arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.
By Minh-Ha Nguyen, Erica Gray, Chih-Ting Yang, Rizwan Hamid, Lingyao Li, Siyuan Ma, Thomas A. Cassini, Cathy Shyr
arXiv:2607. 25497v1 Announce Type: cross Abstract: Pathology foundation models are approaching clinical deployment, yet remain vulnerable to systematic non-biological variation across centres.
By Cl\'ement Grisi, Jeroen van der Laak, Geert Litjens
arXiv:2607. 25497v2 Announce Type: replace-cross Abstract: Pathology foundation models encode non-biological variation introduced by tissue preparation, staining and scanning, enabling shortcut learning that undermines generalisation across institutions.
By Cl\'ement Grisi, Jeroen van der Laak, Geert Litjens
arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.
By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi
arXiv:2608. 05982v1 Announce Type: new Abstract: Inadequate target--disease linkage accounts for 40--50\% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients.
By Pui Chung Siu, Claudia Cabrera, Mani Mudaliar, Arkaitz Zubiaga
arXiv:2411. 05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI.
By Turker Berk Donmez