Knowledge Database | Blogpost directory

Here the overview of our THAUMATEC Blogpost inclusive the assignment to the Blogpost types

  • HealthTech Industry Updates
  • HealthTech Knowledge Guide
  • IOT Technology and Experience
  • Thaumatec

and inside HealthTech Industry Updates the HealthTech Industry Blogpost topics and domains

  • HealthTech Trends and Reports
  • MedTech Regulation Impact
  • Telehealth
  • Smart Digital Healthcare
  • Smart Devices and Wearables
  • Robots and AI for Health

to navigate better through the whole Data Base Blogpost material.

Most recent articles/posts are on the bottom of every chapter/block.


HealthTech Trends and Reports

MedTech Regulation Impact


Smart Digital Healthcare

Smart Devices and Wearables

Robots and AI for Health





Thaumatec HealtTech Industry Update | 5 ways AI could improve the world

‘We can cure all diseases, stabilise our climate, halt poverty’ !  It is not yet clear how the power and possibilities of AI will play out. Here are the best-case scenarios for how it might help us develop new drugs, give up dull jobs and live long, healthy lives.

Recent advances such as Open AI’s GPT-4 chatbot have awakened the world to how sophisticated artificial intelligence has become and how rapidly the field is advancing. Could this powerful new technology help save the world?

1. ‘More intelligence will lead to better everything’

Everything’s going to improve. We will be able to cure cancer and heart disease, and so on, using simulated biology – and extend our lives.

The average life expectancy:

  • it was 30 in 1800
  • it was 48 in 1900
  • it’s now pushing 80
  • and we may reach “longevity escape velocity” by 2029.

 If the wrong people take control of AI, that could be bad for the rest of us, so we really need to keep pace with that, which we are doing. But we already have things that have nothing to do with AI, such as atomic weapons, that could destroy everyone. So it’s not really making life more dangerous. And, it can actually give us some tools to prevent people from harming us.

We’ve made great progress but there are still people who are desperate. More intelligence will lead to better everything. We will have the possibility of everybody having a very good life.

2. ‘We can use AI tools right now to help fight climate change’

Everyone wants a silver bullet to solve climate change; unfortunately there isn’t one. But there are lots of ways AI can help fight climate change. While there is no single big thing that AI will do, there are many medium-sized things.

The first role AI can play in climate action is distilling raw data into useful information:  taking big datasets, which would take too much time for a human to process, and pulling information out in real time to guide policy or private-sector action.

The second role is optimisation of complicated systems: such as the heating and cooling system in a building, where there are many controls that an algorithm can operate efficiently. Many companies are improving energy efficiency, and there is a lot of progress still to be made, especially in industries such as steel and cement.

The third theme is forecasting: AI can’t predict something big-picture like what’s going to happen to the economy e.g. what power is going to be available based on the sun and the wind, forecasting how a storm is going to move, or the productivity of crops based upon the weather.

The fourth theme is in speeding up scientific simulations: such as in climate and weather modelling.

Thinking of AI as a futuristic tool that will lead to immeasurable good or harm is a distraction from the ways we are using it now

3. ‘There is going to be an amazing revolution in healthcare’

There is a rapid transformation in the pharmaceutical industry and university research, where they’re shifting to the use of AI to help discover new molecules and new drugs that would have fewer side-effects, and that would help us cure diseases that currently we don’t know how to cure, including cancer, potentially.

One reason AI can be useful here is that the body is very complicated. Even a single cell is extremely complicated: you have 20,000 genes, and they all interact with each other. Biotechnology has progressed to the point where we can measure all the genes’ activity in a single cell at once. While we collect huge quantities of data, the quantity of data is so large that humans are unable to read it. But because machines can, they are able to build models of how your cells work, and how they could be changing under different circumstances that cause disease. So, you can see what happens if you make an intervention; if you introduce a pollutant or a drug, what will be the effect?

This is not just something happening in academia. There are now dozens of startups that have been created at the intersection of AI and drug discovery, broadly speaking. These have been injected with billions of dollars, while pharmaceutical companies are beefing up their machine-learning departments.

4. ‘AI could radically accelerate the process of technological progress itself’

If we figured out how people are going to share in the wealth that AI unlocks, then I think we could end up in a world where people don’t have to work to eat, and are instead taking on projects because they are meaningful to them. E.g. Children do a lot of things because they enjoy them, and not just because they’re the best person in the world at them. They paint and draw, and they have a lot of fun; I paint and draw, and I have a lot of fun, even though [AI image generator] Midjourney is way better at making pictures than me. Similarly, since the 90s, we have had computer programs that can beat humans at chess, but lots of people still play chess.

If you have intelligent AI systems that are accessible to people, it’s as if everybody has access to an infinitely patient teacher so you could imagine training these AI systems to be an interface between humans and other humans.

5. ‘We can flourish, not just for the next election cycle, but for billions of years’

The positive, optimistic scenario is that we responsibly develop superintelligence in a way that allows us to control it and benefit from it. The “control” part is, I think, more hopeful than many people assume. There is a field of computer science called formal verification, where you come up with a rigorous mathematical proof that a program is always going to do what it’s supposed to. You can even create what is called “proof-carrying code”; it works in the opposite way to a virus checker. If a virus checker can prove that the code you are going to run is malicious, it won’t run it; with proof-carrying code, only if the code can prove that it’s going to do what you want it to do will your hardware run it. This is the type of mechanism we need to ensure advanced AI is safe.

We can’t do this yet with GPT-4 or other powerful AI systems, because those systems are not written in a human programming language; they are a giant artificial neural network, and we have almost no clue how they work. But there is a very active research field called mechanistic interpretability.

The goal is to take these black-box neural networks and figure out how they work. If this field makes so much progress that we can use AI itself to extract out the knowledge from other AI and see what it has learned, we could then reimplement it in some other kind of computational architecture – some sort of proof-carrying code – that you can trust. Then you can still use the power of neural networks to discover and learn, but now you can trust something that’s way smarter than you. Then what are we going to do with it?

The sky’s the limit.


We can cure all diseases, stabilise our climate, eliminate poverty, etc. We can flourish not just for the next election cycle, but for billions of years. We have been on this planet for more than 100,000 years, and most of the time we have been like a leaf blowing around in the wind, without much control of our destiny, just trying to not starve or get eaten. Science and technology and human intelligence have made us the captains of our own ship. If we can build and control superintelligence, we can quickly go from being limited by our own stupidity to being limited by the laws of physics.

It could be the greatest empowerment moment in human history.

Take a look at the article of The Guardian:

Thaumatec HealthTech Industry Update | FDA qualifies Apple Watch

FDA qualifies Apple Watch AFib feature for use in clinical trials.
Officials will accept atrial fibrillation data collected by the wearable as a secondary endpoint in studies of cardiac ablation devices.


  • The Food and Drug Administration has qualified Apple Watch’s atrial fibrillation (AFib) history feature for use in medical device clinical trials, the agency said Wednesday.
  • FDA officials will accept data collected by Apple Watch as a secondary endpoint to help assess AFib burden in studies of cardiac ablation devices. 
  • The FDA said Apple Watch can address challenges related to patient compliance, potential placebo effects and the technical difficulties of measuring AFib burden without an implantable device.

FDA Medical Device Development Tools program

The FDA created the Medical Device Development Tools (MDDT) program to reduce uncertainty in device development. Previously, the agency evaluated tools used to collect data in medical device trials on a case-by-case basis. Through MDDT, the FDA has created a portfolio of qualified tools that sponsors know the agency will accept without needing to reconfirm their suitability for use in a study.

Apple applied with AFib

Apple applied to get its AFib history feature qualified as a MDDT in December. It is the first digital health technology qualified under the program. Apple Watch monitors changes in blood flow at the wrist and measures the intervals between heart beats. The history feature analyzes the intervals to estimate the amount of time the wearer spent in AFib over the previous week.

Qualifying Apple Watch may reduce the burden on a medical device developer, by eliminating the need to provide a rationale for its collection methods and cadence. The Apple Watch could reduce the barrier to assessing AFib burden in device trials.

The FDA said performance may be reduced in patients who have undergone prior ablation and noted the lack of AFib episode timestamps.

See the full article by MEDTECHDIVE

Apple’s technology for monitoring heart rhythms is at the center of a dispute with Alivecor. A federal judge dismissed an Alivecor lawsuit in February, but the company said it would appeal the ruling.

See as well Reuters:

HealthTech Industry Update | Prioritizing Patient Care

The MedTech industry is poised for breakthroughs, owing to the rapid integration of digital ecosystems and technologies like AI and cloud.

2023 was a banner year for MedTech, especially in terms of technological innovations. Incidentally, it also witnessed the largest ever number of FDA approvals on novel medical devices in a single year. This list included a number of AI-enabled MedTech products, among others.

Nurturing tomorrow: Redefining patient experiences

As the market evolves, MedTech firms are pressed to keep up with the changes afoot for competitive edge and business growth. The early mover advantage will be a crucial factor here as the heat intensifies.

Three key areas are continuing to shape the industry:

⚕ IoMT for connected care

The Internet of Medical Things (IoMT) is a transformative technology that allows healthcare providers to deliver remote care using connected medical devices and software that transmits patient results online.

IoMT allows physicians and experts to analyse data in real-time to provide optimized patient care through informed decision-making and timely interventions. This is especially true in today’s scenario, where chronic diseases are straining already limited medical staff and resources.

⚕ AI, the better care ally

AI, and more recently GenAI, presents a plethora of opportunities in medical imaging and chronic disease management. Here, data is the key.

For instance, several medical players are integrating AI systems with medical devices such as colonoscopy equipment to scan every image taken during the procedure in milliseconds, flagging potential lesions. The AI system can sift through tremendous amounts of data to provide insight-driven care and significantly improve the chances of early detection of colorectal cancer. This is just one example of AI transforming healthcare by empowering MedTech advancements.

⚕ Digital platforms for accessible care delivery

Patient care has reached beyond the four walls of the hospital. Through continuous monitoring, virtual hospital wards and e-clinics powered by wearables, AI-powered cloud platforms, AR/VR, and connected devices are set to reduce in-patient visits.

Digitally equipped platforms like this can be used by hospitals, critical care centres, and rehabilitation centres to tackle a wide range of mental, physical, and occupational health challenges, including chronic pain, anxiety, fibromyalgia, and even dementia.


As the MedTech industry enters the next phase of digital evolution, it’s important to diversify the perspective – and think of accelerators like GenAI, cloud, and IoMT as tools for enhancing patient care and engagement. This will also aid providers and payers, who will now benefit from intelligent workflows, connected data streams, and preventive interventions, ultimately reducing costs and administrative burdens.

The digital transformation of MedTech will result in improved collaboration between stakeholders, paving the way for an interoperable and patient-centric ecosystem.

Here the full Article by the World Economic Forum:

HealthTech Industry Update | AI-enhanced ultrasound for women’s health

Ultrasound is used in many different fields. Ultrasonic devices are used to detect objects and measure distances. Ultrasound imaging or sonography is most often used in medicine. In the non-destructive testing of products and structures, ultrasound is used to detect invisible flaws. Industrially, ultrasound is used for cleaning, mixing, and accelerating chemical processes.

Diagnostic ultrasound is a non-invasive diagnostic technique used to image inside the body. Ultrasound probes, called transducers, produce sound waves that have frequencies above the threshold of human hearing (above 20KHz), but most transducers in current use operate at much higher frequencies (in the megahertz (MHz) range). Most diagnostic ultrasound probes are placed on the skin. However, to optimize image quality, probes may be placed inside the body via the gastrointestinal tract, vagina, or blood vessels. In addition, ultrasound is sometimes used during surgery by placing a sterile probe into the area being operated on.  

  • Diagnostic ultrasound can be further sub-divided into anatomical and functional ultrasound.
  • Anatomical ultrasound produces images of internal organs or other structures.
  • Functional ultrasound combines information such as the movement and velocity of tissue or blood, softness or hardness of tissue, and other physical characteristics, with anatomical images to create “information maps.”

These maps help doctors visualize changes/differences in function within a structure or organ.

Here more information and background:

Examples for ultrasound examination in the Femtech area:

Obstetric ultrasonography, or prenatal ultrasound, is the use of medical ultrasonography in pregnancy, in which sound waves are used to create real-time visual images of the developing embryo or fetus in the uterus (womb). The procedure is a standard part of prenatal care in many countries, as it can provide a variety of information about the health of the mother, the timing and progress of the pregnancy, and the health and development of the embryo or fetus.

Breast ultrasound is a medical imaging technique that uses medical ultrasonography to perform imaging of the breast. It can be performed for either diagnostic or screening purposes[1] and can be used with or without a mammogram. In particular, breast ultrasound may be useful for younger women who have denser fibrous breast tissue that may make mammograms more challenging to interpret.

Vaginal ultrasonography is a medical ultrasonography that applies an ultrasound transducer (or “probe”) in the vagina to visualize organs within the pelvic cavity. It is also called transvaginal ultrasonography because the ultrasound waves go across the vaginal wall to study tissues beyond it.

Ovarian cysts are usually diagnosed by ultrasound, CT scan, or MRI, and correlated with clinical presentation and endocrinologic tests as appropriate.

Pelvic congestion syndrome, also known as pelvic vein incompetence, is a long-term condition believed to be due to enlarged veins in the lower abdomen. The condition may cause chronic pain, such as a constant dull ache, which can be worsened by standing or sex. Pain in the legs or lower back may also occur.

Current advances of Ultrasound in the Femtech area:

GE HealthCare designed the ultrasound systems to integrate AI, advanced tools and an ergonomic design. They speed exam time for clinicians while delivering a clearer picture of various conditions impacting women’s health. The latest systems combine high-performance hardware with flexible, scalable software to help increase confidence in diagnostic and treatment decisions.

AI-enabled ultrasound technologies support clear, quick and confident diagnoses. This latest launch helps to address patient demand and reduce staffing burdens by giving clinicians these tools.

New, improved and enhanced Features are:

  • Features include voice commands and the SonoLyst suite of AI tools. SonoLyst tools identify fetal anatomy and annotate and measure where applicable. This can reduce the time to complete second trimester exams.
  • simplifies assessments of the pelvic floor and speeds up exams as well. It automates plane alignment and measurements for high keystroke reduction.
  • They integrated the technology with its Vscan Air CL wireless dual probe. With a flexible, wireless workflow, users gain a wider range of motion.
  • FetalHS simplifies and speeds fetal heart assessments with step-by-step, AI-driven guidance for identifying normal fetal heart anatomy.

Conclusions and Opinions

  • With proven time-saving AI-driven applications, and advanced automation features that simplify exams, the technology can help enhance ease of use and provide clearer images, helping clinicians power through demanding workflows faster while delivering greater consistency and accuracy, ultimately helping deliver better health outcomes for women.
  • The new Voluson Signature series features innovative tools clinicians can rely on, and the automated functions help reduce work stress and improve workflows
  • A new era of ultrasound scanning.

Here the link to the article of +MASS DEVICE:

HealthTech Industry Update | Artificial Intelligence in the Medical Imaging Technology

The integration of Artificial Intelligence (AI) into medical imaging has guided in an era of transformation in healthcare.

The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis.

Advancements in medical imaging and artificial intelligence (AI) have ushered in a new era of possibilities in the field of healthcare. The fusion of these two domains has revolutionized various aspects of medical practice, ranging from early disease detection and accurate diagnosis to personalized treatment planning and improved patient outcomes.

Medical imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) play a pivotal role in providing clinicians with detailed and comprehensive visual information about the human body. These imaging modalities generate vast amounts of data that require efficient analysis and interpretation, and this is where AI steps in.

Technological Innovations

Mathematical models and algorithms stand at the forefront of scientific exploration, serving as powerful tools that enable us to unravel complex phenomena, make predictions, and uncover hidden patterns in vast datasets. These essential components of modern research have not only revolutionized our understanding of the natural world but have also played a pivotal role in driving technological breakthroughs that open up numerous application possibilities across various domains.

The synergy between mathematical models and algorithms has not only enhanced our understanding of the world but has also been a driving force behind technological advancements that have transformed our daily lives.


Convolutional Neural Networks (CNNs) are well suited for grid-like data, such as images, where local patterns can be captured efficiently. However, they struggle with sequential data because they lack a mechanism for modeling dependencies between distant elements (for example, in distinct time instants or far in the image).

Also, CNNs do not inherently model the position or order of elements within the data. They rely on shared weight filters, which makes them translation invariant but can be problematic when absolute spatial relationships are important.

Generative Models

Generative models are a class of machine learning models that can generate new data based on training data. Other generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and flow-based models. Each can produce high-quality images.

Deep Learning Techniques and Performance Optimization

Medical imaging techniques are based on different physical principles, each with their benefits and limitations. The ability to deal with such diverse modalities is also an important aspect to be addressed by AI.


AI-based imaging techniques can be divided in eight distinct categories: acquisition, preprocessing, feature extraction, registration, classification, object localization, segmentation, and visualization. These can also be organized in the clinical process pipeline broadly encompassing prevention, diagnostics, planning, therapy, prognostic, and monitoring. It is also possible to focus on the human organ or physiological process under focus.

Medical Image Analysis for Disease Detection and Diagnosis

Medical image analysis for disease detection and diagnosis is a rapidly evolving field that holds immense potential for improving healthcare outcomes. By harnessing advanced computational techniques and machine learning algorithms, medical professionals are now able to extract invaluable insights from various medical imaging modalities.

Artificial intelligence is an area where great progress has been observed, and the number of techniques applicable to medical image processing has been increasing significantly. In this context of diversity, review articles where different techniques are presented and compared are useful.

The role of AI in facilitating the analysis of large-scale retinal datasets and the development of computer-aided diagnostic systems is also highlighted.

However, AI is not always a perfect solution, and the challenges and limitations of AI-based approaches are also covered, addressing issues related to data availability, model interpretability, and regulatory considerations.

Imaging and Modeling Techniques for Surgical Planning and Intervention

Imaging and 3D modeling techniques, coupled with the power of artificial intelligence (AI), have revolutionized the field of surgical planning and intervention, offering numerous advantages to both patients and healthcare professionals.

By leveraging the capabilities of AI, medical imaging data, such as CT scans and MRI images, can be transformed into detailed three-dimensional models that provide an enhanced understanding of a patient’s anatomy. This newfound precision and depth of information allow surgeons to plan complex procedures with greater accuracy, improving patient outcomes and minimizing risks and AI-powered algorithms can analyse vast amounts of medical data, assisting surgeons in real-time during procedures, guiding them with valuable insights, and enabling personalized surgical interventions.

Image and Model Enhancement for Improved Analysis

Decision-making and diagnosis are important purposes for clinical applications, but AI can also play an important role in other applications of the clinical process.

In complex healthcare scenarios, it is crucial for clinicians and practitioners to understand the reasoning behind AI models’ predictions and recommendations.

Medical images often suffer from noise, artifacts, and limited resolution due to the physical constraints of the imaging devices. Therefore, developing effective and efficient methods for medical image super-resolution is a challenging and promising research topic, searching to obtain previously unachievable details and resolution.

Medical Imaging Datasets

Numerous advancements outlined above have arisen through machine learning public challenges. These initiatives provided supporting materials in the form of datasets (which are often expensive and time consuming to collect) and, at times, baseline algorithms, contributing to the facilitation of various research studies aimed at the development and evaluation of novel algorithms.


Cutting-edge techniques that push the limits of current knowledge have been covered in this editorial. For those focused on the AI aspects of technology, evolutions have been reported in all stages of the medical imaging machine learning pipeline.

The field of medical imaging and AI is evolving rapidly, driven by ongoing research and technological advancements. Researchers are continuously exploring novel algorithms, architectures, and methodologies to further enhance the capabilities of AI in medical imaging. Additionally, collaborations between clinicians, computer scientists, and industry professionals are vital in translating research findings into practical applications that can benefit patients worldwide.

By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Here the link to the full article in Bioengineering (Basel) from Luís Pinto-Coelho

Thaumatec HealthTech Industry Update | How Can Healthcare Ensure Responsible AI Use?

Executives from across the industry shared their thoughts on how the healthcare sector can ensure its use of AI is ethical and responsible during the HIMSS24 conference, which took place in Orlando. Below are some of the most notable ideas they shared.

Collaboration is a must

While the healthcare industry lacks a shared definition for what responsible AI use looks like, there are plenty of health systems, start-ups and other healthcare organizations that have their own set of rules to guide their ethical AI strategy. Healthcare organizations from all corners of the industry must come together and bring these frameworks to the table in order to come to a shared consensus for the industry as a whole, he explained.

Start with use cases that have low risks and high rewards

Currently, there are still many unknowns when it comes to some of the new large language models hitting the market. That is why it is essential for healthcare organizations to begin deploying generative AI models in areas that pose low risks and high rewards. Using generative AI to generate a summary of a patient’s hospital stay and prior medical history isn’t very risky, but it can save nurses a lot of time and therefore be an important tool for combating burnout.

Trust is key

Generative AI tools can only be successful in healthcare if their users have trust in them. Because of this, AI developers should make sure that their tools offer explain-ability. For example, if a tool generates patient summaries based on medical records and radiology data, the summaries should link back to the original documents and data sources. That way, users can see where the information came from.


Healthcare leaders “sometimes expect that the technology will do more than it is actually able to do”  but  AI is not a silver bullet for healthcare’s problems.  To not get stuck in this trap, think of AI as something that serves a supplementary or augmenting function. AI can be a part of the solution to major problems like clinical burnout or revenue cycle challenges, but it’s unwise to think AI will eliminate these issues by itself.

Here the full Article by MedCity News:

HealthTech Industry Update | Are Healthcare Professionals Ready for AI ?

The modern healthcare environment will require ambidextrous experts in healthcare and AI.

As the artificial intelligence (AI) market grows to over $400 billion by 2027, the demand for professionals with expertise in machine learning is increasing as AI technology rapidly evolves. Within the healthcare sector, a deep knowledge of the clinical sciences and healthcare skills will no longer be enough.

Moving ahead, healthcare corporations and academia must empower ambidextrous healthcare professionals (HCPs) with expertise in both machine learning and health science to stay innovative and competitive.

What to do ?

Develop ambidextrous skills in ongoing study and research

Large healthcare companies are exploring ways to integrate AI within their businesses to drive greater efficiencies across different R&D and commercial processes and workflows. With a shift towards corporations placing outsized value on an ambidextrous skillset, HCP graduates are dedicating more focus on AI in their study and research.

Empower ambidextrous professionals to lead interdisciplinary healthcare teams

Interdisciplinary teams that empower ambidextrous AI and healthcare professionals as leaders and project owners will realize their full potential. Their leadership can help fill the gaps in knowledge and communication between team members and drive significant efficiencies.

For HCPs, their lack of technical expertise may lead to an overestimation of AI capabilities, causing a mismatch between expectations and technical realities. For machine learning experts, insufficient knowledge of healthcare can be a barrier to identifying the right problems to solve with AI, resulting in misdirected initiatives and misallocated resources.

Find and cultivate ambidextrous talent in corporate and academic environments

Identifying ambidextrous expertise in health medicine and AI is not easy. Healthcare professionals are specialized experts that take years to train. Few universities have programs that offer training in AI. This is a challenge that the life sciences industry and academia can solve together.

Ambidextrous professionals will drive AI innovation in healthcare

With the right ambidextrous people in the right roles, corporations can further innovation in AI and healthcare. Companies must place a higher emphasis on recruiting and developing ambidextrous experts. The ability to empower and retain such experts and leaders will not only alter the course of their business, but also increase their relevance as AI rapidly transforms our modern economy.

Here the full Article of the MedCity News: AI Disruption is Coming. Are Healthcare Professionals Ready?

‍Thaumatec Knowledge Guide | Flex PCBs in Medical Device Applications

Flexible printed circuit boards can be considered the gymnasts of electronic components. Unlike their rigid counterparts, they possess a remarkable ability to bend and adapt to different shapes, enabling more versatile designs in electronic devices. These boards are made from electrically conductive materials, allowing seamless connections between various electronic components. The flexibility of these PCBs opens the door to a new era of design possibilities.

In essence, flexible PCBs offer a dynamic and adaptable foundation for the intricate electronic systems that power our modern world.

Advantages of Flexible PCBs

Flexible printed circuit boards bring a myriad of advantages to the table, making them a preferred choice in modern electronics.

  • Flexibility and Adaptability
  • Space-saving Marvels
  • Durability and Reliability
  • Seamless Connectivity

What Are Flexible PCBs Used For?

In the changing world of medical devices, the role of flexible rigid printed circuit boards is crucial. These small electronic powerhouses help make devices smaller, meeting the evolving needs of the industry.

  • Medical Wearables: Flex PCBs find application in a variety of wearable health-tracking devices, such as blood glucose monitors, body temperature monitors, blood pressure monitors, heart monitors, etc.
  • Implantable Medical Devices: These refer to devices designed to be inserted into the human body, benefiting from the flexibility of PCBs for integration. Flex PCBs are used in various implantable medical devices such as pacemakers, neurostimulators, implantable cardiac defibrillators, and cochlear implants.
  • Hearing Aid Devices: Flex PCB designs enable the integration of microphone, digital signal processing (DSP), and battery components into a compact unit that fits discreetly behind the ear.
  • Diagnostic and Medical Imaging Equipment: such as ultrasound machines, MRI scanners, CT scanners, X-ray machines, and radiation treatment. The flexibility of Flex PCBs allows for compact and lightweight designs, which are crucial for portable and handheld diagnostic devices.
  • Remote Patient Monitoring Devices: Notable instances of remote patient monitoring devices leveraging flex PCBs comprise wireless blood glucose monitors and wearable ECG sensors.
  • Endoscopic and Minimally Invasive Surgery Devices: Iin endoscopic cameras, catheters, and other minimally invasive surgical instruments the flexibility enables the creation of small, lightweight, and highly manoeuvrable devices that can navigate through the body’s intricate pathways with ease.

Overcoming Design Challenges

Flexible Printed Circuit Boards navigate and conquer unique design challenges, offering solutions that traditional rigid PCBs struggle to address.

  • Compact Design Challenge
  • Environmental Challenges
  • Integration in Small Devices
  • Reducing Complex Wiring
  • Conforming to Unique Shapes

Future Trends and Innovations

As technology advances, the trajectory of Flexible Printed Circuit Boards (Flexible PCBs) points towards exciting trends and innovations that will shape the future of electronic devices.

  • Internet of Things (IoT) Connectivity
  • Miniaturization and Integration
  • Advanced Materials
  • Biocompatible Applications
  • 3D Flexible PCBs
  • Stretchable Electronics


Flexible printed circuit boards emerged as the key to revolutionizing electronic design. Their adaptability addresses real-world challenges, making them indispensable in medical IoT applications.

Future trends forecast even smaller, more versatile boards, promising a dynamic landscape in electronics.

Flexible PCBs are not merely components but architects of a connected, flexible future.

As we navigate this ongoing journey, the potential for innovation remains boundless, ensuring that flexible PCBs continue to play a transformative role in the ever-evolving realm of electronic devices.

Here the full article from TechnoTronix:

HealthTech knowledge guide | How Embedded IoT Medical Devices work

With the proliferation of the Internet of Things (IoT) devices, there has been a huge transformation in terms of smart cities, connected manufacturing, wireless communication, and connected healthcare.

Embedded medical devices reduce the time to diagnose and treat patients effectively since these systems run on a high-speed processor with rich operating system interface.

These devices store data of each patient on the cloud and use them for different analysis and diagnosis purpose on a repetitive basis, decreasing the overall treatment turnaround.

How Embedded IoT Medical Devices Work

IoT medical devices work by connecting to different hardware for the diseases examination purpose. The device system has a touchscreen interface for users to input data for analysis and processing.

As a user inputs data related to the diseases, the system looks for symptoms pre-loaded into the file and tries to match with the provided input. If the match is found with the pre-loaded symptoms, the system responses with the disease name and generates a prescription for general medicine.

In case of a partial or no match, the system notifies to undergo a different test based on the input given by the user and pre-loaded file matching to identify the exact disease and provide prescription accordingly.

Prescriptions and other important details are stored on the cloud-based database management solution which can be used for future analysis. This patient information stored in the cloud can be also used for different analysis.

If proper disease information cannot be found by the given input and other tests performed, the system contacts the Doctor with the given information.

Workflow of IoT Medical Devices


The user will provide the input via a touchscreen panel for the symptoms into Embedded Medical Device. The user also needs to provide all the personal information such as name, contact number, age, etc. Then, the embedded device will return generic medicine prescription for the disease found based on the input or contact the Doctor if the disease is not found.

Embedded Medical Device with IoT:

The embedded medical device receives inputs from the user to match the symptoms with a pre-loaded symptom file and tries to find the matching disease for same. It performs tests suggested based on the pre-loaded symptom file to get the exact match for the disease if the disease is not found by examining the symptoms. If the disease information is not found the system involves the doctor with the given information, who will consult the user, diagnose the disease and accordingly update the symptom file and disease file in the system.

Sensors used in Connected Healthcare Device:

  • Glucometer
  • Temperature Sensor
  • Blood Pressure Sensor
  • Airflow Sensor
  • ECG Sensor
  • EMG Sensor

Prescription for General Medicines:

Based on the input provided by the user, once the disease is found by the embedded medical device, it will look for generic medicine information in the pre-loaded prescription file, mapping the disease and medication.

Cloud Database Management System:

In this stage, the embedded device will store all the user details in the cloud database. This cloud-based solution can store the following information for future analysis:

  • User’s personal information
  • Information about symptoms
  • Information about tests performed and their results
  • Information about disease(s) diagnosedInformation about prescription and medication
  • Information about Doctor’s consultation if any
  • Device information from where all data is getting logged
  • Device health information on cloud just to make sure that device is working fine that includes all the sensor status and other basic information

Different analysis reports can be generated based on above information stored in the cloud for future use and preventive actions.


The embedded IoT medical devices are helpful in the area where basic healthcare facilities are not available.

Based on the disease analysis and data stored in the cloud, it helps users cure the diseases on time and also take preventive actions.

However, it is important to know that a continuous network connectivity is required to integrate the medical device with the cloud.

Medical devices must be compliant according MDR, IEC 60601-1/2/6, IEC 62304, 510K and ISO 13485.

Here the full article from einfochips:

Copyrights © Thaumatec 2024