The Lancet Digital Health

The Updated Sydney System (USS) provides a standardized framework for grading gastritis and stratifying gastric cancer risk. However, subjective observer variability and labor-intensive workflows impede its routine clinical use. To address these challenges, we developed SydneyMTL, a multi-task deep learning framework that uses Multiple Instance Learning (MIL) with task-specific attention pooling to predict severity grades across all five USS attributes simultaneously. Trained on an unprecedented...

BackgroundSepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths annually. Natural language processing (NLP)-driven clinical decision support systems (CDSS) have been deployed in several NHS Trusts to enable automated early detection of sepsis from unstructured clinical notes, yet causal evidence of their effectiveness at the hospital level remains limited. ObjectiveTo estimate the causal effect of implementing N...

Image classification on digital pathology images relies heavily on convolutional neural networks (CNNs), yet the behavior of alternative neural computing paragigms in this domain remains insufficiently characterized. Spiking neural networks (SNNs), which process information through event-driven spike-based dynamics, have recently become trainable at scale but have not been evaluated under standardized colorectal pathology benchmarks. This study presents the first controlled comparison of SNNs an...

Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of-origin classification can accelerate quality control and enable early detection of mislabeled specimens. We developed a deep learning model that classifies the organ of origin from H&E-stained slides using a single low-resolution thumbnail per slide in under one second. For training, we used thumbnails from 16,624 slides from the TCGA and CPTAC ar...

Large language models (LLMs) are evolving into diagnostic co-pilots, yet current benchmarks fail to test the integrated, stepwise reasoning required in diagnostic pathology. Here, we present Pathologys Last Exam (PLE), a curated, highly detailed, text-based benchmark of 100 complex cases spanning organ systems, enriched for rare/challenging entities, plus 20 adversarial cases designed to stress-test model safety. Each case provides structured blocks (Primary, Clinical, Histopathology, IHC/Specia...

Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumo...

Background and AimsArtificial intelligence (AI) is increasingly applied to histological assessment in inflammatory bowel disease (IBD), but most approaches quantify features in isolation and ignore their anatomical location within the mucosa. We developed and validated PAIR-IBD (Perspectum AI Reading in IBD), an AI system that quantifies inflammatory cell populations, crypt injury, and epithelial damage within defined mucosal compartments to distinguish active disease, remission, and equivocal c...

This study investigates the efficacy of transformer-based deep learning architectures--specifically, Vision Transformer (ViT), Class Attention in Image Transformers (CaiT), and Data-Efficient Image Transformers (DeiT)--for the binary classification of colorectal polyps using the Minimalist Histopathology Image Analysis Dataset (MHIST). The dataset comprises 3,152 hematoxylin and eosin (H&E)-stained Formalin Fixed Paraffin-Embedded (FFPE) images annotated as either Hyperplastic Polyps (HP) or Ses...

Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology attempt to predict singular proteins, there is a need to predict the downstream expression of proteins to indicate the propagation of signals. RNA expression provides novel information, but does not provide information about the downstream propagation of protein signals or whether those signals are functional. Using Reverse Phase Protein Array (RPPA...

IntroductionKidney biopsy reports contain rich information that is clinically actionable and useful for research. However, the narrative format hinders scalable reuse. We here investigated whether open-source large language models (LLMs) can extract relevant, standardized readouts from native kidney biopsy pathology reports. MethodsGerman free-text native kidney biopsy reports were parsed with three open-source LLMs (Llama3 70B, Llama3 8B, MedGemma) to generate structured JSON outputs covering ...

ObjectivesTo evaluate the clinical performance of a cadmium-zinc-telluride-(CZT-) based photon-counting computed tomography (PCCT) system for low-dose lung cancer screening (LCS-LDCT) using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system. MethodsSix 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-g...

BackgroundCardiovascular diseases (CVDs) remain the leading global cause of morbidity and mortality. In clinical practice, 10-year risk prediction tools such as the Pooled Cohort Equations, QRISK3, and SCORE2 are widely used because of their transparency and clinical trustworthiness, but they rely heavily on biomarkers and medical history. Hence, most recommendations concentrate on pharmaceutical or procedural management, and in many situations, crucial biomarker indicators are unavailable, maki...

BackgroundGastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs). Given the limited reproducibility of established clinicopathological risk models, deep learning (DL) ...

BackgroundRecent global outbreaks of Mpox have posed significant diagnostic challenges, particularly in resource-limited settings. Conventional diagnostic methods are often inaccessible due to cost, logistical constraints, or lack of trained personnel. These limitations highlight the urgent need for alternative, scalable diagnostic strategies. This study explored the application of machine learning (ML) classifiers trained on clinical symptom data as a rapid, cost-effective tool for Mpox detecti...

Pathology faces persistent challenges including a global shortage of specialists, uneven access to expertise, increasing diagnostic complexity, and a growing need for second-opinion consultations. While digital and telepathology platforms address parts of this problem, existing solutions often trade accessibility for structured, workflow-aware clinical integration. At the same time, multimodal medical AI shows promise for diagnostic support but raises concerns regarding transparency, automation ...

PurposeTo develop SCOPE (Small-lesion COntextual Pancreatic Evaluator), a deep learning model designed to improve CT detection of small pancreatic lesions--pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (PanNETs), and cystic lesions--by integrating voxel-level features with global context. Materials and MethodsThis retrospective study used three independent datasets. A development cohort of 4,065 contrast-enhanced CT scans was used to train a deep neural network that ...

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSExperts in poison control centers must accurately and efficiently assess the severity of an exposure, neither delaying care nor pointlessly sending patients to the hospital, using only the information given during a first phone call. To help healthcare professionals (HP) make these difficult decisions, we developed and evaluated a machine learning-based algorithm that predicts whether a patient should seek medical help or not, based solely on the ...

Medication adherence is critical for effective management of chronic diseases and reducing healthcare burdens. Statins, commonly prescribed for cardiovascular disease prevention, require sustained, lifelong adherence, yet maintaining long-term adherence remains a significant challenge. Here, we analysed longitudinal electronic health records from over one million statin users in Finland and Italy to characterise adherence trajectories and their determinants. Using functional data analysis, we id...

BackgroundArtificial intelligence chatbots (AICs) are increasingly being integrated into scholarly publishing, with the potential to automate routine editorial tasks and streamline workflows. In traditional, complementary, and integrative medicine (TCIM) publishing, editorial and peer review processes can be particularly complex due to diverse methodologies and culturally embedded knowledge systems, presenting unique opportunities and challenges for AIC adoption. MethodsAn anonymous, online cro...

Background & AimsLiver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor prognosis. While clinical surveillance of patients with CLD generates extensive longitudinal data, its unstructured free-text nature hinders large-scale research. To unlock this real-world evidence, we developed a scalable framework using open-source Large Language Models (LLMs) to transform unstructured clinical text into structured data. Me...