Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.
arXiv:2606. 30951v1 Announce Type: cross Abstract: Micro-ultrasound ($\mu$US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability.
By Mohammad Mahdi Abootorabi, Sina Namazi, Armin Saadat, Lyuyang Wang, Obed Dzikunu, Paul F. R. Wilson, Zhuoxin Guo, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
arXiv:2606. 19174v1 Announce Type: cross Abstract: Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability.
By Fangyijie Wang, Jianjun Yu, Wentao Shi, Haixia Huang, Ran Shi, Gu\'enol\'e Silvestre, Kathleen M. Curran
arXiv:2606. 28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging.
By Maria Xenochristou, Ashutosh Joshi, Korosh Vatanparvar, Mohammad Abuzar Hashemi, Prasad Kasu, Deepak Bansal, Anchal Nema, Nivedita Wadhwa, Prashams S Jain, Rebecca Abraham, Will Kimbrough, Dilek Hakkani-Tur, Wilko Schulz-Mahlendorf
arXiv:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.
By Ayusha Abbas, Saram Abbas, Kabita Adhikari
arXiv:2606. 28164v1 Announce Type: cross Abstract: Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis.
By Darya Taratynova, Ahmed Aly, Numan Saeed, Mohammad Yaqub
arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.
By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha
arXiv:2607. 25933v1 Announce Type: cross Abstract: Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning.
By Rui Yang, Weihao Xuan, Yi Lin, Zhuhan Bao, Jonathan Chong Kai Liew, Matthew Yu Heng Wong, Nicol\'as Lescano, Nikita R. Paripati, Emily Ling-Lin Pai, Jiarui Liu, Heli Qi, Heng-Jui Chang, Benny Kai Guo Loo, Huitao Li, Kunyu Yu, Yufan Wang, Chuan Hong, Shijian Lu, Douglas Teodoro, Naoto Yokoya, Ross Koppel, Mona Diab, Hua Xu, David W. Bates, Nan Liu, Yifan Peng
arXiv:2608. 04766v1 Announce Type: cross Abstract: A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources.
By Bin Pu, Jiewen Yang, Liwen Wang, Ying Tan, Guannan He, Xingbo Dong, Qika Lin, Jiarong Guo, Lixian Yang, Zuozhu Liu, Shengli Li, Kenli Li
arXiv:2606. 19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis.
By Yucheng Xing, Ling Huang, Pei Liu, Jingying Ma, Jiaqing Xu, Kai He, Mengling Feng
Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment. In this paper, we introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding that systematically addresses these limitations.
arXiv:2608. 16268v1 Announce Type: cross Abstract: Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation.
By J. Raphael Sch\"afer, Kai Geissler, Till Nicke, Chiara Tappermann, Karoline Heber, Eike Petersen, Habib Mergan, Lars Ole Schwen, Nick Weiss, Annika Gerken, Jan Hendrik Moltz, Tom Bisson, Isil Dogan O, Tim-Rasmus Kiehl, Norman Zerbe, Sefer Elezkurtaj, Robin S. Mayer, Nadine Flinner, Peter Wild, Isabel Dahm, Felix Peisen, Heinrich von Busch, Robert Grimm, Sebastian Arndt, Lisa Siegler, Matthias Stefan May, Antje Prasse, Natalia Artysh, Fabian Kiessling, Johannes Lotz