Multimodal health monitoring and theranostics based on functionalized hydrogels and artificial intelligence
Clinical Snapshot
PICO Framework
| P — Population | Not applicable in the traditional clinical sense; the review addresses preclinical and early translational research contexts involving humans and animal models where functionalized hydrogel-based wearable/implantable biosensors and theranostic systems are being developed |
| I — Intervention | Functionalized hydrogels (conductive, self-healing, stimuli-responsive, and hierarchically porous variants) integrated with data-processing frameworks for multimodal physiological and biochemical signal acquisition, combined with theranostic (diagnosis plus therapy) capabilities |
| C — Comparator | No direct comparator; the review contrasts hydrogel-based systems with conventional rigid biosensors and non-integrated diagnostic or therapeutic platforms, and evaluates AI-augmented versus non-AI data processing approaches |
| O — Outcomes | Material performance metrics (mechanical durability, conductivity, biocompatibility, sensitivity, selectivity); signal processing fidelity (noise reduction, baseline drift correction, feature extraction); diagnostic accuracy; therapeutic efficacy in integrated theranostic applications; translational readiness |
Bottom Line
This narrative review from Shanghai Jiao Tong University surveys the intersection of functionalized hydrogels and data-driven analytical methods for wearable and implantable multimodal health monitoring and theranostics. The authors identify genuine material science challenges — mechanical fatigue under cyclic loading, conductivity degradation, and the sensitivity-biocompatibility trade-off — and propose that computational optimisation approaches may accelerate material design and signal processing. While the thematic scope is clinically relevant and the field is scientifically promising, this review carries significant methodological limitations. The absence of a systematic search strategy, formal quality appraisal of primary studies, quantitative synthesis, and patient-centred outcome data substantially limits its evidentiary value. The overwhelming majority of cited work is preclinical. No human clinical trial data demonstrating diagnostic accuracy, therapeutic efficacy, or safety in real-world conditions is synthesised. For Australian clinicians, no actionable practice change is warranted. This review is best interpreted as a horizon-scanning document for researchers and biomedical engineers, not as a basis for clinical decision-making. The field warrants continued investment in rigorous translational research, including standardised benchmarking, first-in-human studies, and health economic modelling before clinical integration can be responsibly considered.
Key Findings
P Value: Not reported — narrative review with no original statistical analysis
Effect Size: Not applicable — no original data generated; individual primary studies cited within the review report variable performance metrics (e.g., gauge factors, detection limits, therapeutic efficacy) without pooled synthesis
Primary Outcome: Narrative synthesis of material design strategies for functionalized hydrogels and data-driven (machine learning/deep learning) signal processing approaches for multimodal health monitoring and theranostic applications
Nnt Or Sensitivity: Not calculable from this review; individual biosensor studies cited may report sensitivity and specificity values, but these are not systematically extracted or pooled
Confidence Interval: Not reported — no statistical synthesis performed
Clinical Application
Clinical feasibility remains low in the near term. Key barriers include: absence of large-scale human clinical validation; unresolved long-term biocompatibility and mechanical durability in dynamic in vivo environments; lack of standardised manufacturing and quality control processes; and undefined regulatory approval pathways. Proof-of-concept demonstrations in controlled laboratory or small-animal settings do not constitute clinical readiness. No TGA-approved hydrogel-based multimodal theranostic devices of this class are currently listed on the Australian Register of Therapeutic Goods (ARTG). The technologies described are not PBS-listed and have no current MBS item numbers. RACGP guidelines do not yet address wearable biochemical biosensors of this complexity. Australian relevance is prospective: the chronic disease burden (diabetes affecting approximately 1.3 million Australians, cardiovascular disease, wound care in aged care settings) creates a compelling future use case. Researchers at Australian institutions (e.g., University of Wollongong, RMIT, Monash) are active in related flexible electronics and hydrogel biosensor fields, suggesting domestic translational capacity. However, clinicians should not alter practice based on this review. Potentially applicable to patients requiring continuous physiological and biochemical monitoring (e.g., diabetes mellitus, heart failure, wound care, oncology), particularly in contexts where wearable or implantable flexible biosensors could supplement or replace conventional point-of-care testing. Current evidence supports only research-stage applications.
Abstract
Functionalized hydrogels are ideal flexible interfaces for multimodal health monitoring and integrated diagnosis-therapy systems, owing to their tissue-like mechanical properties, programmable biochemical functions, and hierarchical pores. However, practical applications are often limited by several material bottlenecks: mechanical fatigue and conductivity loss under cyclic stress, the mismatch between degradation rate and functional lifespan, and the trade-off between sensitivity and biocompatibility. To address these challenges, artificial intelligence (AI) has been applied to accelerate structural optimization and property prediction through molecular network engineering and inverse design. Meanwhile, during the collection of coupled mechanical and biochemical signals, these interfaces usually suffer from high background noise, data variability, and baseline drift. Machine learning and deep learning can process these complex datasets through noise filtering, automated feature extraction, and pattern recognition, enabling continuous monitoring and adaptive health management. This review summarizes the recent material design strategies of functionalized hydrogels, AI-driven data analysis methods, and their progress and challenges in integrated diagnosis and therapy.
References
- 1.Sun, T., Liu, S., Qian, K., & Li, S. (2026). Multimodal health monitoring and theranostics based on functionalized hydrogels and artificial intelligence. Journal of Materials Chemistry B. Advance online publication. https://doi.org/10.1039/d6tb00696e
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