3.4

The P4 healthcare vision is not only placing doctors and other health professionals at the centre of the care process, but all those relevant to the health consumer. Even if the healthcare ecosystem is operating in a highly regulated environment, by 2030 we can expect this trend to progressively become the norm. The ECS community should participate in the development of dynamic healthcare systems that learn in real time from every result, achieve a better understanding of treatment response and prognostic heterogeneity, and introduce more refined, patient-tailored approaches to disease detection, prevention, and treatment.

The P4 healthcare vision, enabling for instance early diagnostics based upon merged data and machine-learning techniques through the detection of weak signals, allows preventive treatments that are far less intensive than acute treatments and increase the chances of survival and quality of life. The MedTech industry is not alone on this journey. New pharmaceuticals and treatments will be developed for personalised medicine settings by the use of advanced research techniques and equipment, embedding connected devices and exploiting the potential of the IoT and AI.

The future of personalized medicine lies at the intersection of biology, engineering, and artificial intelligence. Advanced in-vitro platforms—such as Organ-on-a-Chip (OOC) systems and 3D tissue models—are enabling the generation of highly specific, human-relevant biological data. These models replicate patient physiology more accurately than traditional methods, offering a powerful foundation for individualized insights. Layered on top of this, AI-driven multiomics analysis integrates genomic, proteomic, and metabolic data to decode the complex molecular signatures of health and disease. This allows for the identification of precise biomarkers and tailored therapeutic strategies. Looking ahead, predictive disease modelling will transform care from reactive to proactive. By simulating disease progression and treatment outcomes, these models will enable early intervention and long-term health planning. Together, these innovations are not just enhancing diagnostics and treatment—they are redefining medicine as a deeply personal, data-driven discipline.

This is all creating a new industry, one that revolves around digital health platforms. This platform-based new-market disruption will enable the emergence of specialised platforms, and new players will enter the health domain. This will impact current business models in healthcare, using aggregated data to create value rather than devices – supporting, for instance, proactive services, facilitating outcome evaluation for the treatment of different therapies, and paving the way for outcome-based or pay-per-use reimbursements. This is a potential path to reducing the burden of healthcare expenditures.

The P4 healthcare model—predictive, preventive, personalized, and participatory—integrates seamlessly with image-based diagnosis and treatment, enhancing its effectiveness and impact across the healthcare continuum. Here's how each component contributes to image-based medical practices:

Predictive:

• Advanced Imaging Techniques: Predictive healthcare leverages advanced imaging technologies such as MRI, CT scans, and PET scans to identify potential health issues before clinical symptoms arise. These imaging modalities can detect early markers of diseases like cancer or cardiovascular issues, providing crucial information for risk assessment and early intervention.
• AI and Machine Learning Algorithms: Predictive analysis uses AI and machine learning to evaluate imaging data, identifying patterns and forecasting disease progression or treatment outcomes. This approach assists in refining diagnosis accuracy and improving prognostic capabilities.

Preventive:

• Routine Screening and Monitoring: Image-based tools are vital in preventive healthcare, enabling regular screenings to detect abnormalities early. Techniques like mammography and low-dose CT for lung cancer screening exemplify preventive applications, reducing the prevalence of advanced-stage diseases through early detection.
• Integration with Lifestyle and Health Monitoring: Imaging can be combined with data from wearable devices and other health monitoring systems to provide comprehensive assessments, facilitating interventions aimed at disease prevention and health maintenance.

Personalized:

• Tailored Imaging Protocols: Personalized healthcare ensures that imaging protocols are adapted to the individual’s specific health risks and needs. This personalization enhances diagnostic precision and helps devise customized treatment plans that reflect the patient’s unique biological makeup.
• Quantitative Imaging: Techniques like radiomics extract large volumes of quantitative data from imaging that can inform personalized treatment plans, such as tailoring radiation therapy doses based on tumor characteristics.

Participatory:

• Patient Engagement through Imaging Data: Participatory healthcare encourages patient involvement in their treatment process, facilitated by easy access to imaging results and reports. Patients can engage more actively in their healthcare decisions when they understand the visual evidence of their health status and disease progression.
• Collaborative Decision-Making: Imaging data fosters a collaborative environment among healthcare providers and patients, allowing for better-informed decisions regarding diagnostic and therapeutic options. Patients can discuss imagery with clinicians, ask questions, and make choices aligned with their preferences and values.

Overall Contribution

By incorporating the paradigms of P4 healthcare, image-based diagnosis and treatment become more effective and patient-centric. Predictive imaging aids early detection and informs preventive strategies, while personalized imaging enhances diagnostic accuracy and treatment customization. Participatory aspects empower patients to engage with their healthcare journey, leading to improved outcomes and satisfaction. This integration ultimately advances the goal of delivering more precise, proactive, and personalized healthcare services.

The addition of AI capabilities – person-centred AI-based consumer devices/embedded AI-based medical devices and systems – to Electronic Components and Systems will significantly enhance their functionality and usefulness, especially when the full power of such networked devices is harnessed – a trend that is often called “edge AI”. AI enables much more efficient end-to-end solutions by switching from a centralised to a distributed intelligence system, where some of the analysis carried out in the cloud is moved closer to the sensing and actions.

This distributed approach significantly reduces both the required bandwidth for data transfer and the processing capabilities of cloud servers. It also offers data privacy advantages, as personal source data is pre-analysed and provided to service providers with a higher level of interpretation. It also offers greater reliability and safety.

A high level of digital trust – for privacy and security by design, hardened and embedded AI models – is of course required for executing transactions in healthcare and wellbeing. Securing the IoT ecosystem is a multiple level problem. Privacy should be “by design”. In general, integrating security features into an existing system can become very complex, sometimes impossible, and often increases the cost of the final product significantly. A more efficient approach is to consider those security requirements at the very beginning of a project, and then integrate them in the design and development phase. The ECS industry can assist with end-to-end solutions by providing on-chip security, supplying comprehensive hardware and software services, including authentication, data encryption and access management.

Next-generation connectivity – better performing, more ubiquitous, accessible, secure, and energy-efficient networks – will contribute to unleashing the potential of digital health. One of the main characteristics of future networks will be their increased intelligence to improve the performance of the networks, and offer sophisticated and advanced services to the users, due to edge computing and metadata, for instance.

With the significantly growing number of wearables and other battery-operated devices with small form-factor, very low power consumption is a major technology challenge for product designers. The transition from linear to circular economy will require innovative designs for the lifetime of electronic components and systems, and disruptive changes in ECS supply chains, to reduce the ecological footprint. The ECS industry will contribute to improving energy efficiency – including new, sustainable, and biocompatible energy harvesting – to locally process data and the transmission of pre-processed data as opposed to the transmission of high-volume data (such as imaging data).

As a result of improved integration and analysis of multimodal data, new tools for clinical decision-making and precision medicine will emerge, supporting advanced research, early diagnostics, personalised medicine and potential curative technologies (e.g., regenerative medicine, immunotherapy for cancer).

A healthcare edge-to-cloud reference architecture leveraging open APIs and interoperability must embed security- and privacy-by-design principles, while aligning with EU guardrails such as GDPR, the Data Governance Act (DGA), the AI Act, and the Cybersecurity Act to ensure trusted, compliant, and resilient digital health services

Home and community care should prioritize sensor-rich, low-power devices with embedded edge machine learning to enable early detection, on-site triage, and safe actuation. Examples include multimodal wearables, continuous glucose monitors, smart inhalers, fall sensors, and closed-loop drug-delivery patches—all designed to operate autonomously and contextually. Edge inference reduces latency, preserves privacy and battery life, and streams standardized events into human digital twins and care-pathway engines for real-time, personalized intervention

Pay-for-cure rather than pay-for-treatment can be an effective way to increase the efficiency of healthcare by avoiding unnecessary tests, therapies, and prescriptions. Combined with empowered patients, care-givers should be able to make better informed and more effective choices for treatment. To achieve this, outcomes need to cover the full cycle of care for the condition and track the patient’s health status aftercare is completed. This first involves the health status, relying for instance on EHRs supporting precise communication between different care-givers’ PoC diagnostic systems or AI-based clinical decisions. Early diagnosis is key for the successful treatment of both modest and challenging medical conditions.

Health outcomes are also related to the recovery and the sustainability of health. Readmission rates, level of discomfort during care, and return to normal activities should be taken into account for both providers and patients. Humanoid robots applying interpreted human body language and emotion in care delivery, sensors, the deployment of companion devices anticipating and contextualising acute or chronic conditions in EHRs involving health models describing the outcome health values for the patients, both in the short term and long term, will have a direct positive effect on readmission prevention.

To achieve this transformation, a supporting IT platform is necessary. Historically, healthcare IT systems have been siloed by department, location, type of service, and type of data (for instance, diagnostic imaging). An innovative and efficient healthcare information infrastructure – integrating IoT with big data learning for optimising workflow, usage, capabilities and maintenance, and of course digital trust – will aggregate the different areas for efficient value-based healthcare, combining prevention tools, early detection, and treatment. This will enable better measurement and facilitate the design and implementation of new bundle-based reimbursement schemes, reducing costs while improving health outcomes. By 2030, value-based healthcare will enable the adoption of optimisation practices already supported by ECS technologies in the industry.

By 2030, clinical decision-making will be augmented by electronic medical records. Digital centres will enable advanced capabilities for clinical decision-making where AI, real-time data from portable Point-of-Care devices, 3D printing for surgeries, continuous clinical monitoring – including robotics to improve treatments either in the operating room, minimal invasively inside the body, at the general practitioner or at home – will support the integration of specialised care units. A diverse set of information, including images, patient monitoring and lab results will be combined with other sensor data and biomedical models to obtain precise, quantified information about the person’s health condition, preventing and providing, for instance, early warnings for (combined) diseases supported by patient health models on complex health conditions. Low–latency, massive image processing is a major information source for AI-based automation, visualisation, and decision support within the whole care cycle, including from the research phase to post-treatment. Precise quantified and annotated imaging and reconstruction is needed at many levels: from molecular imaging up to whole body imaging. The development and use of accurate digital twins of the human body, organs, cells and pathways will enable clinical trials, individualised computer simulations used in the research, development or regulatory evaluation of a medicinal product, device, or intervention. While completely simulated clinical trials are not yet feasible, their development is expected to have major benefits over current in vivo clinical trials, which will drive further research on the subject. Moreover, “digital twins” will help in combining all the data on a personal level and enable personalised clinical decision-making. Full electronic medical records will also enable optimized hospital workflows that increase efficiency and therefore reduce burden on medical practitioners.

Europe is a leading producer of diagnostic imaging equipment. In diagnostic imaging, the ECS industry has begun to place great emphasis on accurate radiation dose monitoring and tracking. Healthcare providers are already applying dose management as part of the quality programme in their radiology departments, and patient-specific computed tomography (CT) imaging and personalisation of scan protocols will be a key aspect of patient-centred care in radiology departments, facilitating the management and control of both image quality and dose with the optimisation of 3D X-ray imaging protocols. In particular, CT and X-ray systems based on the emerging spectral X-ray photon counting technology will enable new ways of imaging in order to reduce dose, improve tissue differentiation, enhance visualisation, or quantify materials. Image analysis and semi-automated decision making including applications like digital pathology will significantly benefit from underlying AI based methods.

The enormous capabilities of the ECS industry in miniaturisation, integration, embedded intelligence, communication and sensing will have a major impact on the next generation of smart minimally invasive devices:

• Highly miniaturised electrical, magnetic and optical systems realised using advanced cost-effective platform technologies will bring extensive imaging and sensing capabilities to these devices, and enable the second minimally invasive surgery revolution, with smart minimally invasive catheters and laparoscopic instruments for faster and more effective interventions.
• Sensing and diagnostics solutions need to achieve appropriate sensitivity, specificity, and time-to-result.
• Reducing waste is possible through sensors made of biological materials, combining a biological component with a physicochemical detector.
• Fusion of diagnostics and surveillance will help reducing system and operational costs.
• To realise next-generation smart catheters, a broad spectrum of advanced ECS capabilities will need to be brought together, foremost in dedicated platforms for heterogeneous miniaturisation and integrated photonics. These can be complemented with platforms for embedded ultrasound, low-power edge computing, and AI and digital health platform integration.
• Optical coherence tomography (OCT) is another example where ECS technologies make a critical impact, in shrinking devices and reducing costs, allowing devices to be used in wider fields beyond ophthalmology.
• AI-enabled image analysis: AI-algorithms can be employed to analyze medical images, such as X-ray, MRI and CT scans to identify anomalies and patterns with high precision and speed. These AI systems assist radiologists in detecting early signs of diseases like cancer, reducing diagnostic errors, and streamlining the workflow by prioritizing cases needing immediate attention
• Automatic image segmentation: AI algorithms can automatically delineate structures and regions of interest in medical imaging, significantly reducing time and effort compared to manual segmentation. This enhances the accuracy of measurements and diagnostics in fields such as oncology, where precise tumor boundary identification is crucial for treatment planning.
• Data and AI driven applications that provide situational awareness during clinical procedures in operation rooms and imaging labs. Such applications are able to understand how a procedure is progressing, recognize key events and anomalies, and anticipate on next steps to be carried out.

Finally, it should be noted that the development of the next generation of smart minimally invasive instruments will go hand in hand with the development of new navigation techniques:

• Breakthrough innovations in photonics are enabling optical shape-sensing techniques that can reconstruct the shape of a catheter over its entire length.
• MEMS ultrasound technology will enable segmented large-area body conformal ultrasound transducers that are capable of imaging large parts of the body without the need for a trained sonographer, to guide surgeons in a multitude of minimally invasive interventions.
• Combined with other technologies, such as flexible and conformal electronics, low power edge computing, AI, and data integration into clinical systems, new optical and acoustic- based technologies may eliminate the use of x-rays during both diagnosis and interventions, enabling in-body guidance without radiation.
• Augmented reality can be used for image-guided minimally invasive therapies providing intuitive visualisation.

As mentioned above, outcomes should cover the full cycle of care for the condition and track the patient’s health status once care has been completed. Biomarkers derived from medical images will inform on disease detection, characterisation, and treatment response. Quantitative imaging biomarkers will have the potential to provide objective decision-support tools in the management pathway of patients. The ECS industry has the potential to improve the understanding of measurement variability, while systems for data acquisition and analysis need to be harmonised before quantitative imaging measurements can be used to drive clinical decisions.

Early diagnosis through PoC diagnostic systems represents a continuously expanding emerging domain based on two simple concepts: perform frequent but accurate medical tests; and perform them closer to the patient’s home. Both approaches lead to improved diagnostic efficiency and a considerable reduction in diagnostic costs:

• Point-of-care testing (PoCT) methodology encompasses different approaches, from the self-monitoring of glucose or pregnancy, to testing infectious diseases or cardiac problems. However, it should be remembered that disposable PoC devices will need to be environmentally friendly in terms of plastic degradation and the replacement of potentially harmful chemicals.
• The key enabling components of current PoCT devices must include smart and friendly interfaces, biosensors, controllers, and communication systems, as well as data processing and storage.
• The emerging lab-on-a-chip (LoC) solutions, embedding multiple sensor platforms, microfluidics, and simple processing/storage elements, are currently the most promising basis for the realisation and development of accurate, versatile, and friendly portable and wearable PoCT devices. Their simplified operation mode eliminates the constraint of molecular biology expertise to perform a real-time reverse transcription polymerase chain reaction (RT-PCR) test, will enable innovative in vitro diagnostic (IVD) platforms, making possible decentralisation from highly specialised clinical laboratories to any hospital lab and near-patient sites, with dedicated sample prep cartridges, a more efficient prevention (referring to the recent Covid-19 pandemic) and prompt personalised diagnosis.

Complementing the strength of Europe in diagnostic imaging, interventional imaging and surgery, and patient monitoring, Europe has a leading ecosystem of research, development and manufacturing of healthcare and life sciences analytical equipment solutions, dependent upon underlying ECS technologies hardware and software technologies. These analytical solutions are applied not only in e.g., the hospital or clinical laboratory, but also during academic, clinical and pharmaceutical research to understand the fundamental scientific causes of disease and develop more effective, potentially personalized, treatments.

Healthcare and life sciences analytical solutions are used in workflows spanning sample preparation, physical or chemical analysis, data acquisition and processing, leading to results and analytical or clinical conclusions, including scientific understanding or diagnosis. Accuracy, quality, robustness and speed of workflow is paramount to reduce time to reliable results, enabling quicker, more efficient and cost-effective research and development, or prescription of the most beneficial treatments. Examples include, amongst others, the swift and accurate diagnosis of a disease and prescription of drug therapy (e.g., cancer), or the analysis of a virus and development of vaccines as the virus evolves (e.g. COVID-19).

Healthcare and life sciences analytical solutions utilize a broad range of underlying techniques, including: chromatography, spectroscopy, sequencing, micro-arrays, electron and optical microscopies, multiplex/high-throughput ELISA², bioinformatics. Solutions incorporating these techniques utilize various semiconductor components and software, with in particular the use of advanced sensors, processors, memory, (electronic and photonic) data transfer and artificial intelligence enabling breakthrough performance.

In addition, digital supply chains, automation, robotics, and next-generation interoperability can drive operations management and back-office efficiencies. Using robotics to automate hospital ancillary and back-office services can generate considerable cost and time efficiencies, and improve reliability. Robotic process automation (RPA) and AI can allow care-givers to spend more time providing care. For instance, robots can deliver medications, transport blood samples, collect diagnostic results, and schedule linen and food deliveries – either as a prescheduled task or a real-time request. Robotic processes also can be used for certain hospital revenue cycle and accounting/finance functions, such as scheduling and claims processing.

Clinical procedure workflows in healthcare settings are inherently complex, often involving sequences of labour intensive tasks and the need to adhere to strict protocols and guidelines. Such factors add to cognitive load of physicians and the case complexity can lead to inefficiencies, increased risk of errors, and challenges in maintaining high-quality patient care. As healthcare demands grow, data and artificial intelligence (AI) emerge as valuable tools to help manage these workflows. By harnessing vast amounts of patient data, AI can offer predictive insights that assist healthcare professionals in making more informed clinical decisions, ensuring that patient care is thorough and timely. AI-driven clinical procedure automation can streamline tasks such as diagnostics, treatment planning and treatment delivery, and reduce the administrative burden on physicians and enabling them to focus more on direct patient care.

The transition to a healthcare carried out outside conventional hospitals requires integration of solutions and services for specific disease groups with hospital units to optimise patient-generated health data (PGHD: continuous monitoring, clinical trials at home, etc), enhanced by the integration of heterogeneous devices and systems used at home covering parts of the care cycle (smart body patches, wearables, biosensors, monitoring implants, remote sensing, etc) also including AI-enabled devices for remote rehabilitation assistance. Solutions are needed that can be integrated into secure health digital platforms, portable end-user devices, remote e-healthcare and AI front-ends.

In addition, the pharmaceutical market is experiencing strong growth in the field of biologics (genomics and proteomics, as well as microarray, cell culture, and monoclonal antibody technologies) that require preparation prior to administration. Smart drug delivery solutions are now based on innovative medical devices for the automated and safe preparation and administration of new fluidic therapies and biologic drugs. These use advanced ultra-low power microcontrollers that control the process reconstitution of the drug based on parameters identified by the practitioner, together with wireless communication modules to transmit data and ensure the patient and treatment are monitored. Smart drug delivery will improve drug adherence as patients will be empowered to administer expensive and complex drugs in their own home.

In this emerging context, care solutions need to be integrated, combining information across all phases of the continuum of care from many sources – preventing, preparing, and providing care based on person-specific characteristics. This will support the development of applicable biomedical models for specific disease groups, for customer groups and for populations, taking heterogeneous data involving history, context, or population information into account.

Supporting prevention, diagnosis, aftercare, and rehabilitation with sensors and actuators to ensure efficient medical decision, leveraging edge computing and imaging as described in the previous section, will be crucial. The next generation of devices will incorporate increasingly powerful edge computing capabilities. Analysing PGHD from medical devices can be synchronised with a web-based monitoring system. When aggregated, this data can be then sent to the organisation’s health data analytics system to process the results and compare them to previous measurements. If the analysis uncovers negative trends in the patient’s health status, it will automatically notify the care team about possible health risks. The ECS industry can play an important role here in bringing ambulatory monitoring to the next level. The following enabling technology platforms can contribute to this:

• Low-power technology for (remote and on-body) sensors, microprocessors, data storage and wireless (microwave, optical, sound) communication modules, etc.
• Miniaturisation and integration technologies for sensors, microprocessors, data storage, and wireless communication modules, etc.
• Advanced sensing technologies for multiplex, painless sensing with high sensitivity and reliability for both wearables and biosensors.
• Printed electronics technology for textile integration and the patch-type housing of electronics.
• Low-power edge AI computing for data analysis and reduction.
• Data communication technology for interoperability of (wireless) data infrastructure hardware (wearable device connections) and software (data sharing between data warehouses for analysis, and with patient follow-up systems for feedback).
• Data security technology for interoperability between security hardware and software components (end-to-end information security).

The development of next-generation drug delivery systems will form part of the IoMT – medical devices and applications that link with healthcare systems using wireless connectivity. Smart drug delivery will improve drug adherence so that patients can administer expensive and complex (biological) drugs in their home environment. Enabling platforms are required to facilitate a transition from the legacy mechanical components seen in current autoinjectors and wearable drug delivery pumps, to highly integrated, patch-like microsystems. These include:

• High-performance sensors and actuators for drug delivery, monitoring and control.
• On-board microfluidics for in-situ preparation and delivery of formulations.
• Minimally invasive (micro-)needles and electrodes for transdermal interfacing, delivery, and diagnostics.
• New materials, containers and power sources that will meet stringent environmental and clinical waste disposal standards.
• Body-worn communication technologies for IoMT integration and clinical interfacing.
• Edge AI for closed-loop control, adherence assessment and clinical trial monitoring.

The development of low-cost, silicon-based MEMS ultrasound transducers technologies is bringing ultrasound diagnostics within the reach of the ECS industry. The ECS industry has the instruments and production technologies to transform these into high-volume consumer products, something no other industry is capable of. Personal ultrasound assisted by AI data acquisition and interpretation will allow early diagnoses in consumer and semi-professional settings, as well as in rural areas. As such, they present a huge opportunity for the ECS industry. It is expected that MEMS ultrasound will enable a completely new industry, with MEMS ultrasound transducers being the enabling platform technology that will drive things on.

Among the emerging applications of advanced MedTech, “smart wound care” – i.e., the merger of highly miniaturised electronic, magnetic, optical and communications technologies with conventional wound dressing materials – will allow the treatment of chronic wounds of patients in their home without the intervention of daily nursing and/or constant monitoring of the status of the wound. While much progress has been made in wearable technologies over the past decade, new platforms must be developed and integrated to enable the rapid rollout of intelligent wound care. These include:

• Flexible and low-profile electronics, including circuits, optical components, sensors, and transducers, suitable for embedding within conventional dressings.
• Advanced manufacturing techniques for reliable integration of microelectronic technologies with foam- and polymer-based dressing materials.
• Biodegradable materials, substrates, and power sources that will meet stringent environmental and clinical waste disposal standards.
• Body-worn communications technologies for low-power transmission of wound status.
• Edge AI to assist the clinical user in data acquisition and data interpretation.

One of the crucial means of coping with the prevalence of the chronic diseases is to achieve a more participative and personalised approach, as such diseases require the long-term monitoring of the patient’s state, and therefore need individuals to take greater ownership of their state of health. Most chronic disease patients have special healthcare requirements and must visit their physicians or doctors more often than those with less serious conditions. Technological innovation has already been identified as a great medium to engage chronic patients in the active management of their own condition since digital health offers great convenience to such patients. Access to biomedical, environmental and lifestyle data (through cloud computing, big data and IoT, edge AI, etc) are expected to better target the delivery of healthcare and treatments to individuals, and to tailor each decision and intervention, especially for the treatment of those with multiple chronic diseases.

Patients will be connected seamlessly to their healthcare teams, care-givers and family, as treatment adherence will be more efficient with the innovations mentioned in previous sections. Remote sensing and monitoring offer great promise for the prevention and very early detection of pathological symptoms. Remote sensing and monitoring have the potential to become embedded into everyday life objects, such as furniture and TV sets, while bearing in mind the constraints related to security and privacy. Remote patient monitoring will support clinical decisions with a reduced potential for false alarms, especially for the long-term monitoring and data analysis of patients with chronic diseases.

Personalized regenerative medicine is transforming chronic disease care—from managing symptoms to restoring function. Cell therapies, tissue engineering, and organ regeneration offer tailored healing using a patient’s own biology. Patients actively participate in their treatment journey, shaping therapies around their needs and data. In the future, lab-grown organs may replace damaged ones entirely, redefining what recovery means.

The ECS industry will need to take the initiative in the development of the next-generation treatment of chronic diseases. The field of remote sensing holds great promise for the lifelong and chronic monitoring of vital signs. The deployment of remote monitoring system relies on sensors integrated into bed or chair. Optical sensing techniques, for instance for remote reflective photoplethysmography, as well as capacitive and radar sensing support this approach. This will be multimodal, with fusing techniques to smart analytics to unify the data into usable information. The strength of remote sensing not only relies on the quality of the acquired signals, but also its potential to reveal slowly changing patterns – possibly symptoms from underlying physiological changes. The analysis of such datasets, currently largely unexplored, will provide new insights into normal versus pathological patterns of change over very long periods of time:

• Treatment of chronic diseases will be enhanced by an upcoming generation of small and smart implantable neuromodulator devices, which are highly miniaturised, autonomous, and cost-effective. These will be implanted, wirelessly powered by radio frequency (RF), microwave, ultrasound or energy harvesting with minimal side effects on the selected nerve through a simple and minimally invasive procedure to modulate the functions of organs in the treatment of pain management, brain disorders, epilepsy, heart arrhythmia, autoimmune diseases (immunomodulation), etc. Since Magnetic Resonance Imaging is the golden standard for brain imaging, these new implants are faced with the fact that they need to be MR-conditional compliant. The increase in occurrence and amount of implants also has a negative effect on MR image quality and/or throughput time, hence innovations are required in the power, RF and software/AI domains to mitigate.
• Organ-on-a-chip (OOC) platforms, which lie at the junction of biology and microfabrication and biology for personalised and safer medicines, are another treatment approach, addressing, for instance, pathologies currently without effective treatment (rare diseases). Often rare diseases are chronic and life-threatening, and they affect approximately 30 million people across Europe. In an OOC, the smallest functional unit of an organ is replicated. The essential capabilities underlying the OOC field are primarily embedded microfluidics and the processing of polymers in a microfabrication environment. Smart sensors can be used as readout devices, while edge AI will be essential in data interpretation and reduction.
• New pharmaceuticals are arising for the treatment of chronic diseases which have side-effects which require regulator hospital-based diagnostic imaging. Innovations are needed to optimize the workflow for “companion diagnostics”.
• In the long run tools for regenerative medicine will enable cell therapies, tissue engineering, organ regeneration and replacement.

For chronic diseases diagnoses, LoC-based technologies – relying on miniaturisation – show promise for improving test speed, throughput, and cost-efficiency for some prominent chronic diseases: chronic respiratory diseases, diabetes, chronic kidney diseases, etc.

The following list includes some typical examples of assistance capabilities related to major chronic diseases covering the first main objective (optimisation of patient follow-up at home):

• Vital signs checker: blood pressure meter, pulse meter, oximeter, thermometer, weight scale expanding to sensor hubs for vital signs enabling applications like Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), Electrocardiogram (ECG), Galvanic skin response (GSR), Blood volume pulse (BVP),
• proactive speech centric HMI approaches to enable interactive operation of healthcare related devices.
• Hospital's software interface, the patient’s file, the patient's risk alarm centre with automatic call to healthcare practitioners.
• Audio and Video communication support (between the patient and their nurse, doctor, and family), and interactive modules for the patient (administrative, activities, menus, medical bot chat, etc.).
• Authentication and geolocation of patients, with patient or patient’s family consent.
• Teleconsultation for nights and weekends at the foot of the bed of patient’s hospital or retirement home.

The second objective – smooth home return – relates to suitable technical assistance in addition to human assistance:

• Enhance the patient's quality of life and autonomy.
• Improve the patient's safety and follow-up in their room through a reinforced work organisation.
• Allow monitoring of the patient’s progress to motivate them during their rehabilitation period and encourage and enable active participation in social life
• Minimally invasive therapies allowing for shorter hospital stays and improved patient wellbeing.
• First-time-right precision diagnoses to prevent hospital readmissions.
• Prepare for the return home: patient support in appropriating technical aids by integrating these solutions into rehabilitation.

Efforts are being made to enhance medical and social care services through different kinds of robots. The purpose here is to provide advanced assisted living services via a general-purpose robot as an autonomous interaction device that can access all available knowledge and cooperate with digital appliances in the home. In this sense, autonomous mobile robots offer several advantages compared to the current (stationary) Ambient Assisted Living (AAL) solutions. Due to sensor-augmented user interfaces, like speech recognition and synthesis, human computer interaction is becoming increasingly natural. Consequently, robots will come to represent a familiar metaphor for most people.

Neurorehabilitation is sometimes required after hospitalisation and is generally a very complex and challenging undertaking resulting in both “successes” and “failures” (setbacks). Neurological patients typically report having “good days and bad days”, which affect performance, motivation and stamina, and where cognitive stimulation (AI-based speech producing programs, social robots, etc), for example, has the potential to improve the efficiency of neurorehabilitation and relieve some of the pressure on health systems. Robotics is well suited for precise, repetitive labour, and its application in neurorehabilitation has been very successful. This is one of the main reasons why the rehabilitation robotics market has tripled over the last five years and, today, rehabilitation robotics is one of the fastest growing segments of the robotics industry. This industry is dominated by European companies that can deliver highly innovative solutions with a strong scientific basis and exceptional manufacturing quality. Based on market size and need, it is projected that the compound annual growth rate (CAGR) for rehabilitation robotics will soon reach between 20% and 50%.

The ECS industry can significantly upscale the “ageing well” area, as it is enabled by most of the focus topics developed in the previous sections. The industry is playing an important role in bringing ambulatory monitoring to the next level. Important aspects here are reducing costs, improving user friendliness (e.g. easy to wear/use devices, interoperable gateways, reduction of patient follow-up systems) and data security.

The enabling technology platforms detailed below are expected to significantly contribute to this prevalence of the ECS industry in ageing well, taking into account that ageing well is very much related to “ageing in place”:

• Low-power technology for sensors and actuators, microprocessors, data storage and wireless communication modules, etc.
• Miniaturisation technology for sensors and actuators, microprocessors, data storage and wireless communication modules, etc.
• Printed electronics technology for textile integration and patch-type housing of electronics.
• Low-power edge AI computing for data analysis and data reduction.
• Data communication technology for interoperability of (wireless) data infrastructure hardware (wearable device connections) and software (data sharing between data warehouses for analysis and with patient follow-up systems for feedback).
• Data security technology for interoperability between security hardware and software components.
• Robotics systems enabling patients to overcome loneliness or mental healthcare issues, complemented with cognitive stimulation by AI-based speech recognition and synthesis enabling augmented HMI for social robots

Interoperability is surely the main challenge faced by the ECS industry in achieving full impact due to the vast heterogeneity of IoT systems and elements at all levels. Interoperability and standardisation need to be elaborated in relation to data and aggregated information. Thus, it is not enough to be able to receive a message, i.e. to understand the syntax of the message, but it is also necessary to understand the semantics. This requirement implies the development of a data model that maps semantic content from the data received from devices into an information system that is usually utilised for collecting and evaluating data from monitored persons. It must be based on several relatively simple principles: creation of formats and protocols for exchange of data records between healthcare information systems; format standardisation and connected interface unification; improvement of communication efficiency; a guide for dialogue between involved parties at interface specification; minimisation of different interfaces; and minimisation of expenses for interface implementation.

• TIMELINE

The following table illustrates the roadmaps for Health and Wellbeing.