While AI can be incredibly helpful in interpreting complex genomic data and predicting patient outcomes, using it within healthcare is not without challenges and raises important ethical considerations as well. Here, we'll outline some of these important issues:
AI interpretability challenges
One of the primary criticisms of AI in genomics is its perceived "black box" nature. AI systems often produce outputs without providing clear explanations or justifications. In clinical diagnostics, understanding not only "what" prediction is made but also "why" is essential for informed decision-making.
Data and machine bias
Genomic and health data are often influenced by outside factors, including socioeconomic status, lifestyle, and a person's environment. Failure to scrutinize AI systems for non-causal bias can perpetuate health disparities. For instance, the AI system DeepGestalt displayed significant accuracy discrepancies in identifying Down syndrome among individuals of different ancestries, underlining the importance of diverse training data. Addressing machine bias requires meticulous attention to avoid propagating biases present in training data or inadvertently introducing new biases during algorithmic development.
Phenotypic data collection
The scalable collection of phenotypic data, essential for training AI models, is a major challenge as it is often in the form of qualitative rather than quantitative data, unlike genetic data. The rise of biobank-scale efforts, such as the UK Biobank and the All of Us Research Program, is providing hope for overcoming this particular challenge by supplying the necessary large-scale training datasets.
Data privacy and patient consent
As the use of AI in healthcare becomes more mainstream, issues surrounding the sourcing and privacy of data used for training will continue to be a cause for concern unless those using the data are completely transparent with healthcare providers, patients and the general public. Ethical considerations also extend to issues of patient autonomy, shared decision-making, and the potential for algorithmic errors, making ongoing discussions and guidelines necessary in the future.
Regulatory bodies and guidelines
The approval of a growing number of AI algorithms by regulatory bodies introduces a need for comprehensive guidelines to navigate ethical challenges. Best practices for interpretability and fairness, coupled with ad hoc guidance, would contribute to the responsible development and implementation of AI-driven diagnostic tools. While AI offers significant advantages in interpreting complex genomic data and improving patient outcome predictions, its use in healthcare is accompanied by critical challenges and ethical concerns. Addressing these issues is crucial for maintaining trust and ensuring the responsible and effective use of AI in genomics and healthcare.
To learn more, download our whitepaper, “Data-driven healthcare: How artificial intelligence and machine learning are transforming genomics."
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