ItemConference Paper

Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation

Title
Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation
Creator
Shrotriya, Shobhit; Banu, P. K. Nizar; Kulkarni, Avi; Kumar, Vinod G.
Description
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.
Source
Smart Innovation, Systems and Technologies;Volume;448 SIST;pp.17-28
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial intelligence (AI); Breast cancer; Embeddings; Generative AI; Large language models (LLM); Machine learning (ML)
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21903018; ISBN: 978-981968125-9;
Format
online
Language
English
Type
Conference paper

Citation

Shrotriya, Shobhit; Banu, P. K. Nizar; Kulkarni, Avi; Kumar, Vinod G., “Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25606.

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