Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review Authors Mehwish Mooghal Department of Breast Surgery, Aga Khan University Hospital Saba Anjum Department of Histopathology, Aga Khan University Hospital Wajiha Khan Department of Surgery and Medicine, Dow University of Health Sciences Karachi Hassan Tariq Department of Histopathology, Armed Forces Institute of Pathology, Rawalpindi Amna Babar Department of Histopathology, Shifa International Hospital, Rawalpindi Lubna Mushtaq Vohra Department of Surgery, Aga Khan University Hospital Karachi, Pakistan. DOI: https://doi.org/10.47391/JPMA.AKU-9S-17 Abstract Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial intelligence (AI) can optimize Ki-67 assessment, improving precision and workflow efficiency in BC management. The study presents a critical analysis of the current state of AI-powered Ki-67 assessment. Results demonstrate high agreement between AI and standard Ki-67 assessment methods highlighting AI's potential as an auxiliary tool for pathologists. Despite these advancements, the review acknowledges limitations such as the restricted timeframe and diverse study designs, emphasizing the need for further research to address these concerns. In conclusion, AI holds promise in enhancing Ki-67 assessment's precision and workflow efficiency in BC diagnosis. While challenges persist, the integration of AI can revolutionize BC care, making it more accessible and precise, even in resource-limited settings. Keywords: Artificial Intelligence, Ki-67 Antigen, Prognosis, Pathologists, Breast Cancer Downloads Full Text Article Published 2024-05-03 How to Cite Mehwish Mooghal, Saba Anjum, Wajiha Khan, Hassan Tariq, Amna Babar, & Lubna Mushtaq Vohra. (2024). Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review. Journal of the Pakistan Medical Association, 74(4), S–109. https://doi.org/10.47391/JPMA.AKU-9S-17 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX Issue Vol. 74 No. 4 (2024): 9th AKU Annual Surgical Conference - Surgery In The Digital Era Section Literature Review License Copyright (c) 2024 Journal of the Pakistan Medical Association This work is licensed under a Creative Commons Attribution 4.0 International License.