Plants are continuously exposed to diverse environmental stresses, including drought, salinity, temperature extremes, and nutrient limitations, which significantly constrain agricultural productivity and ecosystem stability. Understanding how plants perceive, integrate, and respond to these stresses at the genetic level has become a central focus of modern plant science. Recent advances in genomics, transcriptomics, and functional genetics have revealed that plant adaptation to environmental stress is governed by complex, multilayered regulatory networks rather than single stress-responsive genes. These networks involve stress-sensing mechanisms, signal transduction pathways, transcriptional reprogramming, and post-transcriptional regulation, collectively shaping plant performance under adverse conditions. Genetic variation within and among plant species provides a critical resource for stress tolerance, enabling plants to optimize growth, metabolism, and reproductive success in fluctuating environments. Moreover, emerging tools such as genome-wide association studies, epigenetic profiling, and genome editing technologies have accelerated the identification of key genetic determinants underlying stress resilience. Integrating genetic insights with physiological and ecological perspectives has enhanced our understanding of how plants balance stress tolerance with growth and yield. This trade-off is particularly relevant under climate change scenarios. This review synthesizes recent progress in elucidating the genetic mechanisms that drive plant adaptation and performance under environmental stress. It highlights major stress-responsive gene families, regulatory networks, and adaptive strategies, and discusses how these insights can be translated into crop improvement programs. By bridging fundamental genetic research with applied plant breeding, this review underscores the potential of gene integration to develop resilient plant systems capable of sustaining productivity in increasingly challenging environments.

Artificial intelligence (AI) is transforming diagnostic decision-making across the life sciences, yet evidence remains fragmented across human, veterinary, plant, environmental, and microbial domains. We conducted a PRISMA-ScR scoping review (protocol preregistered on OSF; details in Supplement) and bibliometric analysis covering 2015–2025. Searches in PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore (plus arXiv/bioRxiv tagging) identified 28,541 records and 68 preprints; after de-duplication and dual screening, 689 primary studies met inclusion criteria (with 42 preprints analyzed descriptively but excluded from citation-based bibliometrics). Human medicine dominated the corpus (81.3%), followed by veterinary (6.2%), plant (5.1%), environmental (4.2%), and microbial diagnostics (3.2%). Modalities were led by medical imaging (65.0%), then omics (18.0%), time-series (8.1%), spectra (4.1%), text (2.9%), and eDNA (1.9%). Reported performance was high (median AUROC 0.94), but external validity and transparency were limited: only 28.0% performed external validation, 9.0% used prospective designs, and 5.2% reported probability calibration. Reproducibility signals were weak (code availability 22.9%, data availability 18.0%, explicit preregistration rare), and fairness/bias assessments appeared in 7.0% of studies, concentrated in human health. Bibliometrics showed rapid year-on-year growth, with the United States (32.1%) and China (28.4%) leading output and collaborations. Trends indicate a shift from task-specific CNNs to multimodal/foundation-model approaches and early data-fusion gains, but consistent gaps persist in leakage controls, calibration, subgroup reporting, and regulatory alignment. We recommend domain-aware, leakage-resistant splits; at least one independent, real-world evaluation; prevalence-aware metrics with calibration and decision-utility; open datasheets/model cards; and federated/external benchmarking to probe generalization. These practices can convert impressive internal results into dependable, equitable diagnostics that work across clinics, farms, rivers, and labs.