PLOS Digital Health

Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined Group Antenatal Care (G-ANC), digital maternal health systems, and development of an ML-enabled risk stratification model for hypertensive disorders of pregnancy (HDP). This study examined the health-system conditions shaping the pathway from routine care to actionable ML-enabled risk information. Methods We conducted a retrospective mixed-methods implementation analysis of this project that was implemented between 2022 and 2024 in Geita, Tanzania. The analysis triangulated endline evaluation findings, quantitative exit interviews with pregnant women, focus group discussions with women and healthcare workers, key informant interviews, project implementation records, routine data from Tanzania's Unified Community System (UCS), and documented ML development experience. Relevant quotations from the final evaluation report were systematically screened, selected, and coded. An abductive analysis combined inductively derived themes with the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and a study-specific ML implementation pathway. Results Seven themes emerged across three phases of the implementation pathway. ML readiness began before the algorithm, with reliable clinical measurement and documentation; patient participation and task-sharing redistributed data generation. The paper-to-digital transition shaped which information became ML-ready data. The potential value of ML depended on workflow redesign rather than prediction alone. Technology created an efficiency paradox in which task-sharing reduced workload while staffing shortages and confirmatory work generated new burdens. Advanced analytics depended on basic infrastructure, while sustainability relied on teamwork, trust, user demand, and local ownership. Conclusions Our findings suggest that successful ML implementation in maternal health begins with health-system readiness before the algorithm itself. A critical paper-to-digital transition stage was a major determinant of data quality and ML readiness. ML-enabled maternal risk stratification should therefore be approached as a sociotechnical intervention spanning measurement, digitization, clinical confirmation, and follow-up. This implementation readiness is critical for the successful development of an ML-enabled risk stratification system. Crucially, our findings resonate with the key domains of the NASSS framework, underscoring how addressing multidimensional complexities, from technological design to organizational readiness, is vital for successful scale-up and long-term sustainability.

Background Polyendocrine Metabolic Ovarian Syndrome (PMOS), formerly known as Polycystic Ovary Syndrome (PCOS), is a prevalent endocrine disorder with high rates of undiagnosed cases globally. Accessible screening tools are needed to facilitate appropriate management and earlier intervention. As PMOS is frequently characterised by oligo-anovulation, the absence of the characteristic rise in basal body temperature typically seen in ovulatory cycles may serve as a physiological marker for the condition. Objective This study aimed to assess the feasibility of using machine learning to identify individuals with PMOS from temperature data collected by a body-worn device. Methods We used data from 387 users of a vaginal temperature monitor (OvuSenseTM) who responded to a questionnaire. The sample was restricted to individuals with at least three cycles with sufficient temperature data and whose PMOS case/control status could be determined from questions about prior clinical consultation for infertility and conditions for which they take medications. We randomly sampled three menstrual cycles for each participant and derived a set of cycle-level and user-level temperature features. Cycle-level features included cycle length and measures describing the temperature rise indicative of ovulation (e.g. temperature rise, cycle day of temperature rise start). We also constructed a reference cycle representing the typical bi-phasic cycle pattern (created using cycles from those without known fertility conditions) and used this to derive features describing how much a participant's cycles differed from this reference. The cycle-level features were aggregated into user-level features by taking the minimum, maximum, median, and range of the cycle-level features across the three selected cycles for each participant. We used 5-fold nested cross-validation to evaluate the extent that PMOS could be predicted, at the cycle and user levels, using Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Results The average age of participants was 31.97 years (SD=4.58), with 49.6% having a self-reported PMOS diagnosis. The models demonstrated moderate discrimination, with cycle-level AUC-ROC scores ranging from 0.64 (SD=0.02) (LR) to 0.68 (SD=0.04) (RF), and user-level scores ranging from 0.65 (SD=0.07) (LR) to 0.70 (SD=0.04) (RF). All models were reasonably calibrated, though confidence intervals were wide (e.g. RF cycle-level: calibration slope = 0.83 (95% confidence interval [CI]: 0.68, 1.00), calibration intercept = 0.02 (95% CI: -0.11, 0.14); user-level: slope = 0.88 (95% CI: 0.69, 1.15), intercept = -0.01 (95% CI: -0.22, 0.16)). Conclusions This study demonstrates the potential of using body temperature from digital health devices to identify those with PMOS. Such a passive approach to identifying PMOS could help to identify undiagnosed PMOS in those who have not actively sought a diagnosis. Further research is needed to assess its predictive performance and acceptability in a general population using more widely used digital devices.

Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.

Multimodal large language models (MLLMs) are increasingly applied to image-based clinical reasoning, yet their diagnostic reliability in periodontal image interpretation, and the underlying source of their errors, remain poorly characterized. This study evaluated six architecturally distinct MLLMs (Claude Sonnet 4.5, GPT-5.0, Gemini 2.5, GLM-4.6, Sonar, and Grok 4.1) using 50 image-based multiple-choice questions drawn from the American Academy of Periodontology In-Service Examination, spanning clinical photographs, histopathology, radiographs, cardiac rhythm strips, and anatomical illustrations. A sequential two-phase experimental design was used: in Phase 1, each model independently described each image, selected an answer, and provided a supporting citation; in Phase 2, applied only to questions answered incorrectly, models were given an expert-validated visual description and asked to re-answer, allowing diagnostic improvement through visual correction to be measured directly. Expert ground truth for image content was established by a board-certified periodontist and independently validated by a second board-certified periodontist. Model outputs were classified using a dual-process error taxonomy adapted from Norman's model of diagnostic reasoning, distinguishing perceptual errors, arising from inaccurate visual feature extraction, from cognitive errors, arising from flawed reasoning despite accurate perception, with cognitive errors further subdivided into correctable and persistent subtypes, and additional categories capturing compound perceptual-cognitive failures and compensatory reasoning that overcame inaccurate perception. Diagnostic accuracy and error type distribution varied significantly across models and image modality. Correcting inaccurate visual descriptions in Phase 2 improved diagnostic accuracy for a subset of previously incorrect responses, indicating that a meaningful share of errors originated at the level of visual perception rather than clinical reasoning; conversely, a distinct subset of errors persisted despite accurate corrected visual input, indicating reasoning-level failures independent of perceptual accuracy. Some models also reached correct answers despite generating inaccurate image descriptions, reflecting compensatory reasoning resilient to perceptual error. These findings show that aggregate accuracy scores conflate mechanistically distinct failure modes, and that perceptual and cognitive errors carry different implications for how MLLMs might be safely deployed or improved for diagnostic image interpretation. The expert-guided visual correction framework introduced here provides a generalizable, mechanism-based approach to benchmarking multimodal AI diagnostic performance that extends beyond periodontics to other visually driven diagnostic domains in medicine. As MLLMs become increasingly accessible to clinicians, residents, and dental educators, distinguishing perceptual from cognitive failure is essential for guiding responsible clinical use, targeting model refinement, and informing AI-augmented dental education and competency assessment.

Background: Clinical decision support systems (CDSS) are increasingly embedded in national telemedicine services, however how different stakeholders who use them experience these tools, particularly in low- and middle-income countries, is less explored. In the assisted model of India's eSanjeevani service, spoke health workers enter structured symptoms and hub clinicians review a symptom summary and an artificial intelligence (AI)-generated differential diagnosis. Objective: To describe role-specific experiences of eSanjeevani's CDSS functions and to estimate how acceptance coexists with specific reported difficulties within each role. Methods: We analyzed a program feedback web survey completed between 11 July and 29 July, 2025 by hub clinicians and spoke providers. Prespecified item profiles covered review of summaries and suggested diagnoses, the perceived relation of suggestions to the clinician's own diagnosis, ease of use and intention to use (hub), and symptom-capture utility, question quantity, question relevance, summary presentation and satisfaction (spoke). Within-role contrasts were reported as absolute differences in acceptance with Newcombe 95% CIs. Short open comments underwent content analysis. Results: The cohort comprised 190 hub and 662 spoke respondents from 13 and 15 states (21.4% and 19.3% of eligible providers). Among hub respondents, 76.8% regularly reviewed the symptom summary and 58.9% the suggested diagnoses; 72.6% judged suggestions matched (32.6%) or appropriate but different (40.0%), 7.9% mostly inappropriate, and 11.1% reported that suggestions were mostly not generated. Overall, 85.3% intended to keep using the tools. Affirmative intention was 98.4% when suggestions matched, 66.7% when they were mostly inappropriate (difference vs matched or appropriate -23.2 percentage points, 95% CI -48.5 to -4.0) and 61.9% when mostly absent (-25.7, 95% CI -47.1 to -7.3); technical issues, reported by 38.4%, made little difference (-2.8, 95% CI -14.1 to 7.3). Among spoke respondents, 63.4% rated question quantity appropriate, with miscalibration in both directions (15.6% too many; 16.6% too few); 89.9% found the questions relevant and 91.8% found the summary clear. Satisfaction was 75.3% and did not differ materially between those reporting too many questions and those reporting an appropriate number (-2.7, 95% CI -12.8 to 6.0). Comments located spoke difficulties in question burden and missing complaints, and hub difficulties in freezing and in the scope and availability of suggestions. Estimates were stable across cohort, ambiguity and state-exclusion sensitivity analyses. Conclusions: Acceptance of eSanjeevani's CDSS was high in both roles. For hub clinicians it tracked the perceived quality and availability of AI suggestions rather than technical friction; spoke workers accepted the question burden, but its two-directional miscalibration points toward complaint-specific, adaptive questioning. These provider reports do not measure diagnostic accuracy. They identify log-based and audit-based indicators that the next evaluation cycle should measure directly.

Introduction. Artificial intelligence is entering African primary care partly on the premise that populations underserved by clinicians will accept an algorithm in a clinician's place, a premise rarely tested where it matters. We measured how far Nigerian adults separate health from every other use of AI in their lives. Methods. Cross-sectional national survey of 11,013 adults across Nigeria's 36 states and Federal Capital Territory, July-October 2025. About 60% were recruited face to face, with translation where needed, so neither literacy nor English was required to take part. Each rated willingness to use AI in nine everyday situations on a 1-5 scale, one being health advice when no doctor was available. The primary outcome was a within-person difference: health minus the respondent's mean of the other eight. Associations were estimated by ordinary least squares with cluster-robust (CR2) standard errors by state. Results. Health finished last of the nine (mean 3.10), below promoting one's own political party (3.40). The within-person gap was -0.568 (95% CI -0.594 to -0.543; dz -0.417), widening to -0.904 among the 6,924 who distinguished between domains. Keeping a clinician in view did not relieve it: 22.9% would trust AI to help doctors diagnose against 25.5% who would use it to check their own symptoms. Among barriers respondents named, the gap was wider for language (beta -0.427, 95% CI -0.718 to -0.136) and unfriendly staff (-0.411, -0.616 to -0.207), narrower for missing equipment (+0.231, +0.040 to +0.423) and narrower among fluent English speakers (+0.386 per SD, +0.130 to +0.643). Conclusion. Nigerian adults accept AI across their lives and withhold it from their health, most firmly where care has been unaffordable, unintelligible or unkind. No configuration tested commanded majority willingness, including AI presented as assisting a clinician; willingness belongs alongside connectivity and data in deployment planning, not assumed.

Background: Cone-beam computed tomography (CBCT) frequently captures the maxillary sinuses incidentally, and reliable automated detection of sinus abnormality is clinically relevant. Unlike most vision-language benchmarks in medical imaging, which pair images with pre-existing, human-authored clinical reports, findings text can also be generated directly by a large language model from the image itself--raising the question of how much diagnostic value such AI-derived text carries, and whether that value depends on independent verification. Multimodal artificial intelligence (AI) benchmarks risk overstating performance if the provenance of each input--image, raw AI-generated text, or radiologist-verified text--is not clearly separated and reported. Methods: We used 300 mid-sagittal CBCT slices from the MMDental dataset. ChatGPT generated findings text and a provisional normal/abnormal label for every slice (majority vote, three independent readings from the image alone); primary classification performance was assessed on this full, unfiltered set (n=300). A radiologist then independently reviewed each case's image together with ChatGPT's description, producing their own diagnosis; three cases were excluded as insufficient, yielding 297 confirmed cases. On this subset, every model was retrained and re-evaluated under identical 10-fold cross-validation on both the provisional ChatGPT-only labels ("pre") and the radiologist-confirmed labels ("post"), isolating the effect of label provenance from image or architecture. Eight vision architectures, seven language classifiers, and five VLMs were evaluated throughout; three generative models performed exploratory note-drafting. Findings: Raw ChatGPT-generated text produced the highest performance of any modality or condition: language models reached near-ceiling AUC (0.992 to 1.000, n=300), exceeding every vision model (AUC 0.799 to 0.880) and every VLM image-only probe (AUC 0.63 to 0.69). On the 297-case pre/post analysis, this advantage depended heavily on label source: language and text-derived VLM performance fell substantially from ChatGPT-only to radiologist-confirmed labels (e.g. BERT-base AUC 0.999 to 0.837), while vision-model performance was stable or modestly improved (e.g. DenseNet-121 0.867 to 0.891). The radiologist reclassified 62 of 297 cases (21%) relative to ChatGPT's provisional read, and a meaningful proportion of raw ChatGPT text was clinically uninterpretable or unsupported by the imaging. Interpretation: As shown here for the first time, raw, image-derived AI-generated text yields the highest apparent classification performance in this benchmark, but this reflects the text's alignment with its own self-generated labels rather than verified diagnostic content, and a substantial share of that text is not clinically explainable. Radiologist-confirmed text and labels give a lower but trustworthy estimate of true performance, on which convolutional neural network (CNN) vision models remain a stable, comparatively inexpensive baseline. Multimodal dental AI should report performance separately by modality and label provenance rather than pooling headline metrics.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Objective. Triage of rheumatology outpatient referrals is a high-volume administrative task that consumes senior specialist time without advancing patient care. The human triage system is only moderately accurate and reproducible. We assessed whether contemporary large language models (LLMs) are able to perform well enough to support automating this task in practice. In addition, the effects of different prompting techniques on triage accuracy and cost was assessed to help identify to optimal approach. Methods. Twenty referral scenarios spanning the urgency spectrum, based on real referrals were created by a certified Australian rheumatologist. Four rheumatologists triaged all cases independently and blinded, to produce a consensus reference standard. Twenty-three LLMs each triaged every referral into one of five urgency categories, three times (1380 outputs per condition). The experiment was run with a simple prompt and repeated with a advanced prompt supplying explicit triage expectations and worked examples. Results. All 2760 attempts returned valid categories. Under the simple prompt, performance separated into distinct tiers, larger models were more accurate (Spearman rho=0.42; P=.047) and accuracy tracked cost. Advanced prompting minimised between-model variance in accuracy 5.3-fold (0.014 to 0.003; Levene P=.01), abolished the size-accuracy association (rho=-0.05; P=.83) and removed the accuracy-cost relationship. Leading models matched expert consensus on most cases, within or above the range reported for human triage. Under-triage errors persisted with some LLMs. Conclusion. Contemporary LLMs categorise rheumatology referral urgency as well or better than published human triage systems. Advanced LLM prompting methods substitute for the reasoning capability of larger models, suggesting that LLM performance on this task may not require the most expensive models. The tools to automate this administrative task appear to already exist. Strong candidate LLMs that might serve a production ready solution have been identified.

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.

Introduction: Early Childhood Caries (ECC) remains one of the most common chronic conditions in childhood, affecting over 600 million children globally. When untreated, it can lead to severe pain, abscesses, difficulty in chewing that contributes to malnutrition, impaired speech, disrupted sleep, low self-esteem, social withdrawal, and poor academic performance, all of which diminish a childs overall quality of life. Although largely preventable, ECC remains highly prevalent because of delayed preventive care and low parental awareness. Effective health education needs to be consistent, accessible, and available whenever parents need it. Although current evidence indicates that anticipatory guidance is a proven approach, its long-term reinforcement through a mobile app has not yet been tested. This study aims to develop and evaluate the effectiveness of a mobile health (mHealth) application delivering Sustained Anticipatory Guidance (SAG) for the prevention of ECC. Methods and analysis: A two-arm parallel hospital-based randomized controlled trial will be conducted at Sri Ramachandra Institute of Higher Education & Research, Chennai, India. The study will enrol 540 mother-infant dyads from the departments of Pediatric Medicine and the Centre for Early Childhood Caries Research (CECCRe). Eligible dyads with infants aged 6-12 months in the predentate stage will be randomized to the "Zero-Cavity" mHealth app (intervention) or standard care (control). Baseline and semi-annual data on oral hygiene, fluoride exposure, and feeding/diet will be collected. Calibrated dentists will perform quarterly clinical exams using ICDAS II criteria, recording cavitated and non-cavitated lesions. For children at high risk or with enamel defects/incipient lesions, clinical photographs will be taken to support individualized preventive guidance. The primary outcome will be the effectiveness of the app in preventing ECC, while the secondary outcomes will include ECC incidence and prevalence. Discussion: Despite being largely preventable, ECC remains a major public health challenge, sustained by limited preventive access and inconsistent counselling, especially in LMICs. This trial evaluates the Zero Cavity app, believed to bridge this gap by providing sustained, age-appropriate anticipatory guidance beyond clinic settings on oral development, hygiene, diet/feeding, and fluoride use. The behavioural theory-driven approach is expected to enhance cultural relevance, usability, and behaviour change, with optimism that process evaluation will illuminate engagement and implementation. It is believed that this large, adequately powered, parallel-group RCT with intention-to-treat analysis will offer, with optimism, a scalable, low-cost model that will expand the reach and consistency of continuous preventive care in diverse urban and peri-urban populations.

Language discordance can impede community-based research and health communication where trained interpreters are limited. Although multimodal artificial intelligence systems can provide real-time spoken translation, performance with under-resourced languages during spontaneous field interactions remains poorly characterized. We evaluated ChatGPT-4o during bidirectional English-Nepali voice translation in a community setting near Dhulikhel Hospital, Nepal. In this cross-sectional field study, 30 primarily Nepali-speaking adults were recruited by convenience sampling. ChatGPT-4o mediated conversations using standardized English questions and spontaneous Nepali responses. A bilingual Nepali-English reviewer assessed 485 translated utterances using a 3-point accuracy scale and an inductively developed framework for translation and conversational deviations. Of 485 translations, 282 (58.1%) received the highest accuracy rating, 134 (27.6%) a moderate rating, and 69 (14.2%) the lowest. Mean accuracy was higher for English-to-Nepali than Nepali-to-English translation (2.63 {+/-} 0.53 vs 2.23 {+/-} 0.86); 63 of 69 low-accuracy translations (91.3%) occurred in the Nepali-to-English direction. Among 329 deviation tags, the most frequent were distortion of intended meaning (17.1%), overly formal or unnatural phrasing (14.7%), omission (14.2%), and addition of content (11.5%). Some fluent outputs substantially altered meaning or introduced information not expressed by the speaker. ChatGPT-4o demonstrated potential for real-time English-Nepali communication but also produced errors that could alter interpretation of participant responses. Accuracy was lower and more variable for Nepali-to-English translation; however, translation direction was confounded with input type because Nepali inputs were spontaneous and English inputs standardized, limiting conclusions about directional performance. These findings support cautious use for low-stakes conversational exchange and human verification when errors could affect research validity, clinical decisions, or participant understanding. As multimodal AI evolves, performance should be reevaluated across languages, real-world conditions, and model versions, with bilingual oversight and community partnership remaining central to responsible use.

Purpose Prevention and early detection of osteoporosis remains a global challenge, more so in regions like the Philippines where screening barriers exist. Chest x-rays meanwhile are relatively inexpensive, and more frequently done, and therefore can be used for opportunistic screening. This study aimed to develop a deep learning model for osteoporosis detection from chest x-rays using DXA as the gold standard. Methods A convolutional neural network called Osteo-AI was developed using 406 pairs of chest x-rays and DXA scans of Filipino patients aged 50 and above. With data augmentation, the training set expanded to 6,300 pairs. Gradient-weighted class activation mapping technique was applied to localize and identify patterns and areas in the chest x-ray images correlating with osteoporosis. Results Training data consisted of 369 female patients and 37 males. Ages of the patients ranged from 50 to 89 with a mean age of 63 years old. Initial testing yielded promising results, with Osteo-AI achieving a diagnostic accuracy of 85.71%, easily outperforming a benchmark of 33.33% Conclusion Our findings suggest the potential of Osteo-AI to enhance osteoporosis screening accessibility, aiding in early intervention to prevent fragility fractures. Further research involving larger datasets is warranted to refine and optimize the model, potentially improving detection accuracy and expanding its utility in global healthcare settings.

Background: Digital mental health applications offer scalable pathways to expand mental healthcare access in low and middle-income countries, yet limited evidence exists on factors predicting willingness to use such applications in South Asia. This study examined associations between digital mental health literacy (DMHL), Technology Acceptance Model (TAM) constructs, mental health symptoms, and willingness to use smartphone-based mental health applications among adults in Bangladesh. Methods: Cross-sectional study conducted from February to June 2026 across urban (Dhaka), semi-urban (Chattogram), and rural (Sylhet) divisions of Bangladesh. Adults aged 18-60 years attending primary care facilities with smartphone access (n = 400) completed a face-to-face interview in Bangla assessing sociodemographic, depression (PHQ-9), anxiety (GAD-7), DMHL (6-item scale, = .762), TAM perceived usefulness (5-item, = .937) and ease of use (5-item, = .924), and willingness to use mental health applications (5-item scale, = .946). Hierarchical binary logistic regression examined associations with willingness (dichotomized at mean [&ge;]3.5). Results: Greater DMHL was associated with higher willingness (adjusted odds ratio [aOR] = 1.24, 95% CI: 1.11-1.38). Both perceived usefulness (aOR = 1.29, 95% CI: 1.18-1.41) and perceived ease of use (aOR = 1.17, 95% CI: 1.07-1.28) independently predicted willingness. Anxiety symptoms were positively associated (aOR = 1.08 per 1-point increase, 95% CI: 1.01-1.15), whereas depression symptoms, age, and sex were not independently significant after adjustment for other variables. Conclusions: Digital literacy, perceived utility of technology, and symptom-specific phenomenology but not demographics alone are associated with willingness to use digital mental health applications. Building DMHL, optimizing the usefulness and usability of the app, and overcoming implementation barriers should all be given priority.

Background: Kano State has one of the highest concentrations of zero-dose (ZD) children in Nigeria, challenging equitable immunization coverage. Digital engagement tools meant to strengthen timely data to address such gaps often underperform due to limited adaptability to user needs. Learning-by-doing (LxD), which brings together human-centered design and adaptive management, offers more context-responsive solutions. Intervention and Implementation Approach: MCH-Track, a digital platform for tracking and referral for childhood vaccinations was used by 56 female community mobilizers in two local government areas (LGAs) out of the 44 LGAs in Kano State, Nigeria. We conducted a program case study from: 1) August 2024-April 2025 (initial implementation); and 2) adapted implementation (May 2025-November 2025), where LxD was incorporated to address emergent challenges. LxD included structured shadowing, monthly interviews with users, after-action reviews, and change log to track adaptations alongside routine services. Performance was monitored using four indicators: feedback actioned, response time-to-user feedback, ease-of-use score, and mobilizer performance in tracking ZD children. Findings: After incorporating LxD, 17 issues were identified and led to 12 prioritized adaptations. All logged issues received responses, with median response time declining to approximately two weeks, while community mobilizers reported higher usability of the digital platform (e.g., mean ease-of-use score of 4.1 out of 5). Enhanced root-cause analysis enabled trend tracking and clearer escalation pathways for systemic issues like micro-stockouts at immunization points. Empathy tool records revealed mobilizer struggles with platform. This was immediately logged, prompting user experience redesign into a seamless, single-step process from registration-to-referral. During the LxD implementation period, an average of 33 ZD children per month were identified via MCH-Track, a substantial increase from the pre-LxD period (an average of 11 ZD children identified per month). Average referral-to-access rates also improved during the LxD period, rising from 44% of identified and referred ZD children accessing immunization services pre-LxD to 70% after incorporating LxD practices. Conclusion: Embedding LxD within MCH-Track strengthened co-creation, streamlined workflows, and improved community mobilizer performance, demonstrating the value of adaptive management in reaching ZD children.

Abstract Background: A critical challenge for large-scale multi-country population health studies is the ability to collect consistent data across many sites and time periods and ensure that the data collected are valid and comparable. The use of mobile digital devices coupled with data collection platforms can address this challenge. We developed a fit-for-purpose digital data collection platform for the South Asia Biobank study. Objective: To describe the process by which a digital platform was designed, developed and deployed across four countries in South Asia; to demonstrate how the platform enabled field research teams located across these countries to collect non-communicable diseases epidemiological data consistently. Methods: A user-centred design approach was employed for the development of the digital platform to address the dynamic nature of study requirements. This approach uses 5-step iterative loops that, with each iteration, produce a usable prototype version of the software that was then tested by potential users of the platform. Qualitative interviews and quantitative system usability assessments were conducted, and findings utilised as input for the start of the next iterative loop. The process was completed when a working version of the software was developed for the use in the study. Results: Over the course of four iterative loops, the platform was progressively built and tested to ensure its functionality met the requirements of the study. Detailed feedback was collected from key stakeholders and incorporated into the platform with each new version of the applications. The platform leverages advances in mobile and medical device technology along with software integration capabilities to enable efficient and consistent data collection, along with the ability to review data quality and make improvements to the data collection process in real-time. The successful deployment of the data platform has enabled collection of comprehensive baseline data from 205,536 participants in four South Asian countries. Conclusions: Using user-centred design principles, it is possible to develop and deploy a comprehensive digital surveillance data management platform that allows consistent and high-quality data collection in population health studies in remote settings. To the best of our knowledge, this is the first platform that enables the integrated capture of health assessment data from a wide variety of medical equipment that is tailored for deployment in a range of LMIC settings.

Caesarean section in Bangladesh reached 51.8% of deliveries in 2025, and elective caesarean, meaning caesarean before labour began, reached 31.6%. Risk models built on national household surveys are increasingly proposed for pointing audit toward places where scheduled surgery is outrunning clinical need, but they are usually validated in ways that flatter them. Using the 2025 Bangladesh Multiple Indicator Cluster Survey, we developed four models on 9,538 women (logistic regression, elastic net, random forest, gradient boosting) and ran the same procedure under three validation designs: random five-fold cross-validation; five-fold cross-validation grouped by sampling cluster; and leave-one-division-out cross-validation. We also tested transfer between the 2019 and 2025 rounds and audited subgroup calibration. No model improved on logistic regression by a margin worth acting on: the area under the receiver operating characteristic curve ranged from 0.724 to 0.736 under cluster-grouped validation, a spread of 0.012. Validation design mattered far more than the algorithm. Grouping folds by sampling cluster changed discrimination by at most 0.0004, this survey contributing a median of 3 eligible women per enumeration area. Withholding a whole division cost 0.044 to 0.060, more than 100 times as much, and still cost 0.033 to 0.056 after the strongest predictor, an outcome-derived district rate, was removed from every model. A model fitted to 2019 data lost 0.083 when applied to 2025, and the two rounds agreed only moderately on which predictors mattered (Spearman rank correlation 0.61). Calibration held in every wealth quintile, both residence categories and seven of eight divisions; Sylhet was the exception. Elective caesarean is predictable from routine survey items, but that predictability is local. Cross-validation, including cluster-aware cross-validation, does not measure what a model would do in a district it has never seen; a geographic holdout is the cheapest design that does.

Background: Antimicrobial resistance (AMR) is a growing global health threat, and India's high antibiotic consumption makes public knowledge, attitudes, and practices (KAP) a critical determinant of stewardship outcomes. Existing Indian KAP evidence is drawn from small, localised cohorts, leaving a gap in nationwide, digitally-sourced data. This study aimed to describe antibiotic-related knowledge, attitudes and self-reported practices among users of a digital healthcare platform in India, examine demographic and regional variation, and identify potential areas for future stewardship interventions. Methods: A cross-sectional survey was conducted among adult users of the Tata 1mg platform (December 2025-February 2026), with participants invited via an in-app banner. Knowledge and attitude were each scored on a 0-4 scale (4 items); practice was scored as a percentage of applicable items answered favourably, with scoring adjusted for branching logic based on antibiotic-access route. Of 10,726 respondents who started the survey, 4,883 completed it (45.5%). City of residence was mapped to one of six regions (India's five statutory Zonal Council zones plus a North-Eastern region) for 4,187 respondents. Associations with demographic characteristics were assessed using multivariable linear regression, and regional differences were assessed using one-way ANOVA. A parallel survey of 256 physicians was conducted to enable patient-provider comparison. Results: Mean scores were: knowledge 2.28 (SD 1.19), attitude 0.91 (SD 1.14), practice 44.9% (SD 33.4); attitude, not knowledge, was the weakest domain. Older age was associated with higher scores across all domains (practice: B=12.52, p<0.001 for age [&ge;]65 vs. 18-34); city tier was independently associated with lower knowledge and attitude (Tier 3 vs. Tier 1: p=0.010 and p<0.001, respectively). Regional differences were non-significant for knowledge and practice but significant for attitude (F=2.56, p=0.026, ^2=0.003), driven by lower scores in the North-Eastern region. In the parallel physician survey, 55.5% of respondents estimated that more than half of their patients self-medicated before consultation. Conclusion: In this platform-based sample, attitudes were the weakest domain, not knowledge, and 42.0% said their last antibiotic did not come on a doctor's recommendation. The findings may inform digital stewardship touchpoints, such as point-of-purchase guidance and prescription verification, in line with India's National Action Plan on Antimicrobial Resistance 2.0. As a cross-sectional survey of platform users, it cannot establish the relative contributions of knowledge, beliefs and access pathways to antibiotic use.

Background: Patient-generated online reviews contain detailed accounts of physician communication that remain systematically underused in health services research. Prior computational analyses have either applied service quality frameworks without grounding in clinical communication theory, or have been geographically and specialty-restricted. Objective: To examine which physician communication dimensions predict patient recommendation, switching intent, and retention across specialties and cities in India. Methods: We analyzed 62,319 patient reviews from the Practo platform (10 cities, 8 specialties) coded against established clinical communication frameworks, the Kalamazoo Consensus Statement, Calgary-Cambridge Guide, and SPIKES protocol, adapted into a 12-dimension codebook. Expert confirmatory review on 200 stratified reviews yielded 98.5% agreement between reviewer and model labels across 11 communication dimensions. Binary recommendation was designated the primary outcome; recommendation intensity is reported as secondary. Logistic regression with 1,000 bootstrap iterations examined associations between communication dimensions and recommendation and switching intent, adjusting for treatment outcome, cost, wait time, and specialty. Results: Reassurance (OR 2.07, 95% CI 1.86-2.42) and empathy (OR 1.89, 95% CI 1.79-2.00) were the strongest positive predictors of recommendation. Excessive directiveness (OR 0.62, 95% CI 0.60-0.64) and rushedness (OR 0.65, 95% CI 0.64-0.67) were the dominant negative predictors; directiveness was present in 33.3% of switching-intent reviews. Cost asymmetry was pronounced: cost concerns reduced recommendation rates by 54.2 percentage points; positive cost comments increased recommendation by 6.2 points. Specialty variance in recommendation intensity exceeded city variance (ratio 2.63). Conclusions: Excessive directiveness and rushedness were the communication behaviors most strongly associated with adverse behavioral outcomes in this corpus; reassurance and empathy are the strongest drivers of recommendation. These effects are modified by specialty context in ways that align with Kalamazoo, Calgary-Cambridge, and SPIKES theoretical predictions. The specialty-stratified findings provide a direct empirical basis for competency-based communication curricula in undergraduate and postgraduate medical education.

Objective To evaluate the information quality, reliability, readability, and content deficits of ChatGPT, DeepSeek, and Doubao in answering frequently asked questions about bee stings. Methods Twenty-five high-frequency patient questions were selected based on Google Trends, domestic health platforms, and allergy guidelines, and submitted to the three LLMs, yielding 75 responses. Two emergency physicians independently assessed quality using GQS, mDISCERN, QAMAI, EQIP, and PEMAT-U/A. Readability was evaluated using six indices (ARI, FRES, GFI, FKGL, CLI, SMOG). Content deficits were classified qualitatively. Twenty emergency department patients evaluated the comprehensibility and credibility of AI responses. Results Significant differences were found among the three models on all quality metrics (all P < 0.01). DeepSeek achieved the highest mDISCERN score (median 21.00); ChatGPT led on QAMAI (25.00) and EQIP (75.00%); Doubao ranked lowest on all quality indicators but highest on PEMAT-U (73.00%). No model met the sixth-grade reading level standard. Eleven content deficits were identified: outdated medical advice (45.45%), omissions (36.36%), and hallucinations (18.18%). Doubao received the highest patient preference (45.0%). Conclusion The three LLMs exhibited distinct strengths: DeepSeek in source reliability, ChatGPT in overall quality, and Doubao in understandability. All models had excessively high readability levels, and outdated medical advice was the predominant content deficit. Objective To evaluate the information quality, reliability, readability, and content deficits of ChatGPT, DeepSeek, and Doubao in answering frequently asked questions about bee stings. Methods Twenty-five high-frequency patient questions were selected based on Google Trends, domestic health platforms, and allergy guidelines, and submitted to the three LLMs, yielding 75 responses. Two emergency physicians independently assessed quality using GQS, mDISCERN, QAMAI, EQIP, and PEMAT-U/A. Readability was evaluated using six indices (ARI, FRES, GFI, FKGL, CLI, SMOG). Content deficits were classified qualitatively. Twenty emergency department patients evaluated the comprehensibility and credibility of AI responses. Results Significant differences were found among the three models on all quality metrics (all P < 0.01). DeepSeek achieved the highest mDISCERN score (median 21.00); ChatGPT led on QAMAI (25.00) and EQIP (75.00%); Doubao ranked lowest on all quality indicators but highest on PEMAT-U (73.00%). No model met the sixth-grade reading level standard. Eleven content deficits were identified: outdated medical advice (45.45%), omissions (36.36%), and hallucinations (18.18%). Doubao received the highest patient preference (45.0%). Conclusion The three LLMs exhibited distinct strengths: DeepSeek in source reliability, ChatGPT in overall quality, and Doubao in understandability. All models had excessively high readability levels, and outdated medical advice was the predominant content deficit.