arXiv:2609.22161v1 Announce Type: cross
Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how...
By Yuzheng Fan, Haochun Wang, Sendong Zhao, Xiao Han, Ming Ma, Bing Qin
arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.
By Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez
arXiv:2609.17223v1 Announce Type: new
Abstract: Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their...
By Moritz A. Knolle, Martin J. Menten, Laurin Lux, M\'elanie Roschewitz, Emma A. M. Stanley, Georgios Kaissis, Daniel Rueckert, Ben Glocker
arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.
By Mahdi Alkaeed
arXiv:2609.38543v1 Announce Type: new
Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time...
By Lukas Thede, Yash Kumar Atri, David Chen, Danielle Bitterman, Matthias Bethge, Tom Hartvigsen, Zeynep Akata
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra