Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

With growing interest in applying artificial intelligence (AI) to population breast cancer screening, the evidence base has expanded rapidly. This systematic review aims to systematically review and summarise the published evidence on the use of AI in breast cancer screening.

We conducted a systematic review of primary studies assessing AI for screening mammography, extracting test-accuracy metrics (sensitivity, specificity, recall and cancer detection rates) and workflow outcomes.

We searched the Cochrane Breast Cancer Group Specialised Register, Cochrane CENTRAL, PubMed, Embase (Elsevier), Scopus, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform from January 2012 to June 2025; we also screened reference lists of included studies and relevant reviews. No language restrictions were applied.

Primary studies evaluating AI for screening mammography (digital mammography or digital breast tomosynthesis) in asymptomatic women, assessing AI as a standalone reader or AI-assisted radiologist workflows versus radiologists alone. Eligible designs included randomised trials, prospective paired reader studies, real-world implementation/registry cohorts, retrospective cohorts and multireader-multicase reader studies conducted in population-based or opportunistic screening settings. Key outcomes included diagnostic accuracy metrics (eg, sensitivity, specificity, Area Under the Curve (AUC) and/or programme metrics (cancer detection rate (CDR), recall/abnormal interpretation rate, positive predictive value, arbitration/workload). We excluded protocols, pilot/feasibility studies, case reports, editorials and studies without relevant accuracy or screening outcomes.

Two independent reviewers extracted data and assessed risk of bias. Study quality was appraised with Quality Assessment of Diagnostic Accuracy Studies-2 and an AI-specific critical appraisal tool, and findings were synthesised narratively with stratification by study design and AI integration role.

31 studies met the inclusion criteria, encompassing randomised controlled trials, prospective paired-reader studies, registry-based implementations and retrospective simulations, representing more than two million screening examinations across Europe, Asia, North America and Australia. When used as a second reader or within double-reading workflows, AI generally maintained or modestly increased sensitivity (up to +9 percentage points (PP)) while preserving or improving specificity. Triage and decision-referral configurations delivered the greatest operational benefit, reducing reading volumes by 40–90% while maintaining non-inferior cancer detection when thresholds were conservatively calibrated. Stand-alone AI achieved AUC values comparable to radiologists and similar cancer detection in real-world, non-enriched cohorts, although interval-cancer follow-up remains incomplete in several datasets. In prospective randomised evidence, including the Mammography Screening with Artificial Intelligence trial (MASAI) trial, AI-supported screening achieved higher CDRs (6.4 versus 5.0 per 1000; p=0.0021) with stable or reduced false-positive and recall rates. Across implementation and simulation settings, integration of AI reduced radiologist workload substantially, with triage and band-pass approaches reducing the number of reads by approximately 40–90%. Overall certainty is limited by heterogeneity across study designs, reliance on enriched datasets for some accuracy estimates and incomplete interval-cancer follow-up in several major studies.

Contemporary AI systems show diagnostic performance that is broadly comparable to radiologists and can substantially reduce reading workload, particularly when used as a second reader or triage tool. Emerging prospective evidence supports their safe integration in these roles, although transparent reporting, standardised evaluation and long-term population studies are still required before considering AI as a stand-alone reader. AI may improve workflow efficiency and possibly cancer detection, but definitive evidence on safety, especially interval cancer outcomes, remains essential.

To identify the key characteristics required for hypothetical diagnostic tests to be cost-effective for diagnosing giant cell arteritis (GCA).

Combined decision tree and Markov cohort state-transition models were used to evaluate the cost-utility of new diagnostic tests compared with the standard pathways of biopsy and clinical judgement, with and without ultrasound. Input parameters were derived from secondary data and expert opinions. The analysis adopted a lifetime horizon and the UK National Health Service (NHS) perspective, using a willingness-to-pay threshold of £20 000 per quality-adjusted life year (QALY). Bivariate deterministic sensitivity analyses identified the maximum test price at varying diagnostic performance levels, and probabilistic sensitivity analyses over 5000 simulations provided 95% CIs.

UK.

Patients with symptoms suggestive of GCA.

Percentage of GCA-related and glucocorticoid-related complications avoided, maximum test price and incremental QALYs at each sensitivity and specificity combination.

A biomarker test incorporated into a hypothetical diagnostic pathway with perfect accuracy (100% sensitivity and specificity) can be priced up to £7245 (95% CI £5763 to £8727) and remain cost-effective compared with a standard pathway of temporal artery biopsy and clinical judgement. Against a standard pathway including ultrasound, the biomarker test can be priced up to £8606 (£6741 to £10 471). The test’s value was more strongly influenced by improvements in specificity than in sensitivity. The maximum prices decreased with earlier starting age, lower clinician adherence, shorter time horizons and shorter durations of glucocorticoid-related effects.

The study highlights the potential for hypothetical tests to improve GCA diagnosis and reduce glucocorticoid toxicity, while demonstrating their market viability for use within the NHS. It also illustrates how early-stage economic models can provide valuable insights into potential cost-effectiveness to inform the test development process.

The climate crisis represents an unprecedented threat to global health systems, requiring urgent decarbonisation across all healthcare sectors. Although medical diagnostics affect approximately 70% of clinical decisions, they receive disproportionately little attention in healthcare sustainability research. This knowledge gap is particularly concerning as the impact of climate change on health may increase diagnostic testing demands, potentially creating a feedback loop of environmental harm. Carbon assessment methodologies within healthcare are heterogeneous and context-specific, with varying methodologies and assumptions complicating systematic evaluation. The proposed scoping review aims to map and analyse the existing literature on medical diagnostic carbon footprints, synthesising methodological approaches, core assumptions and evidence gaps to guide future decarbonisation efforts.

Four electronic databases (PubMed, Embase, Web of Science and HealthcareLCA) will be systematically searched from their inception to January 2025. The search strategy will combine subject headings and text words related to (1) carbon footprint and (2) diagnostic testing of any form. Only published, peer-reviewed studies will be considered, with no exclusions made on the basis of language, location or publication date. Two independent reviewers will screen titles/abstracts and full texts, with disagreements resolved through discussion. Data will be extracted using a bespoke tool developed and piloted by the research team to capture study characteristics, methodological approaches and key findings. Narrative synthesis and descriptive quantitative analysis will be used to analyse the data. The review will be reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist.

Ethical approval is not required for this scoping review. Our findings will be published in a peer-reviewed scientific journal and presented at scientific conferences.

Extrapulmonary tuberculosis (EPTB) is a serious type of tuberculosis (TB) which can cause systemic clinical manifestations. Rapid diagnosis of EPTB for intervention is of great importance. Nanopore sequencing as a third-generation gene sequencing method is a new kind of rapid TB detection. Previous studies have shown that nanopore sequencing has higher diagnostic sensitivity and specificity for TB diagnosis compared with other diagnostic methods. The aim of this research is to develop a systematic review and meta-analysis protocol for assessing the accuracy of nanopore sequencing in diagnosing EPTB.

This protocol was conducted in strict adherence to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols guidelines. The study protocol has been prospectively registered with the International Prospective Register of Systematic Reviews under the unique identifier CRD42024608415. We will search Chinese databases and English databases in June 2026. Chinese databases will include Wanfang database, China National Knowledge Infrastructure. English databases will include PubMed, EMBASE and the Cochrane Library. Adhering strictly to the reference standard outlined in this protocol, we will screen the literature. The Quality Assessment of Diagnostic Accuracy Studies will be used by us to assess the methodological quality of the included studies. The statistical tools used are Stata with midas commands and RevMan, and we will perform meta-analysis, generate forest plots and Summary Receiver Operating Characteristic curves. A p value of less than 0.05 will be considered statistically significant. If significant heterogeneity exists and there is a sufficient number of studies, we will investigate its source through subgroup analysis and meta-regression.

This investigation uses publicly accessible data repositories, exempting it from ethical review board approval requirements. On finalisation of the analysis, findings will be prepared for dissemination through submission to a reputable medical journal employing rigorous peer review processes. The study methodology adheres to established protocols for systematic review and meta-analysis.

CRD42024608415

Novel diagnostics, particularly point-of-care (POC) tests, play a crucial role in the early detection and management of infectious diseases, especially in resource-limited settings. Ensuring test performance and quality while minimising the risk of human error becomes more relevant when shifting testing tasks from highly controlled settings like centralised laboratories to people with minimal training. Applying usability and human factors engineering principles can reduce the challenges related to human errors. Despite existing frameworks and tools, the practical application of usability guidelines remains variable across different settings.

This scoping review protocol outlines a systematic investigation of current practices in assessing the usability of novel diagnostics, particularly POC tests for infectious diseases intended for use in low-income and middle-income countries. The review will analyse original research studies of all designs and product dossiers that report on the usability evaluation or validation of a diagnostic test for an infectious disease. A qualitative synthesis of the data extracted from the articles will be conducted. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and the Joanna Briggs Institute guidelines for this scoping review.

No ethical approval is required because individual patient data will not be included. The findings will be disseminated through publication in a peer-reviewed journal.

Tuberculosis (TB) remains a significant public health challenge in many African communities, where underreporting and underdiagnosis are prevalent due to barriers in accessing care and inadequate diagnostic tools. This is particularly concerning in hard-to-reach areas with a high burden of TB/HIV co-infection, where missed or delayed diagnoses exacerbate disease transmission, increase mortality and lead to severe economic and health consequences. To address these challenges, it is crucial to evaluate innovative, cost-effective, community-based screening strategies that can improve early detection and linkage to care.

We conduct a prospective, community-based, diagnostic, pragmatic trial in communities of the Butha Buthe District in Lesotho and the Greater Edendale area of Msunduzi Municipality, KwaZulu-Natal in South Africa to compare two strategies for population-based TB screening: computer-aided detection (CAD) technology alone (CAD4TBv7 approach) versus CAD combined with point-of-care C reactive protein (CRP) testing (CAD4TBv7-CRP approach). Following a chest X-ray, CAD produces an abnormality score, which indicates the likelihood of TB. Score thresholds informing the screening logic for both approaches were determined based on the WHO’s target product profile for a TB screening test. CAD scores above a threshold prespecified for the CAD4TBv7 approach indicate confirmatory testing for TB (Xpert MTB/RIF Ultra). For the CAD4TBv7-CRP approach, a CAD score within a predefined window requires the conduct of the second screening test, CRP, while a score above the respective upper threshold is followed by Xpert MTB/RIF Ultra. A CRP result above the selected cut-off also requires a confirmatory TB test. Participants with CAD scores below the (lower) threshold and those with CRP levels below the cut-off are considered screen-negative. The trial aims to compare the yield of detected TB cases and cost-effectiveness between two screening approaches by applying a paired screen-positive design. 20 000 adult participants will be enrolled and will receive a posterior anterior digital chest X-ray which is analysed by CAD software.

The protocol was approved by National Health Research Ethics Committee in Lesotho (NH-REC, ID52-2022), the Human Sciences Research Council Research Ethics Committee (HSRC REC, REC 2/23/09/20) and the Provincial Health Research Committee of the Department of Health of KwaZulu-Natal (KZ_202209_022) in South Africa and from the Swiss Ethics Committee Northwest and Central Switzerland (EKNZ, AO_2022–00044). This manuscript is based on protocol V.4.0, 19 January 2024. Trial findings will be disseminated through peer-reviewed publications, conference presentations and through communication offices of the consortium partners and the project’s website (https://tbtriage.com/).

ClinicalTrials.gov (NCT05526885), South African National Clinical Trials Register (SANCTR; DOH-27-092022-8096).