Clinical Application of Large Language Models for Breast Conditions: A Systematic Review
Billy Ho Hung Cheung, Karen Gwyn Poon, Cheuk Fai Lai, Ka Chun Lam, Michael Tiong-Hong Co, Ava Kwong · medRxiv · 2024
Abstract Background The application of artificial intelligence (AI) like Large Language Models (LLM) into the healthcare system has been a frequently discussed topic in recent years. Materials and Methods We conducted a systemic review on primary studies about the applications of LLM in breast conditions. The studies are then categorized into their respective domains, namely diagnosis, management recommendations and communication for patients. Results The diagnostic accuracy ranged from 74.3% to 99.6% across different investigation modalities. The concordance of management recommendations ranged from 50% to 70% while the prognostic evaluation of breast cancer patients of distant recurrence showed an accuracy of 75% to 88%. In regards to patient communication, it is revealed that 18-30% of the references used by the LLM were irrelevant. Conclusion This study highlights the potential benefits of LLM in strengthening patient communication, diagnose and management of patients with breast conditions. With standardized protocol and guideline to minimize potential risks, LLM can be a valuable tool to support future clinicians in the field of breast management.