Integrating AI into Smart Health: Wearables, Imaging, and Software as a Medical Device

Opportunities and Challenges in the Next Phase of Digital Healthcare

Artificial intelligence is reshaping healthcare technology across the Smart Health sector. It’s now embedded in wearable devices, medical imaging, and regulated software systems, influencing how data is gathered, interpreted, and applied in patient care. The result is a shift toward faster insights, earlier interventions, and more personalised treatment pathways, but also new challenges around validation, safety, and trust. 

Understanding how AI is being used in wearables, medical imaging, and Software as a Medical Device (SaMD), highlights both the potential and the work still required for responsible adoption. 

AI in Wearables

Wearable sensors now do far more than count steps or track sleep. They monitor heart rhythms, oxygen saturation, and motion patterns continuously, generating data that AI models can interpret to identify trends or early signs of illness. The ability to detect subtle physiological shifts is giving users and clinicians a more complete picture of health outside traditional care settings. 

A strong example of this is the Axon-R project from Cognixion and Blackrock Neurotech, a non-invasive brain–computer interface that uses AI to interpret neural signals for communication and rehabilitation. It demonstrates how wearables are moving beyond fitness tracking into clinical grade neurotechnology, where real-time signal processing and adaptive learning models play a central role. 

As hardware improves, machine learning models are increasingly deployed directly on the device, using edge AI technologies rather than relying on the cloud. Processing data locally reduces latency and strengthens privacy, which is a crucial factor when handling sensitive health information. 

Models built on narrow datasets may not perform reliably across diverse populations. Validation and reproducibility are key regulatory expectations, and privacy continues to be a central issue as data moves between sensors, smartphones, and external services. The success of wearable AI is dependent on technical precision, transparent design, and clinically sound evidence, not just on innovation itself. 

AI in Medical Imaging

In medical imaging, AI has already shown tangible benefits. Machine learning systems can assist in detecting tumours, segmenting tissues, and tracking disease progression. In radiology, ophthalmology, and dermatology, these systems help reduce variability and improve diagnostic efficiency. 

Quibim’s QP-Prostate, is an imaging model that supports prostate-cancer detection and analysis. The company has also developed AI tools for the brain, liver, breast, and lung, reflecting how imaging AI is being adapted across multiple anatomical and clinical domains to support radiologists in diagnosis and monitoring. 

Clinicians must be able to interpret how an algorithm reaches its conclusions. Without transparency, confidence drops quickly. Explainable AI (systems that make their reasoning accessible) is becoming essential for clinical acceptance and regulatory approval. 

Data quality also defines success. Building reliable imaging models requires large, well-annotated datasets that represent real-world patient diversity. Many organisations still lack this scale of data. Even when models perform well, they need to fit smoothly into existing imaging and record systems to be practical in use. 

Regulators such as the MHRA, FDA, and EMA have begun publishing detailed frameworks for AI-driven imaging technologies, but continuous-learning models still challenge conventional approval processes. When an algorithm can change over time, defining and maintaining validation becomes a moving target. Balancing innovation with patient safety continues to be a central issue. 

AI in Software as a Medical Device (SaMD) 

Software as a Medical Device, or SaMD, has become one of the most active areas in healthcare innovation. These are software applications that perform a medical function independently of hardware, often using AI to diagnose, predict, or recommend clinical actions. 

AI-driven SaMD introduces new complexity. Algorithms may evolve as they process more data, leading to changes in behaviour that regulators describe as algorithmic drift. Managing these updates while maintaining compliance is one of the sector’s most persistent challenges. 

Lifecycle documentation and version control are essential. Developers must record not only code changes but also how data was collected, labelled, and used in model training. Standards such as ISO 14971 and IEC 62304 provide a foundation for this discipline, while more recent guidance from the FDA and MHRA focuses on ongoing transparency and performance monitoring. 

The MHRA’s AI Airlock is a notable development – a sandbox allowing companies to test adaptive algorithms in a controlled, supervised environment. Initiatives like this are helping to define what safe, evidence-based innovation looks like in the age of learning systems. 

AI Integration and Adoption

Despite rapid advances, integrating AI into everyday clinical practice remains difficult. Healthcare IT infrastructures are often fragmented, with legacy systems that don’t easily communicate. Implementing AI tools in these environments requires technical adaptation and, more importantly, cultural change. 

Many algorithms perform well in research but lack large-scale, real-world validation. Clinicians are understandably cautious when systems cannot demonstrate consistent accuracy across diverse patient groups. Explainability and auditability are vital for trust. AI must act as a support tool, not an opaque authority. 

A “human-in-the-loop” model (where clinicians remain central to decision-making) could be the most reliable path forward. This approach maintains accountability and enables continuous learning, both for the system and its users. 

Adoption is also limited by the skills gap. Deploying and maintaining AI requires input from software engineers, data scientists, clinicians, and regulatory specialists. Progress will depend on cross-disciplinary collaboration and investment in technical ability across healthcare teams. 

AI’s Future Outlook

The Smart Health sector is moving toward adaptive systems that evolve based on new data and outcomes. These could eventually support fully personalised models of care, but they also introduce new questions about validation, traceability, and oversight. 

Federated learning offers a promising direction. By allowing models to train across multiple institutions without sharing raw data, it supports both scalability and privacy. Ethical oversight will still be central, ensuring algorithms are fair, unbiased, and transparent about how patient data is used. 

Standardising how AI systems are tested and validated could become a defining feature of the next regulatory phase. Clearer expectations will help innovators build safe products while giving clinicians confidence in the tools they use. 

Ultimately, AI’s future in Smart Health will depend less on technical capability and more on how effectively it integrates into clinical workflows and regulatory structures. Trust will be earned through consistency, clarity, and measurable benefit to both patients and clinicians.  

Endnote

AI is already part of the Smart Health ecosystem, but sustainable progress requires disciplined development and responsible integration. Robust validation, transparent algorithms, and close alignment with clinical practice are going to be essential if these technologies are to reach their full potential. 

References

• FDA: “Good Machine Learning Practice for Medical Device Development” (2021) 
• MHRA: “Software and AI as a Medical Device Change Programme Roadmap” (2023) 
• European Commission: “Artificial Intelligence Act” (2024) 
• ISO 14971:2019 – Medical Device Risk Management 
• IEC 62304:2006 – Software Lifecycle Processes 

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