arXiv:2608. 13695v1 Announce Type: cross Abstract: Large language model providers routinely cite multilingual safety benchmarks spanning a dozen or more languages as evidence that their models are safe for non-English-speaking users.
By Chialuka Prisca-Mary Onuoha, Bright Etornam Sunu, Rashidat Sikiru
arXiv:2508. 00923v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against.
By Jiazhen Pan (Cherise), Bailiang Jian (Cherise), Paul Hager (Cherise), Yundi Zhang (Cherise), Che Liu (Cherise), Friederike Jungmann (Cherise), Hongwei Bran Li (Cherise), Julian Canisius (Cherise), Chenyu You (Cherise), Junde Wu (Cherise), Jiayuan Zhu (Cherise), Fenglin Liu (Cherise), Yuyuan Liu (Cherise), Niklas Bubeck (Cherise), Moritz Knolle (Cherise), Chen (Cherise), Chen (Cherise), Christian Wachinger, Zhenyu Gong, Cheng Ouyang, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert
arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.
By Jose D. Posada, David Love, Somalee Datta, Priya Desai
arXiv:2606. 19640v1 Announce Type: cross Abstract: AI and large language models (LLMs) have emerged as promising tools to address global mental health challenges.
By Yunkai Xu, Saeed Abdullah
arXiv:2601. 09853v3 Announce Type: replace-cross Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises.
By Sraavya Sambara, Yuan Pu, Ayman Ali, Vishala Mishra, Lionel Wong, Monica Agrawal
arXiv:2608. 15691v1 Announce Type: cross Abstract: Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance.
By Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao