The paper introduces a Mixture of Multicenter Experts (MoME) framework that leverages diverse clinical strategies to reduce bias in medical AI without sharing data across institutions. MoME integrates specialized expertise from multiple centers, improving generalizability and adaptability of a multimodal target volume delineation model for prostate cancer radiotherapy. The model, trained with few-shot imaging and clinical notes, outperformed baselines, especially in high inter‑center variability or limited data scenarios, and allows local customization without cross‑institutional data exchange.
arXiv:2603. 09448v2 Announce Type: replace-cross Abstract: Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers.
The paper presents a generative AI model that predicts 3‑D radiation dose distributions based solely on user‑defined preference settings, allowing planners to balance trade‑offs between organs‑at‑risk and target volumes. This approach offers greater flexibility and personalization compared to traditional deep‑learning models that rely on institutional reference plans. The authors report that their method can outperform the Varian RapidPlan model in adaptability and plan quality in certain scenarios, and it is designed for easy integration with clinical treatment planning systems.
arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.
The paper introduces Generalist‑Specialist Mixture‑of‑Experts (GS‑MoE), a two‑branch architecture that combines a cross‑modal generalist model with modality‑specific specialists through domain‑constrained feature fusion. GS‑MoE improves detection of rare pathologies in multimodal medical imaging, achieving significant per‑class F1 gains and outperforming dense and specialist‑only MoE baselines while using about 53% fewer active parameters at inference. The study demonstrates that balancing cross‑modal shared representations with expert routing can enhance performance on low‑prevalence conditions.
arXiv:2609.37682v1 Announce Type: new Abstract: The rapid expansion of large-scale medical datasets and computational resources has driven significant progress in medical foundation models. Given the...
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.
arXiv:2608. 14657v1 Announce Type: new Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single data modalities and their inability to leverage structured clinical knowledge.
Dose-PlanNet is a physics-guided 3D deep learning model that predicts radiotherapy dose distributions for prostate cancer, aiming to automate complex treatment planning. In a prospective trial, the model achieved comparable target coverage while slightly reducing target homogeneity, yet it significantly improved high-dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in most cases across both moderate hypofraction and stereotactic body radiation therapy arms.
Dose-PlanNet is a physics-guided 3D deep learning architecture that predicts radiotherapy dose distributions for prostate cancer. In a prospective trial, the model achieved target coverage comparable to clinical plans while slightly reducing target homogeneity, yet it significantly improved high‑dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in 11 of 14 Moderate Hypofraction and 9 of 12 SBRT plans, demonstrating that physics-informed deep learning can accelerate radiotherapy workflows without compromising dosimetric quality.
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.
arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.
arXiv:2607. 06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference.