Routine urine screening is a fundamental diagnostic tool in clinical practice. It provides critical insights into an individual’s health status and enables the early detection of a wide range of diseases. Urinalysis plays a key role in identifying early signs of kidney disease, diabetes mellitus, urinary tract infections (UTIs), and various metabolic disorders [1–5]. It can also yield ancillary information related to hydration status, dietary intake, medication use, and lifestyle habits. Alterations in urine characteristics such as color, turbidity, or odor may indicate underlying pathology, although these features may also reflect benign influences such as diet, medications, physical activity, and hydration levels [6–10].

Urinalysis typically consists of three components: i) Physical examination, which evaluates color, clarity, volume, and specific gravity; ii) Chemical analysis, which tests for substances such as protein, glucose, ketones, blood, bilirubin, and pH; and iii) Microscopic examination, which identifies cells, casts, crystals, and other particulates [11]. Together, these analyses provide a noninvasive yet comprehensive assessment of renal, metabolic, and infectious conditions.

Despite its diagnostic value, routine urine screening faces notable barriers, particularly in resource-limited settings. Manual processing, reliance on centralized laboratories, the requirement for trained personnel, and delays in result reporting can reduce accessibility and hinder timely clinical decision-making. These limitations have increased interest in the integration of digital health technologies, particularly the Internet of Things (IoT), into diagnostic workflows.

IoT-enabled systems support real-time data collection, automated analysis, and remote transmission of results. Through the integration of smart sensors and cloud-based platforms, these systems can detect key urinary biomarkers and support rapid interpretation through AI and ML algorithms. Such innovations have the potential to decentralize urine diagnostics, reduce diagnostic delays, and improve diagnostic accuracy, especially in settings with limited infrastructure [12–15].

This systematic review aims to synthesize current evidence on the role and impact of routine urine screening in modern healthcare, with particular emphasis on digital transformation and IoT integration. The review explores the clinical utility of urine screening, evaluates technological advancements in diagnostic methods, examines implementation challenges, and highlights the potential of IoT-based systems to improve large-scale public health outcomes.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparent and reproducible reporting.

A total of 265 articles were initially identified. Of these, 241 were obtained from database searches, and 24 were identified from other sources, including citation tracking of relevant studies. After duplicate removal and eligibility screening, 65 studies met the inclusion criteria and were selected for analysis, as illustrated in the PRISMA flow diagram (Figure 1). Notably, 14 of the 21 articles identified through citation searching were included in the final dataset. Of the 99 reports initially sought for retrieval from the database search, 28 full-text articles were not accessible through databases at the time of the study. Additional attempts were made through institutional library access and publisher platforms to access these 28 full-text articles, but they could not be retrieved due to access restrictions at the time of the review. While this may introduce potential retrieval bias, the review synthesis was conducted using the 51 available full-text studies, supplemented by 14 additional studies obtained through alternative methods, encompassing a range of device types, methodologies, and populations.

This systematic review addresses a rapidly evolving domain at the intersection of medical informatics, health service delivery, and technological innovation. Its primary aim is to improve healthcare outcomes through the use of digital technologies, including the IoT, mobile applications, and cloud computing, to support efficient care delivery at local, regional, and global levels [88]. Although digital health tools have been under development since the early 2000s, their widespread adoption, particularly within primary healthcare (PHC), accelerated markedly during the COVID-19 pandemic. This period highlighted their critical role in improving healthcare access, continuity, and system resilience [89, 90].

Evidence drawn from 65 studies demonstrates clear momentum toward automation, decentralization, and digital transformation in urinalysis. At the same time, the findings reveal substantial variability in engineering rigor, cybersecurity implementation, and clinical validation. Synthesis of the available evidence indicates that IoT-based and digitally transformed urine testing systems extend beyond improvements in convenience. These technologies fundamentally reshape the diagnostic paradigm through multimodal sensing, real-time analytics, and seamless integration with broader digital health ecosystems, including telemedicine platforms, electronic health records (EHRs), and HDT frameworks ultimately increasing the patient engagement [91].

The prominent trend is the transition from episodic, laboratory-based urinalysis to decentralized systems capable of generating high-frequency or continuous health data. Smart urine strips and smartphone-based analyzers, once viewed primarily as consumer-grade tools, have evolved into technically robust systems that leverage algorithmic color correction, ML-based interpretation, and encrypted wireless data transmission to deliver clinically relevant insights. Despite these advances, early-generation systems continue to face limitations related to environmental variability, reagent instability, and dependence on user technique. In contrast, microfluidic and acoustofluidic point-of-care platforms represent a more substantial departure from conventional dipstick testing. Their capacity for automated sample metering, multiplexed biochemical assays, and high-resolution microscopic analysis elevates their function from basic screening to tightly controlled analytical evaluation. These characteristics reduce operator error, enhance analytic reliability, and enable integration with mobile imaging and computational modules, which positions these platforms as credible alternatives to centralized laboratory urinalysis.

Across device categories, the engineering architectures that support IoT-enabled urine diagnostics are progressing toward more advanced computational models. Early systems relied primarily on cloud-based analytics, with transmission of raw or minimally processed data for remote interpretation. Although cloud platforms provide scalability and facilitate integration with clinical dashboards, they also introduce latency, bandwidth constraints, and dependence on network reliability [92, 93]. Several studies address these challenges through the incorporation of edge computing, which allows on-device preprocessing, feature extraction, and, in some cases, complete model inference. More advanced systems leverage dynamic computation offloading, accuracy-aware optimization, and multiuser scheduling strategies to maintain diagnostic timeliness under constrained network conditions [94]. Collectively, these findings emphasize the importance of aligning computational architecture with clinical context, particularly for time-sensitive conditions such as UTIs and acute kidney injury.

Recent advances in mobile edge computing provide valuable insights into optimizing computational workflows for IoT-enabled urine diagnostics. Studies on multi-user computation offloading and transmission scheduling for delay-sensitive applications demonstrate how latency constraints can be managed through dynamic task partitioning between edge nodes and cloud servers [95, 96]. Such frameworks are particularly relevant for time-critical urinalysis applications, including acute infection detection and renal injury screening, where diagnostic delays may have direct clinical consequences.

Accuracy-aware online optimization strategies, such as those proposed for dynamic HDT deployment at the edge, further highlight the importance of balancing inference precision with computational and energy constraints. These approaches enable adaptive decision-making regarding where analytic tasks should be executed, allowing systems to prioritize edge-based inference when network conditions degrade while preserving cloud-based model refinement during stable connectivity.

Game-theoretic and ML–based computation offloading mechanisms proposed for cloudlet-based mobile computing also offer transferable design principles for distributed urinalysis platforms [97]. In large-scale deployments such as community screening or smart toilet networks, such strategies can support fair resource allocation, scalability, and robust performance across heterogeneous user populations.

Recent surveys on HDT architectures in personalized healthcare emphasize the role of continuous physiological data streams in enabling individualized health modeling [98]. IoT-enabled urinalysis contributes uniquely to HDT frameworks by providing noninvasive, biochemical, and longitudinal indicators of renal, metabolic, and hydration status. Unlike episodic laboratory testing, continuous or high-frequency urine data enable dynamic updates to digital twin representations, supporting early anomaly detection and personalized intervention strategies.

Generative AI–driven HDT models further enhance this capability by simulating physiological trajectories and predicting disease progression under varying behavioral or therapeutic conditions [99]. Additionally, privacy-preserving approaches such as differentially private federated multi-task learning offer promising solutions for safeguarding sensitive urine-derived data while enabling collaborative model training across distributed healthcare environments [100].

Additionally, the growing use of generative AI models, reconfigurable intelligent surfaces (RIS) to optimize network quality of experience, and mobile AI-generated content (AIGC) for personalized guidance reflects a maturation of the field beyond isolated diagnostic tools [101, 102]. Collectively, these studies position IoT-enabled urinalysis as a foundational data modality within a broader computational healthcare system.

Despite these promising developments, several important gaps remain. Most notably, there is a persistent lack of large-scale, clinically validated trials that evaluate diagnostic accuracy, reproducibility, and device performance across diverse populations and real-world settings. Many studies report encouraging sensitivity and specificity; however, these metrics often originate from small, controlled laboratory evaluations rather than robust cohort-based testing. Issues such as calibration drift, reagent shelf life, optical variability, and sensor degradation received limited attention, which raises concerns regarding long-term reliability.

Taken together, the findings of this review indicate that IoT-enabled urinalysis is progressing toward becoming an integral component of digitally transformed healthcare. As sensing technologies mature and computational architectures advance, future systems will likely evolve into hybrid platforms that combine microfluidic precision, edge-based intelligence, multimodal sensor fusion, and seamless integration with digital twin ecosystems. Despite these advances, the clinical value of such devices remains dependent on user adoption, usability, and accessibility. Smart toilets, for example, provide exceptional diagnostic continuity but may be impractical in low-resource settings. In contrast, low-cost smartphone-based systems offer broad accessibility but may lack biomarker diversity or analytical precision. Bridging this divide requires translational research that addresses engineering performance alongside workflow integration, clinician acceptance, and cost-effectiveness. Furthermore, although several studies describe theoretical or proposed system architectures, few provide implementation-level detail related to cybersecurity, interoperability, or regulatory compliance. Only a limited number of studies specify encryption standards, blockchain-based data integrity mechanisms, or federated learning frameworks designed to protect sensitive biosensor-derived health data. As these technologies become increasingly embedded within clinical systems, the development of secure architectures that comply with HIPAA and GDPR requirements remains essential.

Addressing current gaps in security, validation, and standardization [103–105] will be critical to realizing this vision.

The digital divide also remains a significant barrier to widespread adoption. Limited digital literacy, unreliable internet connectivity, and restricted access to smartphones or other smart devices can impede effective use of digital health technologies, particularly within marginalized communities. This tension between technological utility and individual rights or knowledge varies by disease type, local legal frameworks, and cultural norms. Responsible navigation of these factors is necessary to ensure equitable and ethical deployment.

Overall, routine urine screening remains an essential, noninvasive diagnostic tool for the early detection of conditions such as kidney disease, diabetes, infections, and certain cancers. Innovations such as EzeFind, smart toilets, and related platforms demonstrate how affordable, user-friendly, IoT-based solutions can address long-standing limitations of traditional urinalysis through real-time diagnostics, remote monitoring, and seamless clinical data exchange. These advances expand diagnostic access, empower patients, strengthen preventive care, enhance health system responsiveness, and improve outcomes at both individual and population levels. IoT-enabled urine screening systems therefore, represent a promising frontier in decentralized, patient-centered healthcare. By bridging gaps in early diagnosis, strengthening surveillance, and supporting equitable access, particularly in underserved communities, such technologies have the potential to contribute meaningfully to more resilient, efficient, and inclusive health systems.