Overview #

Artificial Intelligence (AI) is one of the most significant developments in the medical field and is transforming biobanking and biomedical research. The potential benefits of AI are just beginning to emerge, with the ability to enhance specimen and data management, accelerate research processes, and facilitate scientific findings. By efficiently analysing datasets, AI can also identify patterns and correlations that may be otherwise overlooked in order to predict disease risk, identify potential biomarkers, and suggest personalised treatment options. These valuable insights can ultimately improve health outcomes for patients. 

As AI and machine learning systems gain more access to data and computational power, their effectiveness and utility in biobanking will also grow. However, legitimate concerns exist. The application of AI in biobanking opens complex questions from legal, ethical and societal perspectives, with potential for far-reaching consequences. It is therefore essential to identify and address arising ELSI issues in order to manage any adverse impacts. This requires a proactive and interdisciplinary approach on the part of the biobanking community to ensure that AI technologies are integrated and managed in a responsible manner that ultimately protects both individual rights and public welfare.  

Key ethical, legal and societal issues #

The main ethical, legal, and societal considerations of AI in biobanking and biomedical research encompass a wide range of challenges that should be addressed to ensure the responsible use of AI technologies. Key issues include:  

Informed consent  #

With AI there may be a shift away from traditional human-centered processes. It is therefore essential that informed consent from participants is obtained as it remains a foundational ethical requirement in biomedical research. Furthermore, with AI’s capabilities to analyse and repurpose data in unforeseen ways, participants should be fully informed about how their data will be used. Transparent communication about data sharing, along with the scope of AI analyses, is essential.  

Trust in AI and trustworthy AI  #

Trustworthiness has become a key requirement in the development and application of AI. This is highlighted, for instance, by the European Commission with their Ethics Guidelines for Trustworthy AI recommended by the High-Level Expert Group on AI. The discourse on trustworthy AI often focuses on defining conditions under which it can be achieved by following guidelines or regulations with scholars aiming to come up with practices to realise such aims. Nonetheless, from a multidisciplinary perspective that accounts for ethical, legal, and societal implications of medical AI, trust needs to be understood as a complex relational concept that involves several trust or/trustee relationships, such as trust in technology, institutions, and persons, e.g. scientists who trust each other, patients who trust scientists and healthcare professionals.  

Accountability  #

AI systems may be involved in decision-making processes that were previously made by individuals. The issue of assigning accountability therefore becomes relevant, particularly given the distribution of responsibility within biobanking development and operations, where multiple stakeholders are involved. It can therefore be very challenging to pinpoint where and with whom responsibility lies when errors are made.  

Data Privacy and Security  #

AI systems in biobanking and biomedical research rely on processing vast amounts of sensitive data, including genetic information, medical histories, and personal identifiers. Protecting this data from breaches and unauthorised access is paramount. Researchers must ensure compliance with data protection regulations such as the GDPR. Robust encryption, secure storage, and strict access controls are essential measures to safeguard data privacy and security. 

Bias and unfairness  #

Data can be flawed and biased from the outset. AI algorithms are therefore susceptible to biases that can arise from the data they are trained on or from the design of the algorithms themselves. In biobanking and biomedical research, biased AI models can lead to unequal treatment, misdiagnosis, or disparities in healthcare outcomes. It is therefore vital to develop and validate AI systems with diverse and representative datasets, continuously monitor for biases, and implement fairness metrics to ensure equitable treatment across different populations. 

Transparency  #

AI systems, particularly those based on deep learning, can often function as “black boxes” with decision-making processes that are not easily interpretable. In the context of biobanking and biomedical research, it is important to strive for transparency and explainability in AI models as far as is possible, with the aim of being able to understand and explain how AI systems arrive at specific conclusions or recommendations. This to ensure accountability and foster trust among stakeholders. 

Ownership and control of data  #

The question of who owns and controls the data in biobanking and biomedical research is complex. Participants, biobanks, researchers, and AI developers all have interests in the data. Clear policies and agreements regarding data ownership, usage rights, and the sharing of benefits arising from AI-driven discoveries are necessary to navigate these complexities and ensure fair practices. 

Regulatory compliance  #

AI applications in biobanking and biomedical research must comply with existing regulations and guidelines governing biomedical research and healthcare. This includes obtaining necessary approvals from ethical review boards, adhering to clinical trial regulations, and ensuring that AI-based tools meet regulatory standards for safety and efficacy. Staying abreast of evolving regulatory landscapes and adapting AI systems accordingly is crucial for compliance. 

Social implications and public perception  #

The use of AI in biobanking and biomedical research has broader social implications, including public perception and acceptance. Engaging with the public, addressing concerns about AI’s role in healthcare, and fostering a dialogue about the benefits and risks associated with AI technologies are essential for building public trust and ensuring the ethical deployment of AI in biomedical research. 

Legal and Ethical international and EU sources

General Data Protection Regulation (GDPR)

The GDPR provides a regulatory framework for lawfully processing personal data. While not explicitly about AI, the GDPR would apply, given that AI systems would be dealing with processing sensitive personal data. 

EU Artificial Intelligence Act (AI Act)

Regulation (EU) 2024/1689. Approved in May 2024, the AI Act regulates the use of AI, adopting a tiered approach based on risk assessment: the greater the potential societal harm from an AI application, the stricter the regulations imposed. The AI Act applies to all sectors and industries, including the life sciences, imposing various obligations at every stage of the AI cycle.    

EU Commission High-Level Expert Group on AI, Ethics guidelines for trustworthy AI, 2019

The Guidelines put forward a set of 7 key requirements that AI systems should meet in order to be deemed trustworthy. A specific assessment list aims to help verify the application of each of the key requirements. 

Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law  

It is the first international legally binding treaty dedicated to AI. It promotes human rights, democratic governance, transparency, accountability, risk assessment, and oversight across the AI lifecycle.  

Council of Europe – AI in Healthcare

Guidance on the application of AI in healthcare, focusing on patient autonomy, equitable access, health data governance, and the doctor–patient relationship.  

UNESCO, Recommendation on the Ethics of Artificial Intelligence

Adopted in 2021 as the first global normative framework on AI ethics. It establishes principles including: human rights, human dignity; fairness and non-discrimination; transparency and explainability; accountability; privacy and data governance; human oversight; environmental sustainability.  

OECD Recommendation of the Council on Artificial Intelligence

It is the first intergovernmental AI standard (2019, updated 2024). It promotes trustworthy, human-centred AI through principles including: inclusive growth; human rights; transparency; robustness and security; accountability.  

World Health Organization (WHO), Ethics and Governance of Artificial Intelligence for Health (2021)

It addresses ethical governance of AI in health, including: protection of autonomy; promotion of human well-being; transparency; responsibility and accountability; inclusiveness and equity; sustainability; responsiveness to changing technologies.  

WHO Guidance on Large Multi-Modal Models in Health (2024)

It provides recommendations for the responsible development and deployment of advanced AI models in healthcare, including governance of large biomedical datasets.  

BBMRI-ERIC resources   #

Webinars  #

• Ethics of AI in Imaging: Ethical and Societal Implications 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. This webinar gives an overview of the types of ethical issues that are raised by AI in biomedical research, offering a comprehensive and systematic review of existing literature. The webinar concludes with a reflection on the topics identified that shape the understanding of ‘Ethics of AI’ and the gaps in the discourse. 
• Artificial Intelligence in South-Eastern Asia – the example of the Indonesian National Artificial Intelligence Strategy on Ethics and Policy This BBMRI-ERIC ELSI Dialogues webinar explores the growing use of AI in healthcare across South-East Asia through the case study of Indonesia’s National Artificial Intelligence Strategy. The session examines the ethical and legal principles underpinning Indonesia’s AI governance framework, highlighting the role of the national philosophy Pancasila in promoting human-centred, trustworthy AI. It also compares the Indonesian approach with the European Union’s AI governance framework, providing valuable insights into different regulatory models and the shared challenges of ensuring responsible AI development in healthcare and biomedical research.  
• Navigating the Future – Legal and IT Challenges, Risks, and Opportunities of AI in Biobanking This BBMRI-ERIC ELSI Dialogues webinar examines the opportunities and challenges associated with integrating artificial intelligence into biobanking. Experts discuss how AI can improve biobank management, data analysis, diagnostics, and precision medicine while addressing the legal and ethical implications arising from the use of AI with sensitive health and genomic data. The webinar focuses on the impact of the GDPR and the EU AI Act, legal responsibilities related to AI-assisted decision-making, algorithmic bias, transparency, explainability, cybersecurity, and the importance of interdisciplinary governance to ensure trustworthy AI in biobanking. Practical examples illustrate how technical innovation and regulatory compliance can be balanced to support responsible biomedical research.  

Publications  #

The following list refers to scientific papers, statements, guidance, etc. drafted by members of BBMRI-ERIC ELSI Team: 

• Akyüz K, Cano Abadía M, Goisauf M, Mayrhofer MT.. Unlocking the potential of big data and AI in medicine: insights from biobanking. Front Med (Lausanne). 2024. https://doi.org/10.3389/fmed.2024.1336588 
• Fritzsche MC, Akyüz K, Cano Abadía M, McLennan S, Marttinen P, Mayrhofer MT, Buyx AM.. Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges. Front Genet. 2023. https://doi.org/10.3389/fgene.2023.1098439 
• Goisauf M, Cano Abadía M.. Ethics of AI in Radiology: A Review of Ethical and Societal Implications. Front Big Data. 2022. https://doi.org/10.3389/fdata.2022.850383 
• Muller H, Mayrhofer MT, Van Veen EB, Holzinger A, The Ten Commandments of Ethical Medical AI. Computer, vol. 54, no. 07, pp. 119-123, July 2021, https://ieeexplore.ieee.org/document/9473208/ 
• Cano Abadía M, Goisauf M. Beyond the algorithm: embedding ethics for trustworthy AI in radiology and oncology. Front Digit Health. 2026. https://doi.org/10.3389/fdgth.2026.1756256 
• Goisauf M, Cano Abadía M, Akyüz K, Bobowicz M, Buyx A, Colussi I, Fritzsche MC, Lekadir K, Marttinen P, Mayrhofer MT, Meszaros J. Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop. J Med Internet Res. 2025. https://doi.org/10.2196/71236 
• Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, Weicken E, Asselbergs FW, Prior F, Collins GS, Kaissis G, Tsakou G, Buvat I, Kalpathy-Cramer J, Mongan J, Schnabel JA, Kushibar K, Riklund K, Marias K, Amugongo LM, Fromont LA, Maier-Hein L, Cerdá-Alberich L, Martí-Bonmatí L, Cardoso MJ, Bobowicz M, Shabani M, Tsiknakis M, Zuluaga MA, Fritzsche MC, Camacho M, Linguraru MG, Wenzel M, De Bruijne M, Tolsgaard MG, Goisauf M, Cano Abadía M, Papanikolaou N, Lazrak N, Pujol O, Osuala R, Napel S, Colantonio S, Joshi S, Klein S, Aussó S, Rogers WA, Salahuddin Z, Starmans MPA; FUTURE-AI Consortium.. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025. https://doi.org/10.1136/bmj-2024-081554 
• Mayrhofer, M.T., Slokenberga, S., & Mežinska, S. (Eds.). (2025). Artificial Intelligence in Biobanking: Ethical, Legal and Societal Challenges (1st ed.). Routledge. https://doi.org/10.4324/9781032634487 
• Colussi I. A possible risk governance approach for AI in health research and biobanking. In: Mayrhofer MT, Slokenberga S, Mežinska S, editors. Artificial Intelligence in Biobanking: Ethical, Legal and Societal Challenges. 1st ed. Routledge; 2025. doi:10.4324/9781032634487 
• Akyüz K, Cano Abadía M, Goisauf M. Artificial intelligence, sex, and gender: Hypes, hopes, and potential risks in biobanking. In: Mayrhofer MT, Slokenberga S, Mežinska S, editors. Artificial Intelligence in Biobanking: Ethical, Legal and Societal Challenges. 1st ed. Routledge; 2025. doi:10.4324/9781032634487  
• Mayrhofer MT. Powerful AI: between accountability and potentiality. In: Mayrhofer MT, Slokenberga S, Mežinska S, editors. Artificial Intelligence in Biobanking: Ethical, Legal and Societal Challenges. 1st ed. London: Routledge; 2025. doi:10.4324/9781032634487 
• Mayrhofer MT, Slokenberga S, Mežinska S. Learnings from unboxing the black box. In: Mayrhofer MT, Slokenberga S, Mežinska S, editors. Artificial Intelligence in Biobanking: Ethical, Legal and Societal Challenges. 1st ed. London: Routledge; 2025. doi:10.4324/9781032634487 
• Bak M, Madai VI, Fritzsche MC, Mayrhofer MT, McLennan S. You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly. Front Genet. 2022 Jun 13;13:929453. doi: 10.3389/fgene.2022.929453. PMID: 35769991; PMCID: PMC9234328. 

External resourses #

• AI Ethics and Governance in Practice  by the Alan Turing Institute. Developed by the Alan Turing Institute’s Public Policy Programme, this 8-module programme provides tools and guidance to ensure responsible use of AI technologies.   
• Principles for Augmented Intelligence Development, Deployment, and Use  by the American Medical Association (AMA). These principles expand on existing AI policy, and emphasise the need for ethical, equitable, responsible, and transparent AI development and governance at the national level. 
• Ethics and Governance of Artificial Intelligence for Health: WHO Guidance by the World Health Organization. This report from the WHO outlines ethical challenges and risks of using AI in healthcare, presents six consensus principles, and offers recommendations for effective governance of AI technologies whilst ensuring accountability of stakeholders involved.  
• Ethics Guidelines for Trustworthy AI  by the High-Level Expert Group on Artificial Intelligence set up by the European Commission. These Guidelines present a framework aimed to foster Trustworthy AI by giving guidance that promotes and ensures ethical and robust AI. In addition to providing a set of ethical principles, the Guidelines also provides information from an operational perspective.  

Relevant literature #

• Battineni G, Hossain MA, Chintalapudi N, Amenta F. A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review. Diagnostics (Basel). 2022 May 9;12(5):1179. doi: 10.3390/diagnostics12051179. PMID: 35626333; PMCID: PMC9140088. 
• Kinkorová J. Artificial intelligence in biobanking: current situation and future perspectives. Cas Lek Cesk. 2025;164(7-8):328-332. English. PMID: 41615792. 
• Mayrhofer MT (2025) How the world of biobanking is changing with artificial intelligence. Front. Digit. Health 7:1626833. doi: 10.3389/fdgth.2025.1626833 
• Venturini P., Lobato Faria P., Cordeiro J.V., AI and omics technologies in biobanking: Applications and challenges for public health, Public Health, Volume 243, 2025, 105726, ISSN 0033-3506, https://doi.org/10.1016/j.puhe.2025.105726. 

Acknowledgements #

The entry was co-funded by EuCanImage, INTERVENE, and BIG PICTURE, projects that have received funding from the European Union’s Horizon 2020 research and innovation programme under, respectively, grant agreement No 952103, No 101016775, No 945358. BIG PICTURE is a European public-private partnership funded by the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. 

Contacts #

• Name: Dr. Melanie Goisauf, Senior Scientist – Ethics of AI Lab Lead , melanie.goisauf@bbmri-eric.eu