Aug 2026· Nature· Vol 656, pp. 577 - 589· 0 citations· 189 references
Medicine
TL;DR
The Review examines rapid LLM adoption in clinical care, outlining emerging security and safety risks across development stages, key protective layers, clinically relevant threats and current mitigation responsibilities in a single integrated framework.
A thorough review of the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations are reviewed to suggest a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices.
Noah Wright· International Journal of Mod...· 0 citations
This paper conducts a comprehensive analysis of evaluation methods, deployment processes, and governance strategies for LLMs in the healthcare field, focusing on three key issues: model version drift, multilingual external validation, and prompt injection security governance.
Song-Bin Guo, Sui-Xing Zhong, Yixian Ma et al.· International Journal of Sur...· 0 citations
A lifecycle-based framework utilizing the collaborative “red-blue-purple” teaming model is introduced, utilizing the collaborative “red-blue-purple” teaming model to ensure that clinical guardrails remain robust against evolving adversarial tactics.
F. Umer, Muhammad Muthar Shaikh, Absar Ur Rahman· BDJ Open· 1 citation
This thesis proposes a unified two-layer approach to enhancing the trustworthiness and security of LLM-enabled systems and introduces the Adaptive LLM Threat Response (ALTR) framework, which integrates behavioral anomaly detection, context-aware prompt classification, and temporal threat memory to identify and mitigate adversarial interactions in real time.
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 0 citations
Current evidence indicates that LLMs have substantial potential to enhance healthcare delivery, research, and personalized medicine, but they should currently be regarded as supportive tools rather than autonomous clinical decision-makers.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· 0 citations