Precision diagnostics is shifting from episodic testing to continuous inference. Precision diagnostics is increasingly shifting from a phased testing model to a continuous inference model[
1]. Traditional clinical diagnosis typically uses single-point measurements interpreted through reference ranges, which works well for many established testing methods. However, it may miss early or transient pathophysiological changes[
2–
3]. Currently, wearable and implantable biosensors are changing this by enabling continuous measurements, allowing clinicians to examine trends, variability, and deviations from an individual’s baseline, rather than relying on a single reading[
4]. For example, wearable platforms now routinely capture signals such as electrocardiogram (ECG)/photoplethysmography (PPG)-derived cardiac features[
5], activity and sleep indicators[
6], body temperature[
7], and continuous blood glucose trends[
8]. Implantable and minimally invasive devices extend monitoring to internal environments and hard-to-sample compartments (e.g., interstitial fluid [ISF]), enabling more stable access to specific biomarkers[
9–
10]. At the same time, novel biosensor architectures, including flexible and skin-conformal electronics, microfluidics, and nanomaterial-enhanced interfaces, are expanding the range and sensitivity of detectable analytes in sweat, saliva, tears, and wound exudate[
11–
13]. This shift is changing the meaning of “diagnosis.” Continuous biosensing technologies can infer from a trajectory whether an individual has significantly deviated from their baseline level, whether this deviation is persistent, and whether it conforms to a clinically actionable pattern. Its advantage lies not only in providing more data but also in using longitudinal signals to make more inferences to assess risk, disease subtype, stage, trajectory, or impending deterioration, thereby supporting early intervention and more effective treatment plans.
Why Artificial Intelligence (AI) is structurally necessary for biosensor-centered precision diagnostics. Continuous sensing introduces interpretive burdens. Wearable and implantable devices generate high-frequency, multimodal data streams that are noisy, context-dependent, and susceptible to artifacts and drift[
14]. In practical applications, biosensor signals can be distorted by motion artifacts, differences in skin contact, drift and calibration errors, environmental conditions, and matrix effects in complex biofluids, e.g., sweat contamination[
15], evaporation[
16], saliva biofouling[
17]; enzyme degradation in certain electrochemical designs[
18]. These realities make it difficult to translate measurement data streams into reliable clinical decisions using only static thresholds or manually defined rules. Complex causality makes output analysis “extremely difficult,” motivating the integration of pattern analysis and classification algorithms to bridge the gap between data acquisition and analysis. The AI-biosensor framework comprises information collection by biosensors, signal conversion through transduction, and AI-data processing that spans interface functions, classification, modeling/analysis, and decision-making[
19]. This architecture naturally maps into clinical workflows, where raw sensor signals must be filtered, context-dependent, and interpreted to produce actionable outputs.
What must work before AI can help? The enabling technologies underlying AI-biosensors are not merely supportive details. They often determine the effectiveness of AI models outside of controlled conditions. Jin et al. highlight flexible bioelectronics as foundational to wearables, identifying commonly used substrates such as polyimide (PI) and polyethylene terephthalate (PET), as well as stretchable platforms such as polydimethylsiloxane (PDMS)[
20]. They emphasize that smart polymers, nanomaterials, and hydrogels can improve sensing interfaces and signal transduction, but they also point to persistent engineering constraints, including miniaturization, power consumption, cost, and sensor array crosstalk. Additionally, the choice of biofluid and sampling physics strongly shapes both sensor performance and the generalizability of machine learning. For example, sweat sensors face challenges such as low sweat secretion rates, evaporation, skin contamination, uncertainties in the correlation between sweat and blood, low analyte concentrations, and ongoing calibration and stability challenges[
21–
22]. Saliva-based wearables can suffer from biofouling due to proteins and food or drink interference[
23–
24]. Tear-based contact lens sensors must contend with power supply issues, repeatability, practical access to tear fluid, and the risk of enzyme denaturation due to sterilization[
25–
26]. ISF sensors, often based on microneedle platforms, can experience electrode material loss, enzyme degradation, and biocompatibility limitations[
27]. Implantable blood biosensors are susceptible to foreign body reactions, leading to a decline in function over time[
28]. These mechanisms are crucial because they cause changes in distribution, causing AI models trained in one environment to fail in another, unless their robustness is explicitly designed and validated.
AI-empowered biosensor-centered precision diagnostics. There are two major practical uses of AI in biosensor-centered precision diagnostics (Figure 1). First, AI reduces data volume prior to transmission to support low-power operation and scalable connectivity[
29–
30]. Instead of continuously transmitting raw waveforms, models perform feature extraction, temporal summarization, compression, and event-triggered reporting, transmitting only clinically meaningful segments or compact representations. This approach reduces bandwidth and energy consumption while preserving information relevant to monitoring and decision-making. Second, AI improves data quality and interpretability by enhancing data consistency and reliability under noisy conditions[
31–
32]. In practice, machine learning often acts as a digital signal-conditioning and calibration layer, enabling denoising, artifact suppression, drift correction, normalization across devices or users, and anomaly detection. These steps make sensor-derived variables more comparable longitudinally and across individuals, thereby strengthening clinical interpretability. Methodologically, both supervised and unsupervised learning are routinely applicable because biosensing tasks span calibration, state estimation, anomaly detection, and outcome prediction. Representative algorithms include support vector machines (SVM) for robust classification/regression boundaries[
33]; principal component analysis (PCA) for dimensionality reduction, decorrelation, and compact feature construction[
34]; hierarchical cluster analysis (HCA) for structure discovery and sample grouping[
35]; artificial neural networks (ANN
) and convolutional neural networks (CNN) for nonlinear mapping and high-dimensional pattern extraction (including time-series and image class readouts)[
36]; and decision trees for fast, rule-like decision structures that can be comparatively interpretable and deployable on constrained hardware[
37]. The choice of algorithm should depend on task requirements, such as noisy environments, label quality, computational constraints, and interpretability requirements, rather than just model complexity. In general, the importance of AI for signal cleaning, fusion, and calibration is often as significant as its importance for “diagnosis,” as clinical value depends on converting sensor outputs into reliable clinical signals.
Translational applications remain a major obstacle to the routine clinical application of AI-empowered biosensors. Many successful AI-powered biosensor cases remain confined to controlled research environments, with few systems achieving the robustness and scalability required for real-world healthcare. The lack of high-quality datasets, the variability caused by biosensor fabrication and environmental changes, data gaps, and limited adaptability are all major limiting factors that may lead to bias and overfitting. The underlying mechanism of these risks lies in the recorded degradation pathways in biosensing systems, such as sampling variability and baseline drift, which accumulate over time. Biocontamination, sterilization-related changes, and foreign-body reactions can further impair sensor interfaces and gradually degrade signal fidelity[
20]. Therefore, many failures will occur not because an algorithm is inherently flawed, but because the training distribution does not match the deployment distribution and because stability constraints were not addressed end-to-end. Additionally, continuous monitoring generates sensitive health data that requires encryption, secure storage, access controls, transparent policies, and explicit consent[
38]. Smartphone-based platforms exacerbate privacy and security concerns because personal information is concentrated on a single device, and the quality and security of the app ecosystem vary greatly[
39]. For instance, federated learning allows models to be trained locally on resource-constrained edge devices (e.g., smartwatches or smartphones) without transferring raw, sensitive health data to centralized servers[
40–
41]. Coupled with on-device differential privacy, which injects controlled noise into model updates, these architectures safeguard patient identities while maintaining population-level diagnostic accuracy. These limitations must be considered early on, as they impact model update strategies, data governance, and post-market surveillance.
Overcoming non-technical barriers in clinical adoption. Beyond technical and data-centric hurdles, successful clinical translation must overcome significant non-technical barriers, such as workflow integration. AI-biosensor systems must seamlessly interface with existing Electronic Health Records (EHRs) and provide actionable insights without increasing cognitive load or causing alert fatigue for healthcare providers. Furthermore, clinician trust hinges on model explainability. Black-box algorithms often face skepticism in medical settings, necessitating the adoption of Explainable AI (XAI, e.g., SHapley Additive exPlanations [SHAP] and Local Interpretable Model-agnostic Explanations [LIME]) to transparently justify diagnostic inferences[
42–
43]. Regulatory classification also presents a challenge, as adaptive AI algorithms blur the lines of traditional Software as a Medical Device (SaMD) frameworks, requiring novel continuous-monitoring approval pathways from agencies like the Food and Drug Administration (FDA)[
44]. Finally, the widespread adoption of these technologies relies heavily on the establishment of clear reimbursement models, ensuring that continuous remote monitoring and AI-driven diagnostics are financially viable for both patients and healthcare institutions[
45].
In conclusion, although the application of AI to precision clinical diagnostics is still in its early stages, AI-enhanced wearable biosensors lay a solid foundation for precision clinical diagnosis because they can perform continuous measurements and extract actionable information from complex sensor data streams by combining computational inference. The most reliable development path depends not on isolated accuracy metrics but on system reliability, representative data, validation commensurate with deployment, safe and ethical governance, and equitable access. If these requirements are met, AI biosensors can shift diagnosis towards earlier detection, personalized monitoring, and more timely care in clinical and everyday life.
The Author(s) 2026. This article is published by Higher Education Press at journal.hep.com.cn.
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