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Received — 26 September 2025 ⏭ Favourites

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

✇Omics In Lung
By: Samuel P Border · Ricardo Melo Ferreira · Nicholas Lucarelli · Suhas Katari Chaluva Kumar · Anindya S Paul · David Manthey · Laura Barisoni · Yulia A Levites Strekalova · Jessica Ray · Ying-Hua Cheng · Avi Z Rosenberg · John E Tomaszewski · Sayat Mimar · Jeffrey B Hodgin · John W Hickey · Bei Wei · Fiona Ginty · Arivarasan Karunamurthy · Juexin Wang · Mauminah Raina · Gloria S Pryhuber · Jeffrey Purkerson · HuBMAP consortium · Tarek M El-Achkar · Sanjay Jain · Michael T Eadon · Pinaki Sarder
25 September 2025 at 18:00

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

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

✇Journal of Medical Internet Research
By: Katy E Trinkley · Danielle Maestas Duran · Shelley Zhang · Meagan Bean · Larry A Allen · Russell E Glasgow · Amy G Huebschmann · Chen-Tan Lin · Jason N Mansoori · Anna M Maw · James Mitchell · Laura D Scherer · Daniel D Matlock
26 September 2025 at 04:00
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.

Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study

✇Journal of Medical Internet Research
By: Matthijs Berkhout · Koen Smit · Danielle Sent · Rob Kusters · Johan Versendaal · Thijs van Houwelingen
25 September 2025 at 18:00
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:

Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis

✇npj Digital Medicine
By: José Morano · Botond Fazekas · Emese Sükei · Ronald Fecso · Taha Emre · Markus Gumpinger · Georg Faustmann · Marzieh Oghbaie · Ursula Schmidt-Erfurth · Hrvoje Bogunović
25 September 2025 at 08:00

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

Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments

✇npj Digital Medicine
By: Yafei Si · Yurun Meng · Xi Chen · Ruopeng An · Limin Mao · Bingqin Li · Hazel Bateman · Han Zhang · Hongbin Fan · Jiaqi Zu · Shaoqing Gong · Zhongliang Zhou · Yudong Miao · Xiaojing Fan · Gang Chen
25 September 2025 at 08:00

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

Ophthalmic drug discovery and development using artificial intelligence and digital health technologies

✇npj Digital Medicine
By: Haoran Cheng · Joy Le Yi Wong · Chrystie Wan Ning Quek · Jeffrey L. Goldberg · Vinit B. Mahajan · Tien Yin Wong · Jodhbir S. Mehta · Daniel S. W. Ting · Darren S. J. Ting
25 September 2025 at 08:00

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
Received — 25 September 2025 ⏭ Favourites

The Biodiversity Cell Atlas: mapping the tree of life at cellular resolution

✇Nature - Issue - nature.com science feeds
By: Arnau Sebé-Pedrós · Amos Tanay · Mara K. N. Lawniczak · Detlev Arendt · Stein Aerts · John Archibald · Maria Ina Arnone · Mark Blaxter · Phillip Cleves · Susana M. Coelho · Mafalda Dias · Casey Dunn · Anamaria Elek · Jonathan Frazer · Toni Gabaldón · Jesse Gillis · Xavier Grau-Bové · Roderic Guigó · Oliver Hobert · Jaime Huerta-Cepas · Manuel Irimia · Allon Klein · Harris Lewin · Christopher J. Lowe · Heather Marlow · Jacob M. Musser · László G. Nagy · Sebastián R. Najle · Lior Pachter · Sadye Paez · Irene Papatheodorou · Michael J. Passalacqua · Nikolaus Rajewsky · Seung Y. Rhee · Thomas A. Richards · Tatjana Sauka-Spengler · Lauren M. Saunders · Eve Seuntjens · Jordi Solana · Yuyao Song · Ulrich Technau · Bo Wang
24 September 2025 at 08:00

Nature, Published online: 24 September 2025; doi:10.1038/s41586-025-09312-4

The Biodiversity Cell Atlas aims to create comprehensive single-cell molecular atlases across the eukaryotic tree of life, which will be phylogenetically informed, rely on high-quality genomes and use shared standards to facilitate comparisons across species.

Molecular advances in early-stage and locally advanced non-small cell lung carcinoma: Shaping the future of precision oncology-systematic review

✇Omics In Lung
By: Wael Abdo Hassan
24 September 2025 at 18:00

Sci Prog. 2025 Jul-Sep;108(3):368504251383055. doi: 10.1177/00368504251383055. Epub 2025 Sep 24.

ABSTRACT

ObjectiveTo synthesize recent molecular advances that inform diagnosis, risk-stratification, and perioperative treatment in early-stage and locally advanced non-small cell lung carcinoma (NSCLC), with emphasis on comprehensive genomic profiling, minimal residual disease (MRD) detection by circulating tumor DNA (ctDNA), and the translation of biomarkers into targeted and immunotherapy strategies.MethodsSystematic review registered in PROSPERO (CRD420251076423). Searches of PubMed, Scopus, Web of Science, and Embase (January 2015-April 2025) followed PRISMA 2020/PRISMA-S. From 4640 records, 890 duplicates were removed; 3750 titles/abstracts were screened; 150 full texts were assessed; 75 studies met inclusion criteria. Risk of bias used Newcastle-Ottawa Scale (NOS) for observational studies and Cochrane RoB 2 tool for randomized controlled trials; certainty was summarized with GRADE where applicable.ResultsActionable alterations (e.g. EGFR, ALK, KRAS, MET, RET, BRAF, NTRK) are prevalent in early-stage NSCLC and comparable to advanced disease, supporting routine comprehensive genomic profiling in curative-intent settings. Next-generation sequencing (NGS) and ctDNA enable the detection of MRD, earlier relapse prediction, and dynamic treatment monitoring. Perioperative strategies integrating targeted therapy and immunotherapy (e.g. adjuvant EGFR-TKI, neoadjuvant chemo-immunotherapy) improve pathological and disease-free outcomes in selected biomarker-defined populations. Evidence profiles generally show low-to-moderate risk of bias and moderate-to-high certainty for key outcomes related to profiling and MRD, with heterogeneity across platforms and endpoints.ConclusionsMolecular advances-particularly broad NGS and ctDNA-based MRD-are reshaping the perioperative management of early and locally advanced NSCLC, enabling precision selection for targeted and immunotherapy approaches. Standardization of testing workflows and reporting, and cost-effective implementation are priorities for equitable adoption and for future trials that combine NGS, MRD, and multi-omic/AI-driven risk stratification.

PMID:40990633 | PMC:PMC12461064 | DOI:10.1177/00368504251383055

Expanding care coordination in an integrated health system through causal machine learning

✇npj Digital Medicine
By: Ben J. Marafino · Colleen Plimier · Patricia Kipnis · Gabriel J. Escobar · Laura C. Myers · Michelle C. Donnelly · John D. Greene · Marc D. Flagg · Jessica R. Small · Vincent X. Liu
24 September 2025 at 08:00

npj Digital Medicine, Published online: 24 September 2025; doi:10.1038/s41746-025-01925-3

Expanding care coordination in an integrated health system through causal machine learning

Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review

✇Journal of Medical Internet Research
By: Manuel Alberto Silva · Emma J Hamilton · David A Russell · Fran Game · Sheila C Wang · Sofia Baptista · Matilde Monteiro-Soares
25 September 2025 at 01:00
Background: Diabetes-related foot ulceration (DFU) is a common complication of diabetes, with a significant impact on survival, health care costs, and health-related quality of life. The prognosis of DFU varies widely among individuals. The International Working Group on the Diabetic Foot recently updated their guidelines on how to classify ulcers using “classical” classification and scoring systems. No system was recommended for individual prognostication, and the group considered that more detail in ulcer characterization was needed and that machine learning (ML)–based models may be the solution. Despite advances in the field, no assessment of available evidence was done. Objective: This study aimed to identify and collect available evidence assessing the ability of ML-based models to predict clinical outcomes in people with DFU. Methods: We searched the MEDLINE database (PubMed), Scopus, Web of Science, and IEEE Xplore for papers published up to July 2023. Studies were eligible if they were anterograde analytical studies that examined the prognostic abilities of ML models in predicting clinical outcomes in a population that included at least 80% of adults with DFU. The literature was screened independently by 2 investigators (MMS and DAR or EH in the first phase, and MMS and MAS in the second phase) for eligibility criteria and data extracted. The risk of bias was evaluated using the Quality In Prognosis Studies tool and the Prediction model Risk Of Bias Assessment Tool by 2 investigators (MMS and MAS) independently. A narrative synthesis was conducted. Results: We retrieved a total of 2412 references after removing duplicates, of which 167 were subjected to full-text screening. Two references were added from searching relevant studies’ lists of references. A total of 11 studies, comprising 13 papers, were included focusing on 3 outcomes: wound healing, lower extremity amputation, and mortality. Overall, 55 predictive models were created using mostly clinical characteristics, random forest as the developing method, and area under the receiver operating characteristic curve (AUROC) as a discrimination accuracy measure. AUROC varied from 0.56 to 0.94, with the majority of the models reporting an AUROC equal or superior to 0.8 but lacking 95% CIs. All studies were found to have a high risk of bias, mainly due to a lack of uniform variable definitions, outcome definitions and follow-up periods, insufficient sample sizes, and inadequate handling of missing data. Conclusions: We identified several ML-based models predicting clinical outcomes with good discriminatory ability in people with DFU. Due to the focus on development and internal validation of the models, the proposal of several models in each study without selecting the “best one,” and the use of nonexplainable techniques, the use of this type of model is clearly impaired. Future studies externally validating explainable models are needed so that ML models can become a reality in DFU care. Trial Registration: PROSPERO CRD42022308248; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022308248

Article: InfoQ AI, ML and Data Engineering Trends Report - 2025

✇InfoQ
By: Srini Penchikala · Savannah Kunovsky · Anthony Alford · Daniel Dominguez · Vinod Goje
24 September 2025 at 19:05

This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.

By Srini Penchikala, Savannah Kunovsky, Anthony Alford, Daniel Dominguez, Vinod Goje
Received — 24 September 2025 ⏭ Favourites

Fine-Tuning Methods for Large Language Models in Clinical Medicine by Supervised Fine-Tuning and Direct Preference Optimization: Comparative Evaluation

✇Journal of Medical Internet Research
By: Thomas Savage · Stephen P Ma · Abdessalem Boukil · Ekanath Rangan · Vishwesh Patel · Ivan Lopez · Jonathan Chen
24 September 2025 at 05:15
Background: Large language model (LLM) fine tuning is the process of adjusting out-of-the-box model weights using a dataset of interest. Fine tuning can be a powerful technique to improve model performance in fields like medicine, where data access is restricted and LLMs may have poor out-of-the-box performance. Objective: In this study we investigated the benefits of fine tuning with supervised fine tuning (SFT) and direct preference optimization (DPO) across a range of LLM applications for medicine Methods: We use Llama3 7B and Mistral 7B v2 to compare the performance of SFT and DPO across four datasets for common natural language tasks in medicine. The tasks evaluated were simple classification, clinical reasoning, summarization, and clinical triage. Results: Clinical Reasoning accuracy increased 8% and 7% with DPO over SFT for Llama3 (p value 0.003) and Mistral2 (p value 0.004) respectively. Summarization quality, graded on a five point Likert scale, increased 0.13 and 0.10 for Llama3 and Mistral2 (p values

Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study

✇Journal of Medical Internet Research
By: Fabio Dennstädt · Max Schmerder · Elena Riggenbach · Lucas Mose · Katarina Bryjova · Nicolas Bachmann · Paul-Henry Mackeprang · Maiwand Ahmadsei · Dubravko Sinovcic · Paul Windisch · Daniel Zwahlen · Susanne Rogers · Oliver Riesterer · Martin Maffei · Eleni Gkika · Hathal Haddad · Jan Peeken · Paul Martin Putora · Markus Glatzer · Florian Putz · Daniel Hoefler · Sebastian M Christ · Irina Filchenko · Janna Hastings · Roberto Gaio · Lawrence Chiang · Daniel M Aebersold · Nikola Cihoric
24 September 2025 at 03:30
Background: Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts. Objective: This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical experts. The aim was to evaluate the overall quality of answers, as well as the potential harmfulness of the answers if used for clinical decision-making. Methods: Physicians from 10 departments of European hospitals collected questions arising in the clinical practice of radiation oncology. Fifty of these questions were answered by 3 senior radiation oncology experts with at least 10 years of work experience, as well as the LLM Llama3-OpenBioLLM-70B (Ankit Pal and Malaikannan Sankarasubbu). In a blinded review, physicians rated the overall answer quality on a 5-point Likert scale (quality), assessed whether an answer might be potentially harmful if used for clinical decision-making (harmfulness), and determined if responses were from an expert or the LLM (recognizability). Comparisons between clinical experts and LLMs were then made for quality, harmfulness, and recognizability. Results: There were no significant differences between the quality of the answers between LLM and clinical experts (mean scores of 3.38 vs 3.63; median 4.00, IQR 3.00-4.00 vs median 3.67, IQR 3.33-4.00; P=.26; Wilcoxon signed rank test). The answers were deemed potentially harmful in 13% of cases for the clinical experts compared with 16% of cases for the LLM (P=.63; Fisher exact test). Physicians correctly identified whether an answer was given by a clinical expert or an LLM in 78% and 72% of cases, respectively. Conclusions: A state-of-the-art medical LLM can answer real-life questions from the clinical practice of radiation oncology similarly well as clinical experts regarding overall quality and potential harmfulness. Such LLMs can already be deployed within the local hospital environment at an affordable cost. While LLMs may not yet be ready for clinical implementation as general AI assistants, the technology continues to improve at a rapid pace. Evaluation studies based on real-life situations are important to better understand the weaknesses and limitations of LLMs in clinical practice. Such studies are also crucial to define when the technology is ready for clinical implementation. Furthermore, education for health care professionals on generative AI is needed to ensure responsible clinical implementation of this transforming technology.
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