arXiv:2511.08986v2 Announce Type: replace 
Abstract: Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hinder timely validation as new models emerge rapidly. Here, we propose BRIDGE, a data-reuse RCT design for AI-based risk models. AI risk models support a broad range of interventions, including screening, treatment selection, and clinical alerts. BRIDGE trials recycle participant-level data from completed trials of AI models when legacy and updated models make concordant predictions, thereby reducing the enrollment requirement for subsequent trials. We provide a practical checklist for investigators to assess whether reusing data from previous trials allows for valid causal inference and preserves type I error. Using real-world datasets across breast cancer, cardiovascular disease, and sepsis, we demonstrate concordance between successive AI models, with up to 64.8% overlap in top 5% high-risk cohorts. We then simulate a series of breast cancer screening studies, where our design reduced required enrollment by 46.6%--saving over US$2.8 million--while maintaining 80% power. By transforming trials into adaptive, modular studies, our proposed design makes Level I evidence generation feasible for every model iteration, thereby accelerating cost-effective translation of AI into routine care.

تعتبر التجارب السريرية العشوائية (RCTs) ضرورية لتأكيد الفعالية السريرية لأدوات الذكاء الاصطناعي الطبية، لكن تكاليفها العالية والجداول الزمنية الطويلة تشكل تحديات كبيرة. يقدم التصميم المقترح BRIDGE حلاً من خلال إعادة استخدام بيانات المشاركين من التجارب السابقة عندما تنتج نماذج الذكاء الاصطناعي تنبؤات متشابهة. يمكن أن يقلل هذا النهج من متطلبات التسجيل بنسبة 46.6% ويوفر أكثر من 2.8 مليون دولار مع الحفاظ على قوة إحصائية تبلغ 80%، مما يبرز إمكانيته في التحقق الفعال من نماذج الذكاء الاصطناعي في مجالات مثل سرطان الثدي وأمراض القلب والإنتان.

Los ensayos controlados aleatorios (ECA) son esenciales para validar la efectividad clínica de las herramientas de IA médica, pero sus altos costos y largos plazos presentan desafíos significativos. El diseño propuesto BRIDGE ofrece una solución al reutilizar datos de participantes de ensayos anteriores cuando los modelos de IA producen predicciones similares. Este enfoque puede reducir los requisitos de inscripción en un 46.6% y ahorrar más de 2.8 millones de dólares, manteniendo una potencia estadística del 80%, demostrando su potencial para una validación eficiente de modelos de IA en áreas…

Les essais contrôlés randomisés (ECR) sont essentiels pour valider l'efficacité clinique des outils d'IA médicale, mais leurs coûts élevés et leurs délais prolongés posent des défis importants. La conception proposée BRIDGE offre une solution en réutilisant les données des participants d'essais précédents lorsque les modèles d'IA produisent des prédictions similaires. Cette approche peut réduire les besoins d'inscription de 46,6 % et économiser plus de 2,8 millions de dollars tout en maintenant une puissance statistique de 80 %, démontrant son potentiel pour une validation efficace des modèles…

Randomized controlled trials (RCTs) are essential for validating the clinical effectiveness of medical AI tools, but their high costs and lengthy timelines pose significant challenges. The proposed BRIDGE design offers a solution by reusing participant-level data from previous trials when AI models yield similar predictions. This approach can significantly reduce enrollment requirements by 46.6% and save over $2.8 million while maintaining an 80% statistical power, demonstrating its potential for efficient AI model validation in areas like breast cancer, cardiovascular disease, and sepsis.

Data reuse enables cost-efficient randomized trials of medical AI models

arXiv:2601.08336v1 Announce Type: new 
Abstract: Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.

تم اقتراح شبكة متعددة الوسائط جديدة تُدعى BioMorphNet لتحسين تصنيف الأنسجة وتحليل صور الشرائح الكاملة من خلال دمج الميزات الشكلية والتعبير الجيني المكاني، مما يعالج القيود في منهجيات البحث الحالية حول السرطان.

Se ha propuesto una nueva red multimodal llamada BioMorphNet para mejorar la clasificación de tejidos y el análisis de imágenes de diapositivas completas mediante la integración de características morfológicas y expresión génica espacial, abordando las limitaciones de las metodologías actuales en la investigación del cáncer.

Un nouveau réseau multimodal nommé BioMorphNet a été proposé pour améliorer la classification des tissus et l'analyse des images à grande échelle en intégrant des caractéristiques morphologiques et l'expression génique spatiale, répondant aux limitations des méthodologies de recherche sur le cancer actuelles.

A new multimodal network named BioMorphNet has been proposed to enhance tissue classification and whole-slide image analysis by integrating morphological features and spatial gene expression, addressing limitations in current cancer research methodologies.

Tissue Classification and Whole-Slide Images Analysis via Modeling of the Tumor Microenvironment and Biological Pathways

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Data reuse enables cost-efficient randomized trials of medical AI models

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