Artificial Intelligence Tools in Biomedical Research: Part 1—Literature Search and Knowledge Mining

The exponential growth of biomedical literature has rendered traditional search methods inadequate. Artificial intelligence (AI) tools have emerged and are developing as transformative solutions for literature search and knowledge mining. This first article of a series, intended to address different components of biomedical research, provides a comprehensive analysis of recent advancements, practical applications, and challenges in deploying AI for biomedical research. The objective of this work is to synthesize the evolution, capabilities, and limitations of AI-driven tools for literature discovery, summarization, and evidence synthesis, offering actionable insights for researchers across disciplines. AI tools have progressed from keyword-based retrieval to semantic and multimodal approaches. Platforms such as Elicit, BioGPT, and PubTator 3.0 enable rapid extraction of gene-disease associations and evidence-based insights, while ResearchRabbit and Connected Papers visualize citation networks. Systematic review tools like Rayyan and Covidence reduce screening time by up to 50%. Variability in output quality, risk of hallucination, and lack of algorithmic transparency pose challenges. Open-source solutions (<i>e.g.</i>, BioGPT, DeepChem) and explainability-focused tools (<i>e.g.</i>, Scite.ai) offer promising pathways to mitigate these concerns. AI-driven literature workflows can accelerate hypothesis generation, systematic reviews, and translational research. However, close human expert oversight remains indispensable to ensure rigor and interpretive accuracy. These technologies are not a passing trend; they are forging the contours of tomorrow's research landscape. The peril lies as much in reckless adoption as in willful oblivion. This editorial serves as a general roadmap for integrating trustworthy AI tools into biomedical research, fostering high-impact innovation. <i>Antioxid. Redox Signal.</i> 44, 1-10.