INTEGRATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL LIBRARIES: TRANSFORMING INFORMATION SERVICES
DOI:
https://doi.org/10.29121/JISSI.v2.i1.2026.47Keywords:
Artificial Intelligence, Medical Libraries, Information Services, Natural Language, Processing, Machine Learning, Healthcare InformaticsAbstract
Purpose: This paper discusses the application of artificial intelligence (AI) to medical libraries and its contribution to the revolution of information services. It examines the role of AI technologies in increasing the level of information retrieval, user interaction, and evidence-based decision-making within a hospital setting.
Design/Methodology/Approach: The research is based on a systematic narrative review, in which the analysis of peer-reviewed journals on AI in medical libraries, academic libraries, and healthcare information systems is made. Published materials of the last 3-5 years (2013-2026) were searched, filtered, and examined to find important themes, trends, advantages, and obstacles related to AI use.
Findings: The results are that AI technologies and especially machine learning, natural language processing, and smart chatbots can make a substantial contribution to information discovery, routine processes automation, and personalized services. The integration of AI allows to improve efficiency and scalability and facilitate clinical decision-making with quicker access to biomedical information. Nonetheless, the issue of data privacy risks, algorithmic bias, technical illiteracy, and infrastructural obstacles prevent its mass implementation.
Limitations of the Research: The research relies on secondary data hence restricting the ability to empirically validate the research. The results are also informed by the access to published literature, where most of the studies are based in the developed regions.
Practical Implications: The article lays stress on the necessity of planning, professional education, ethics, and effective data management as the key to the successful implementation of AI in medical libraries.
Originality/Value: The proposed study delivers a comprehensive view of AI-driven change in medical libraries and identifies some of the research gaps, which serve as guidance in subsequent research and practice
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Copyright (c) 2026 Sunil Kumar, Jovita Kaur, Riju Kumari, Kehar Singh, Sonia Devi, Mamta (Author)
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