arXiv:2512.08674v1 Announce Type: new Abstract: Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.

npj Digital Medicine, Published online: 18 November 2025; doi:10.1038/s41746-025-02066-3

Reply to: Utilizing foundation models for developing clinical tools

Nature, Published online: 08 May 2024; doi:10.1038/s41586-024-07370-8

Enhanced light–molecule interactions in high-finesse fibre-based Fabry–Pérot microcavities are used to detect and profile individual unlabelled solution-phase biomolecules, leading to potential applications in the life and chemical sciences.

Nature, Published online: 12 July 2023; doi:10.1038/s41586-023-06327-7

Retrotransposons hijack the alternative end-joining DNA repair process of the host for a circularization step to synthesize their second-strand DNA.