Artificial Intelligence in Life Sciences: Considerations for Policy-Making and Social Impact

Abstract

Artificial intelligence (AI) is rapidly transforming life sciences by improving biological discovery, clinical diagnosis, drug development, epidemiological surveillance, precision medicine, and health-system efficiency. Its ability to process large and complex datasets has created new opportunities for faster and more accurate decisionmaking across laboratory research, public health, and patient care. However, the adoption of AI in life sciences raises important policy and social questions related to data governance, algorithmic bias, explainability, accountability, workforce adaptation, privacy, equity, and public trust. This review paper synthesises current applications of AI in life sciences and examines key governance challenges that must be addressed for socially responsible implementation. It argues that policy design should move beyond innovation promotion alone and include safeguards for fairness, transparency, safety, and inclusion. The paper proposes a policy framework centred on risk-based regulation, human oversight, quality standards for data and models, participatory governance, and ethical impact assessment. It also discusses the implications for low- and middle-income settings, where digital divides may amplify inequity if AI systems are not context-sensitive. A balanced approach to supporting innovation while protecting rights and social welfare is essential for ensuring that AI in life sciences contributes to public good rather than deepening existing disparities.

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