The Lancet Digital Health

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...

BackgroundDelivering timely, high-quality feedback on resident scholarly projects is labour-intensive, especially in large programmes. We developed an AI-assisted evaluation system, powered by the open-weight LLaMA-3.1 large-language model (LLM), to generate formative feedback on Family Medicine residents scholarly projects and compared its performance with expert human evaluators. MethodsWe evaluated whether the AI-generated feedback achieves comparable quality to expert feedback. The tool ing...

Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under domain shift remains insufficiently explored. We propose Boundary-Explicit Guided Attention U-Net (BEGA-UNet), a boundary-aware segmentation architecture that introduces explicit edge modeling as a structural inductive bias to enhance both segmentation accuracy and cross-domain robustness. The framework integrates three components: an Edge-Guided ...

BackgroundThe 2024 blood culture bottle shortage brought diagnostic resource allocation to the forefront, reflecting persistent, foundational challenges with low-value testing and empiric treatment approaches under clinical uncertainty. ObjectiveTo determine whether a machine learning approach using electronic medical record data can predict bacteremia more effectively than existing systems and practices to guide diagnostic testing and empiric treatment strategies. MethodsIn a retrospective co...

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic cli...

ObjectiveTo systematically identify and characterize methodological heterogeneity in sepsis case detection methods using the MIMIC-III database or the eICU-CRD, and to quantify the resulting variability in sepsis detection rates. Materials and MethodsWe conducted a PRISMA-guided systematic review of PubMed and Web of Science (2016-2024), and stratified studies by cohort definition to obtain comparable subsets. We extracted information on sepsis case detection methodology across six domains: par...

BackgroundCandidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients. MethodsWe developed a deep learning model (PyTorch_EHR) to predict 7-day candidemia risk by using electronic health record data from two large cohorts (Houston Methodist Hospital System [HMHS] and ...

PurposeLarge language models (LLMs) are used for biomedical text processing, but individual decisions are often hard to audit. We evaluated whether enforcing a mechanically checkable "show your work" quote affects accuracy, stability, and verifiability for trial eligibility-scope classification from abstracts. MethodsWe used 200 oncology randomized controlled trials (2005 - 2023) and provided models with only the title and abstract. Trials were labeled with whether they allowed for the inclusio...

BackgroundPersonalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for actionable clinical insight. Current machine learning approaches often provide predictions, but fail to inform clinicians when those predictions are unreliable. ObjectiveDevelop a deployment-readiness framework that integrates causal inference, interpretable effect-trees, and calibration assessment to distinguish actionable signal from unreliable variati...

BackgroundSystematic reviews (SRs) are essential for evidence-based medicine but require extensive time and resources for abstract screening. Large language models (LLMs) offer potential for automating this process, yet concerns about data privacy, intellectual property protection, and reproducibility limit the use of cloud-based solutions in research settings. ObjectiveTo evaluate the performance of a locally deployed 20-billion parameter LLM for automated abstract screening in systematic revi...

Academic institutions privilege norms of continuous productivity and uninterrupted availability, creating conformity pressures that systematically disadvantage those who deviate from an implicit template of the ideal academic. This study explores how doctoral students and faculty in the health sciences perceive the reproduction of social homogeneity. Semi-structured interviews were conducted with nine participants at a German university hospital. Data were analysed using reflexive thematic anal...

UK-based quantitative research on the health and education outcomes of Unaccompanied Asylum-Seeking Children (UASC) remains limited, especially at national level. Linked administrative data provide an unprecedented opportunity to study these outcomes among UASC. This paper lays a foundation for further research, particularly examining the influence of socio-demographic, legal and environmental factors on UASCs health and educational outcomes. We described the UASC population with a first record...

BackgroundLarge language models (LLMs) are increasingly deployed in medical contexts as patient-facing assistants, providing medication information, symptom triage, and health guidance. Understanding their robustness to adversarial inputs is critical for patient safety, as even a single safety failure can lead to adverse outcomes including severe harm or death. ObjectiveTo systematically evaluate the safety guardrails of state-of-the-art LLMs through adversarial red-teaming specifically designe...

Wearable devices present transformative opportunities for personalized healthcare through continuous monitoring of digital biomarkers; however, individual variations in device wear time could mask or otherwise impact signal identification. Despite the widespread adoption of wearable devices in research, no comprehensive framework exists for understanding how wear time varies across populations or for addressing wear time-related biases in analysis. Using Fitbit data from 11,901 participants in t...

Objective: Evaluating and monitoring patients with cervical spondylotic myelopathy (CSM) remains a challenge due to limited tools for assessing objective neurological disability longitudinally and in the home environment. Given their prevalence and low cost, mobile health (mHealth), and specifically smartphone technologies offer a promising approach to fill this gap. This study explored stakeholder perspectives on the role of mHealth in CSM monitoring to inform development of a smartphone-based ...

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabetes. Total diabetes (diagnosed and undiagnosed) affects approximately 40.1 million Americans, or 12% of the population, with roughly 1.5 million new cases diagnosed annually. Continuous Glucose Monitoring (CGM) provides real-time, 24/7 insights into glycemic variability, detecting dangerous highs, lows, and trends that HbA1c (a 3-month average) mis...

BackgroundPlatelet count and C-reactive protein (CRP) are blood tests commonly used in primary care as part of diagnostic work up for symptomatic patients. Abnormal results of these tests can indicate an undetected cancer; however, it is not known whether the association between an abnormal test result and cancer risk varies by patient ethnicity. MethodsThis cohort study used routinely collected primary and secondary health care records in England with linkage to national cancer registry data. ...

ObjectivesVisit-to-visit blood pressure variability (VVV BPV) is an underutilised risk factor for cardiovascular disease (CVD). This study aims to determine the minimum number of BP measurements needed and to identify cut-off values for the standard deviation (SD), coefficient of variation (CV), and average real variability (ARV) of systolic and diastolic VVV BPV to predict CVD risk in primary care. MethodsWe analysed data from the electronic practice-based research network (ePBRN) in Southwest...

Large language models (LLMs) perform strongly across a wide range of medical applications, yet it remains unclear whether such success reflects genuine understanding of medical concepts. We present an ontology-grounded, concept-centered evaluation of medical concept understanding in LLMs. Using 6,252 phenotype concepts from Human Phenotype Ontology, we decompose concept understanding into three core dimensions--concept identity, concept hierarchy, and concept meaning--and design corresponding be...

Thyroid carcinoma is one of the most prevalent endocrine malignancies worldwide, and accurate preoperative differentiation between benign and malignant thyroid nodules remains clinically challenging. Diagnostic methods that medical practitioners use at present depend on their personal judgment to evaluate both imaging results and separate clinical tests, which creates inconsistency that leads to incorrect medical evaluations. The combination of radiological imaging with clinical information syst...