AI in drug development: Systemic challenges and opportunities in biotech innovation
Original framing: “STAT+: Insilico Medicine CEO on how best to use AI in drug development” — STAT News
Scientifically, AI has shown promise in accelerating drug discovery by analyzing vast datasets and identifying novel molecular structures. However, the lack of transparency in AI algorithms and biases in training data raise concerns about reproducibility and fairness. Rigorous peer review and open science practices are needed to ensure scientific integrity in AI-driven biotech.
AI in drug development is not a neutral technological advancement but a reflection of broader power structures in global health.
By integrating indigenous knowledge, addressing historical injustices, and centering marginalized voices, AI can become a tool for equity rather than exclusion. Cross-cultural collaboration and open-source innovation are essential to ensure that AI serves the public good. Future models must account for the environmental and ethical implications of AI infrastructure, while governance frameworks should promote transparency and accountability. Only through a systemic, inclusive approach can AI contribute to a more just and sustainable pharmaceutical landscape.