A bibliometric review on the trends, issues and concerns on AI assisting in diagnostics, drug discovery, personalized medicine, and treatment planning

This study presents a comprehensive bibliometric analysis of the evolving role of Artificial Intelligence (AI) in healthcare, with a specific focus on diagnostics, drug discovery, personalized medicine, and treatment planning. Drawing upon data from the Scopus database between 2021 and 2025, the research examines 48 scholarly publications sourced from 47 journals, conference proceedings, and book chapters. The analysis aims to uncover prevailing research trends, collaboration patterns, thematic developments, and key concerns surrounding the integration of AI into critical healthcare domains. Findings reveal a significant surge in scientific production over the past five years, with an annual growth rate exceeding 120%, indicating heightened global interest in AI-driven healthcare solutions. Despite the rising volume of publications, the average number of citations per article showed a declining trend, highlighting the saturation of the field and a shift from foundational to more applied research. Thematic mapping and keyword analysis identified core research clusters centered on AI technologies such as machine learning, deep learning, and natural language processing applied to oncology diagnostics, clinical decision-making, and precision medicine. Emerging ethical themes, such as data privacy, algorithmic bias, transparency, and explainable AI, also surfaced, reflecting the growing interdisciplinary engagement. Geographically, countries such as India, the United States, Australia, and the United Kingdom lead in publication output, although international collaboration remains uneven, with many contributions being single-country efforts. Notably, citation impact does not always align with productivity, as evidenced by countries such as the UK and Finland, which have demonstrated high citation rates despite lower publication volumes. Visualization tools, such as VOSviewer and Bibliometrix, revealed an increasingly dense and diversified research landscape, with intellectual structures that bridge technical AI development, ethical governance, and healthcare implementation. While AI’s integration into healthcare shows remarkable progress, the study identifies challenges in equitable collaboration, responsible innovation, and ensuring meaningful societal impact. The bibliometric insights offer valuable guidance for researchers, policymakers, and funders, emphasizing the need for interdisciplinary approaches, global cooperation, and ethical oversight to responsibly advance AI’s transformative potential in healthcare.