Artificial Intelligence (AI) has the potential to transform the field of biobanking by improving the management, analysis, and utilization of biospecimens. But what are some ethical considerations when it comes to using AI in biobanking?

One prominent concern is the potential for bias in algorithms. If the datasets that AI algorithms are trained on are biased or incomplete, the algorithms can perpetuate those biases. In the context of biobanking, this could lead to biased outcomes in areas such as sample selection for research studies or personalized treatment recommendations. It is essential to ensure that the data used to train AI algorithms is diverse, representative, and free from biases so as not to perpetuate existing health disparities or discriminate against certain populations. Next, privacy concerns also arise when AI is employed in biobanking. AI systems often require access to extensive personal health information to make accurate predictions or recommendations. Protecting the privacy and confidentiality of individuals’ health data becomes crucial. Biobanks must have robust security measures in place to safeguard sensitive data from unauthorized access or breaches. Another ethical consideration is the need for transparency and explainability in AI algorithms. The outputs generated by AI systems can have a significant impact on individuals’ lives, and it is important to understand how those decisions are made. Biobanks must strive for transparency in their AI algorithms, ensuring that the decision-making process is clear, accountable, and interpretable.