Big data science involving machine learning, deep Learning and artificial intelligence has revolutionised research in various disciplines, including medicine. These advancements have significantly contributed to predicting, preventing, and treating diseases more effectively by leveraging data-driven approaches. Big data science enable large-scale and multi-dimensional aggregation and analysis of heterogeneous health data sources. However, along with these advancements, ethical challenges arise. One of the primary concerns is the risk of compromising privacy and personal autonomy. With the aggregation and analysis of large amounts of health data, there is a potential for unauthorized access or unintended identification of individuals. Safeguarding patient confidentiality becomes paramount in ensuring the ethical viability of health-related big data studies. Deidentification techniques play a crucial role in protecting patient privacy by removing or anonymizing identifiable information. Transparency, trust, and fairness are also important ethical considerations in the use of big data. The public's demand for transparency in data usage and the assurance of fair and unbiased analysis should be addressed to maintain public trust in these technologies.
Data ownership is a complex matter in big data science, particularly when considering health-related data. Determining who owns the data and how it can be used ethically is a topic that requires careful consideration. Group-level ethical harms should also be taken into account. While big data analysis can provide valuable insights and benefits at the population level, there is a risk of inadvertently causing harm or perpetuating biases against certain groups. Attention should be given to ensuring that the use of big data does not disproportionately impact vulnerable populations or reinforce existing inequalities. Another ethical consideration is the distinction between academic and commercial uses of big data. Academic research often prioritizes knowledge generation and societal benefits, while commercial uses focus on profit and market-driven goals. Balancing the interests and potential conflicts between these two domains is crucial to maintain ethical integrity in the use of big data. In conclusions, it is essential to address the ethical challenges associated with health-related big data science. Safeguarding patient confidentiality, ensuring privacy, promoting transparency and fairness, considering group-level ethical harms, and navigating the distinction between academic and commercial uses are all critical aspects of ensuring the ethical viability of health-related big data studies.
