AI-Assisted Drug Discovery and Diagnostics

Artificial intelligence is rapidly reshaping biomedical research, offering transformative tools for drug discovery and diagnostics. From high-throughput molecular screening to personalised treatment pathways, AI systems promise efficiency, precision, and innovation. Yet these advances also surface complex ethical, social, and scientific questions that demand rigorous reflection.

This framework invites academic researchers, clinicians, and interdisciplinary collaborators to examine the integration of AI in biomedical contexts through a values-driven lens. It explores how algorithmic design, dataset composition, and model interpretability influence outcomes, and how these factors intersect with equity, access, and trust.

Structured across six iterative steps, the guide scaffolds ethical inquiry alongside technical insight. It highlights historical shifts in AI capability, emerging regulatory landscapes, and the lived realities of patients and practitioners. Through reflection prompts, stakeholder engagement strategies, and values-in-practice exercises, it supports researchers in cultivating responsible, human-centred innovation.

In an era where algorithms may guide diagnoses and shape drug pipelines, this resource affirms that ethical oversight is not peripheral; it is foundational. It calls for inclusive, transparent, and accountable science that honours both complexity and care.

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