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FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology

Nat Commun. 2025 Sep 25;16(1):8388. doi: 10.1038/s41467-025-63050-9.

ABSTRACT

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.

PMID:40998789 | PMC:PMC12462499 | DOI:10.1038/s41467-025-63050-9

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Google Introduces VaultGemma: An Experimental Differentially Private LLM

VaultGemma is a 1B-parameter Gemma 2-based LLM that Google trained from scratch using differential privacy (DP) with the aim of preventing the model from memorizing and later regurgitating training data. While still a research model, VaultGemma could enable applications cases in healthcare, finance, legal, and other regulated sectors.

By Sergio De Simone
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Application of Nudges to Design Clinical Decision Support Tools: Systematic Approach Guided by Implementation Science

Background: Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools have had mixed results and are often disliked by clinicians. Principles from behavioral economics, including “nudges,” may improve the effectiveness and clinician satisfaction of CDS tools. Objective: This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. Methods: We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments and Ego (MINDSPACE) nudge framework and the Practical, Robust Implementation and Sustainability Model (PRISM) implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. A case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure was used to illustrate how these frameworks can be applied in real-life scenarios. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. Results: Four iterative steps guided by PRISM were defined: 1) engage partners for user-centered design, 2) develop a shared understanding of the nudge types, 3) determine the nudge type for the overarching CDS format, and 4) brainstorm and prioritize nudge types and forms to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. Conclusions: We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues in designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. The findings can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.
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Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study

Background: Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. Objective: This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework. Methods: A qualitative study was conducted using semistructured interviews with 22 nurses from across the Netherlands, representing 3 hospital types: general (n=9), top-clinical (n=12), and academic (n=1). The sample included a diverse mix of practicing nurses, nurses-in-training, and clinical nurse information officers, with clinical experience ranging from 1.5 to 38 years. Interview transcripts were analyzed thematically, beginning with an inductive coding approach to identify key factors. These were then categorized deductively using the extended FITT framework. In total, 988 code instances were examined. To ensure analytical rigor, the coding process was separately conducted by 2 researchers and reviewed by an expert panel. Results: A total of 26 distinct factors were identified, categorized into 4 FITT dimensions: technology-individual, technology-task, task-individual, and organizational context. Of these, 11 factors were facilitators (eg, cognition, clarification, and prevention), 7 were barriers (eg, alarm fatigue, poor design, and limited digital proficiency), and 8 were both facilitators and barriers depending on the context (eg, acceptance, workload, and training). In addition, key value tensions emerged, such as the balance between standardization and professional autonomy, and the trade-off between enhanced decision support and increased administrative burden. Conclusions: The findings underscore the complexity of CDSS adoption in nursing practice, highlighting the interaction of facilitators and barriers across FITT dimensions. Practical recommendations include participatory design processes, targeted training programs, advanced alert management systems, and strong organizational support. Addressing value tensions and aligning CDSS functionality with nurses’ workflows can enhance adoption and optimize patient outcomes. Trial Registration:
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Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01852-3

Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
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Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01956-w

Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments
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Ophthalmic drug discovery and development using artificial intelligence and digital health technologies

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01954-y

Ophthalmic drug discovery and development using artificial intelligence and digital health technologies
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What top VCs want from AI founders: Inside the investor lens with Jon McNeill, Aileen Lee, and Steve Jang at TechCrunch Disrupt 2025

Jon McNeill (DVx Ventures), Aileen Lee (Cowboy Ventures), and Steve Jang (Kindred Ventures) share what AI founders need to know now: from defensibility to term sheets. TechCrunch Disrupt 2025 takes place October 27–29 in San Francisco. Register before tomorrow ends to save up to $668.
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