Navigating the next generation of healthcare with MedTech

MedTech has a long history, evolving from ancient papyrus records and early inventions like the stethoscope and X-rays to modern innovations like minimally invasive surgeries and high precision in vitro diagnoses.

In the last 20 years, breakthroughs in MedTech have transformed patient care in myriad ways. For instance, early cancer detection through protein biomarkers imagery gives additional time for intervention and treatment, considerably improving the overall recovery rate.

Non-invasive prenatal testing methods help analyze fetal DNA from maternal blood samples, reducing the need for invasive procedures and detecting early genetic abnormalities. Prosthetic limbs equipped with neural sensors provide real-time feedback and enable patients to sense temperature, movement and pressure, resulting in better adoption and improved well-being.

How technology is shaping the future of MedTech?

As MedTech infiltrates deep into the healthcare ecosystem, we believe that the following trends and applications will gain more and more traction:

Telemedicine and digital therapeutics: As more clinics offer telehealth services, healthcare will become more affordable and accessible. MedTech devices will collect health data (such as temperature, heart rate, oxygen level or insulin level) and biometrics in real-time, enabling patients to monitor and treat their conditions at home.
Healthcare data storage and exchange: Standards like HL7 (Health level 7) and FHIR (Fast Healthcare Interoperability Resources) are driving data exchange between new applications and legacy medical systems. Meanwhile, cloud solutions boost collaboration, data transfer and data-driven decision-making.
Cybersecurity and data protection: Concepts like chain of custody (CoC) will be more prevalent when discussing protecting sensitive health information. By monitoring every stage from generation to collection, processing, storage, analysis, interpretation and transfer, systematic processes like CoC eliminate potential unauthorized access, ensure data protection and mitigate data loss risks. The CoC is particularly important in strengthening data chains in the Internet of Medical Things (IoMT) field.
Artificial intelligence: We foresee the development of AI models for enhanced diagnosis, disease progression prediction, optimization of treatments, discovery of new remedies and drugs, and reduction of adverse effects.
3D printing: MedTech will combine the power of AI with the versatility of 3D printers to create prosthetics, personalized bone implants and spinal implants. AI models (developed after analyzing patient’s CT scans) can design custom implants and a 3D printer can bring this model to life respecting important parameters like bone density or porosity, resulting in better patient fit, increased adoption rates, and reduced complications.

Translating MedTech into tangible gains for the patients

Let’s look at MedTech applications through some real-world scenarios!

We are witnessing innovations such as specialized clothing—socks, vests, and T-shirts—integrated with wearable technology seamlessly embedded into the fabric. These clothes can monitor blood pressure, blood flow, lung fluid, body pose and body movement.

The data gathered through these devices can help predict trends. Predictions can be validated by a health professional, allowing for treatment adjustments or exploring new care approaches without needing a visit to a healthcare facility.

Imagine a patient with high blood pressure using a wearable device that tracks key health metrics like blood pressure, heart rate, activity, sleep, and stress in real time. This data feeds into a digital therapeutic platform that provides personalized lifestyle recommendations and allows doctors to adjust treatments based on real-time insights.

The platform enables continuous monitoring, helping the patient take control of their health by understanding triggers and making informed lifestyle changes, such as quitting smoking after observing its impact on blood pressure. Physicians benefit from reliable data trends for precise treatment adjustments.

At the same time, remote monitoring reduces healthcare costs by preventing complications and minimizing the need for in-person visits—especially valuable for patients in remote areas or with mobility issues.

Why leverage AI for support and maintenance of Computed Tomography?

Here are some of the tangible benefits:

Medical imaging diagnostics: Early detection of diseases through imaging-based analysis.
Sentiment analysis: Predictive health diagnosis, particularly for mental health and addiction management.
Predictive diagnosis: Using EHR data and wearable biometrics to predict diseases and their progression.
Data-driven discovery: Extracting insights from EHRs, research papers, and clinical trials for diagnostics and drug discovery.
Automated workflows: Streamlining administrative processes.
Chatbot assistance: Preliminary patient interaction through AI-driven chatbots.
Quality insights: Analyzing patient feedback for sentiment-based insights on care quality.

If we apply artificial intelligence to the findings of a computed tomography scanner, it will help minimize downtime and ensure operation reliability and diagnostic accuracy.

Let’s imagine an AI-powered diagnostic system that compares real-time performance data with historical data from the machine and monitors changes in temperature, vibrations, radiation emissions and other metrics.

The system then sends the data to a central maintenance platform where any deviations are notified with the likely causes. It then plans maintenance activities proactively based on the deviation patterns, reducing performance degradation.

The same data anomalies, such as system calibration errors and image quality deterioration, are automatically flagged. Similar algorithms can also predict the failure of key components. The same system can be further customized to track equipment performance remotely, minimizing the need for service visits.

Here are some of the tangible benefits of Computed Tomography:

Constant monitoring of radiation levels and image quality supports increased system reliability and assures the safety and accuracy of medical investigations.

Analyzing the performance of various prevents unexpected CT failure and allows optimized scheduling of maintenance activities at lower costs and with increased efficiency. It can also help predict the entire lifespan of critical components.

The ability to read and manipulate data remotely reduces the number of physical visits required for machinery diagnosis and increases the speed of diagnosis and decision-making.