Artificial intelligence‐driven bioinformatics: From analytical pipelines to model‐centered biological discovery

Abstract Traditional bioinformatic pipelines, which focus on predefined statistical analyses, are increasingly inadequate for handling high‐dimensional, heterogeneous, and context‐rich multi‐omics, spatial, and clinical datasets. Artificial intelligence (AI) is extending modern bioinformatics from predefined analytical pipelines toward learned biological representations, intervention‐oriented modeling, and more automated discovery workflows. This review comprehensively summarizes AI advances across genomic interpretation, single‐cell and spatial transcriptomics, multi‐omics integration, structural bioinformatics, disease stratification, precision medicine, and drug discovery. We categorize underlying AI technologies, including classical machine learning, deep representation learning, generative foundation models, and executable AI agents, and highlight their complementary use cases alongside inherent limitations and failure modes. Building on rather than claiming priority for the model‐centered paradigm described in recent literature, this review contributes a cross‐domain, failure‐aware evaluation framework organized around six unified axes: representation validity, task‐specific incremental value, robustness under biological and technical distribution shift, causal support, resource requirements, and validation/governance readiness. We further discuss key technical and social barriers and hierarchically prioritize future research directions. We conclude that AI complements rather than replaces conventional bioinformatic tools, extending the capacity for hypothesis generation and mechanism‐driven validation. This review provides structured and practical guidance for robust, interpretable, and clinically translatable AI‐driven biological discovery.