Bioinformatics is an interdisciplinary field that combines biology and computational analysis, enabling us to discover patterns and insights from complex biological data. We systematically examine publication trends, collaboration networks, and thematic evolution through a comprehensive bibliometric analysis of Artificial Intelligence (AI) applications in bioinformatics literature from 2010 to 2024. Using VOSviewer and Biblioshiny software, we analyzed documents from the Scopus database to identify research trends, influential authors, leading institutions, and collaborative networks. The purpose is to provide a systematic overview of research outputs, highlight thematic clusters, and identify emerging directions to guide future research efforts. This paper presents a systematic bibliometric overview of research outputs in AI-driven bioinformatics. We filtered documents from the Scopus database and identified 344 relevant papers to gain insights into AI growth and its impact on bioinformatics. It emphasizes AI’s role in advancing knowledge and healthcare innovations. Through keyword frequency analysis, citation network examination, and cluster analysis, we systematically identify critical research gaps, including the need for advanced deep learning (DL) models, explainable AI, multi-omics data integration, and improved model validation protocols. Moreover, we outline several key areas for future research: developing AI models to predict and analyze protein misfolding, improving the interpretability and clinical validation of AI systems, integrating multi-omics data for personalized cancer treatment computational frameworks, designing AI tools to study non-coding Ribonucleic Acid (RNAs) in gene regulation, and innovating low-cost accessibility computational platforms to support precision medicine in low-resource settings. As a result, this study provides a resource for many stakeholders, including researchers, policymakers, and practitioners seeking to realize AI’s full potential in bioinformatics and related life sciences by outlining future research directions.
Karnyoto et al. (2026) studied this question.