Artificial intelligence in biomedical research: advancing non-animal methodologies - Sandhu, Jagnoor Singh; Parida, Amrita; Hegde, Shreya; V., Manju (Frontiers in Animal Science, 2025)
Animal models for several decades have offered a foundation for discovering human physiology along with promoting therapeutic innovation. However, limitations like translational gaps, controversy-ridden ethics, and regulatory issues are even more dearly acknowledged now. The “3Rs” (replacement, reduction, refinement) charts its course to humane as well as efficacious science with artificial intelligence (“AI”) as a chief facilitator of such a transition. By delivering sophisticated analytical power, AI renders 3Rs enforceable with concomitant predictions, simulations, and validations while minimizing animal subjects’ dependency. Machine and deep-learning algorithms are capable of processing massive, complex datasets to simulate human biology, forecast therapy outcomes, and discover candidate drugs, thereby circumventing large-scale animal usage. In such a manner, AI can directly facilitate replacement while promoting reduction through maximized experimental designs as well as refinement through data-driven improvements for animal welfare. The interplay of AI as well as the latest alternative methods such as organoids, organs-on-chips, and body-on-chip devices is emphasized within this review, which also briefs on evolving international policies with regard to AI ethics guidelines. This mini-review evaluates the modern role of AI in biomedical research, presenting its role across drug discovery, toxicology, disease modeling, and personalized therapy. We evaluate both encouraging prospects and existing challenges such as strict validation requirements and ethics controls as well as interdisciplinary collaboration that inform AI’s embracing within animal-research-free models.