artificial Intelligence in oncology. Watch the ELSI contribution to the EUCAN Image project.

BIGPICTURE

The project aims to create a repository of digital copies of around 3 million slides covering a range of disease areas. This repository will then be used to develop artificial intelligence tools that could aid in the analysis of slides.

ACKNOWLEDGEMENTS