Hedging, ambiguity, and the rejection of misinformation: evidence from Chinese readers.

The healthcare sector is undergoing a substantial digital transformation, shifting from conventional models centred on episodic care and manual record keeping toward intelligent, connected, and data-driven systems that support continuous monitoring, timely intervention, and personalized care (, ). A key enabler of this transition is the Internet of Medical Things (IoMT), which comprises interconnected medical devices, wearable technologies, implantable sensors, mobile health platforms, and health information systems that acquire, transmit, and exchange physiological and behavioural data across patients, clinicians, and healthcare infrastructures (, ). Through such connectivity, IoMT supports remote patient monitoring, chronic disease management, telemedicine, early warning systems, and more responsive clinical decision making (, ). The growth of IoMT reflects broader changes in healthcare service delivery. Connected medical devices are increasingly being used to extend care beyond hospitals into homes, community settings, ambulatory care, and remote-monitoring environments. Although market estimates vary, they consistently indicate rapid expansion: earlier estimates placed the IoMT market at approximately USD 158.1 billion by 2022, while more recent estimates valued the global IoMT market at USD 230.69 billion in 2024 and projected growth to USD 658.57 billion by 2030 (, ). This expansion is driven by rising demand for remote patient monitoring, wearable technologies, telehealth integration, cost-efficient care delivery, and the increasing need to manage chronic diseases outside traditional hospital settings. For health systems, the expected impact is not limited to more devices. IoMT is expected to support earlier detection of clinical deterioration, improved care continuity, more personalized disease management, reduced pressure on physical healthcare infrastructure, and wider access to services for underserved or hard-to-reach populations (, ). IoMT value depends on continuous processing, not only data collection. These systems generate heterogeneous, noisy, high-volume, and temporally dynamic data from devices with different sampling rates, protocols, battery limits, and reliability profiles. This creates challenges for conventional methods under uncertainty, nonlinearity, missing values, and non-stationary conditions (). IoMT therefore needs intelligent models that support accurate prediction, adaptive learning, evaluated explainability, and deployment-aware operation across edge, fog, cloud, and hybrid healthcare infrastructures. To address these challenges, artificial intelligence (AI) and machine learning (ML) have become integral to IoMT platforms, improving predictive performance, responsiveness, and adaptive decision support (, ). Among the earlier intelligent approaches, artificial neural networks (ANNs) gained prominence because of their strong pattern-recognition capabilities and their success in tasks such as medical image analysis, physiological signal interpretation, and disease classification (, ). In parallel, fuzzy logic systems provided an effective means of representing and reasoning with imprecise clinical knowledge through rule-based structures that can be inspected and tested for interpretability. By operating on linguistic variables such as “moderate fever” or “elevated blood pressure,” fuzzy systems can support human-readable reasoning when rule bases remain concise, stable, and clinically meaningful in settings characterized by ambiguity and uncertainty (, ). Nevertheless, these paradigms also exhibit important limitations when applied independently. Neural networks are often criticized for limited transparency, while fuzzy systems may become difficult to scale in high-dimensional and data-intensive environments (, ).