Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shrotriya, Shobhit; Banu, P. K. Nizar; Kulkarni, Avi; Kumar, Vinod G. Title A name given to the resource Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;448 SIST;pp.17-28 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8126-6_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8126-6_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028362364?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028362364?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shrotriya S., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Banu P.K.N., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Kulkarni A., ThoughtSphere, CA, United States; Kumar V.G., Accenture, Bengaluru, India Description An account of the resource Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, affecting individuals across all ages and sexes. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the field of medical diagnostics, offering the potential to enhance the detection, diagnosis, and prediction of breast cancer. Despite these advancements, challenges remain, including the need for large, diverse datasets to train robust models, the integration of AI tools into clinical workflows, and addressing ethical concerns related to AI in healthcare. This paper explores the application of Large Language Models (LLMs) using embeddings in breast cancer management, focusing on its ability to analyze medical data, including imaging, histopathology datasets to identify patterns that may be imperceptible to human experts. Datasets from real-world setting have been secured for analysis across multiple models. Convolutional Neural Network (CNN) model and custom-built large language model are employed to demonstrate the precision and accuracy of Generative AI techniques and observed that custom-built LLM with 98.44% outperforms the traditional AI approaches such as CNN with 61.72%. Future studies can further establish how these models can assist in stratifying patients based on risk, thereby enabling personalized treatment plans that can reduce overtreatment and improve quality of life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence (AI); Breast cancer; Embeddings; Generative AI; Large language models (LLM); Machine learning (ML) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981968125-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online