Ewan Birney, director of the European Bioinformatics Institute at the European Molecular Biology Laboratory, reflects on the ethical and practical challenges posed by recent advances in artificial intelligence (AI) within the field of biology, drawing parallels with the genomics revolution of the early 2000s.

Birney, who played a key role in developing software to identify human genes during the initial sequencing of the human genome, describes the early genomics era as marked by rapid scientific progress amid uncertain outcomes and public debate over ethical concerns. He sees similar patterns emerging today with AI, where transformative technology is advancing ahead of regulation and sparking broad discussion about its implications.

The AI system AlphaFold’s success in predicting protein structures in 2020 represents a significant milestone, affirming the potential of deep learning approaches to tackle complex biological problems. Birney notes that AI methods, including domain-specific tools such as ChromoBNet and Delphi-2M, are providing new ways to explore areas once thought intractable, such as modeling multiple concurrent diseases in individual patients. Nevertheless, he cautions against excessive hype, emphasizing the limits of current knowledge about biology’s complexity—particularly with conditions involving the human brain—and the substantial effort required to validate potential treatments.

Birney warns that overly optimistic predictions, such as claims that AI will cure all diseases within a few years, risk undermining public trust and damaging the field’s credibility when such expectations go unmet. He stresses the importance of humility and rigorous scientific inquiry, highlighting that AI’s strength lies in augmenting researchers’ ability to generate effective models and insights rather than producing breakthroughs in isolation.

Reflecting on lessons from the genomics era, Birney points to the need for ethical frameworks and regulatory mechanisms to keep pace with scientific advances. Early concerns about genomic data privacy, gene patenting, and the risk of eugenic thinking were addressed through collaborative efforts that included funding ethical research and passing anti-discrimination legislation.

Moving forward, Birney advocates for sustained, collaborative efforts combining human expertise and AI capabilities to deepen understanding of living systems and translate discoveries into improved health outcomes. He calls for grounded scientific goals rather than grandiose claims, underscoring the value of sharing knowledge to build a more informed and healthier future.