A Literature Review on Ethics for AI in Biomedical Research and Biobanking

Abstract

Background: Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research. Methods: We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization. Results: More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current ‘hot’ topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results. Conclusions: The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.

Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education

“…Hence, AI applications may perpetuate existing inequities in the medical field by providing unequal access to care or making biased treatment decisions. Further concerns include the insufficient protection of data privacy and confidentiality, as well as the lack of informed consent when retrospectively using patient data for training [ 18 , 19 ]. Finally, there is a risk that AI systems will compromise patient autonomy and dignity by making treatment decisions without appropriate oversight [ 15 , 20 ].…”

Unlocking the potential of big data and AI in medicine: insights from biobanking

“…AI is central to the idea of “biobanks for the future” ( 61 ) though challenges in implementation of AI in biobanking range from difficulties aligning standards not only across data in the long run, but also samples, workflows, ethics management, legal and governance-related aspects, from transparency to informed consent ( 28 ) as well as justice, both epistemically and ethically ( 14 ). There are efforts such as workshops or collections of best practices to increase the “readiness” of these infrastructures for AI ( 60 ) with calls, checklists, tools and frameworks for ethical use of AI in medicine/biobanking ( 47 , 62 ). New and alternative forms of governance are needed for a new form of biobanking that revolves around big data considering the increasing widening of the scope of data from social media to devices capturing bodily function, resulting in streams of data over time and analytical capacity over space ( 63 ).…”

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

“…Ethical considerations are paramount in AI implementation, as biases can be incorporated into models through improper data collection or usage methods [ 40 ]. These biases can be many but are fundamentally based on the cohort makeup and data used in the specific AI systems.…”