Biosensors are making a big impact in healthcare, blending biological detection with smart analytics to deliver real-time diagnostics and personalized care. From wearable glucose monitors to cardiac trackers, these devices are helping people manage chronic conditions more effectively—and in many cases, reducing hospital visits altogether. But with this rapid progress comes a new challenge: regulation.
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As biosensors become more advanced, the frameworks meant to keep them safe and ethical are struggling to keep up. Data privacy, algorithmic bias, and international compatibility are just a few of the growing concerns.
In this article, we’ll break down how regulators are responding, where the gaps still exist, and why it all matters for patients, clinicians, and innovators alike.
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Who’s Regulating What?
Let’s start with the basics: who’s in charge of making sure biosensors are safe?
The oversight of biosensors involves a complex interplay of regional and international regulations. In the United States, the Food and Drug Administration (FDA) classifies medical devices into three risk-based categories: Class I, II, and III. High-risk biosensors, such as implantable glucose monitors, must meet strict requirements. The FDA focuses on having devices approved before they hit the market, monitoring them after they are on the market, and following Good Manufacturing Practices (GMP) to ensure safety and effectiveness.1
Over in the European Union (EU), newer regulations like the Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) raise the bar for clinical evidence and tracking. They also introduce a Unique Device Identification (UDI) system to help trace devices throughout their lifecycle.
Globally, there’s the International Medical Device Regulators Forum (IMDRF) working to create some consistency, but many countries—especially in parts of Asia and Africa—still lack robust systems.1
Meanwhile, the EU Artificial Intelligence Act (2026) is gearing up to directly address AI-based biosensors. It’ll require developers to assess algorithmic risk, especially when devices make treatment recommendations.2 This signals a clear shift: regulators aren’t just thinking about hardware anymore—they’re thinking about software ethics too.
The Ethical Gray Areas
It's one thing to talk about ethics in theory, but when biosensors are collecting real-time health data day and night, those concerns become immediate.
Biosensors are now raising important questions about data ownership, consent, and fairness. Take smartwatches and fitness trackers, for example. They gather tons of personal health info, but most users have no idea how that data’s being used—or who it’s being shared with.2
A 2023 study in JMIR Diabetes found that 60 % of diabetes apps sold anonymized user data to advertisers without users’ clear consent.3 That’s a huge red flag for privacy, and it highlights gaps in laws like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA)
It’s also a problem for equity. People with limited digital literacy—often lower-income or older adults—are more likely to use outdated devices or miss the fine print. And when it comes to AI-powered biosensors, underrepresentation of minority groups in training data can lead to diagnostic errors, further deepening health disparities.3
Cultural acceptance matters too. In some communities, implantable sensors raise concerns about autonomy or conflict with religious beliefs. Regulators need to engage with patients, clinicians, and ethicists early in the process to avoid one-size-fits-all policies that don’t actually fit.2,4
When Tech Moves Faster Than the Rules
Let’s be honest—tech doesn’t wait for regulations to catch up, and the rapid advances in biosensor technology are beginning to challenge traditional rules for medical devices.
Wearables like Abbott’s Freestyle Libre 3 now send real-time alerts via Bluetooth, which sounds great... until you realize it introduces a whole new layer of cybersecurity risk. In 2023, the FDA issued draft guidance requiring wireless devices to include encryption and allow over-the-air updates. But many older products weren’t built for that—and retrofitting them isn’t easy.4,5
Then there’s nanotech. Biosensors using materials like graphene can detect stress hormones like cortisol with incredible accuracy. But what happens when those materials break down and linger in the body? Similarly, multiplex biosensors (which detect multiple biomarkers at once) challenge current validation protocols that only test for one substance at a time.
Some regulators are testing out “sandbox” environments—controlled spaces where developers can experiment without jumping through every hoop. It’s a smart move, but critics worry it could delay access to promising devices if not managed well.6,7
To stay flexible, agencies like the FDA are starting to allow adaptive pathways—where approved devices can be updated based on real-world performance data.¹ It’s a step toward keeping up with innovation without compromising safety.1
The Validation Bottleneck
Validation is a huge hurdle in its own right—especially for startups.
Take non-invasive biosensors like sweat-based glucose monitors. Sounds cool, right? But analyte levels can vary a lot between people, so regulators now want multi-site trials to confirm accuracy across diverse populations. That’s great for science, but not so great for startups with limited funding.1,4
Meanwhile, efforts like the FDA’s Interoperable Medical Device Initiative aim to standardize data formats so biosensors can plug seamlessly into EHR systems. But adoption is slow. Many digital health companies are hesitant to share their data, and that creates bottlenecks for clinicians trying to work with multiple tools.
Postmarket surveillance also varies wildly by region. Some countries simply don’t have the infrastructure to track device performance in real time. Coordinated international monitoring could help—if governments are willing to invest in it.5
Data Security: Still a Work in Progress
Biosensors rely heavily on wireless connections and cloud storage, which means cybersecurity is no longer optional—it’s essential.
And yet, 40 % of FDA-approved wearables don’t use robust encryption.2,4 That’s a major concern when these devices are capturing heart rates, glucose levels, and even user locations. Sure, GDPR allows for hefty fines, but enforcement is inconsistent, especially in countries with weak digital governance.
AI-enabled biosensors come with their own risks. For instance, insulin pumps that use adaptive algorithms can be manipulated by malicious code if not properly secured. The FDA’s 2024 guidelines now require penetration testing before launch, but retrofitting older devices remains a logistical nightmare.2,4
To manage risk, hospitals often create segmented networks to isolate vulnerable devices—a decent workaround but far from ideal. Blockchain and federated learning are being explored as longer-term solutions, offering secure logs and decentralized training models. But again, regulation hasn’t caught up yet.2,4
Market Access: Not the Same Everywhere
Reimbursement is another sticking point—and it varies a lot from country to country.
In the US, insurers often demand pre-authorization. In France, devices must pass cost-effectiveness reviews. In many low-income countries, the issue isn’t just approval—it’s affordability and lack of regulatory clarity.1,2
Public-private partnerships, like the WHO’s Access to Biosensors Initiative, aim to close this gap by using tiered pricing. But developers are still hesitant to focus on smaller markets because of unclear reimbursement policies.
Health Technology Assessments (HTAs) could help streamline decisions, but there’s no global standard yet—making it tough to scale innovation equitably.1,2
Looking Ahead: Global Harmonization and Future Directions
If biosensors are going to reach their full potential, global regulatory alignment is non-negotiable. Right now, only 15 countries have adopted the World Health Organization’s Global Model Regulatory Framework—a clear sign of fragmentation. This is especially problematic for clinical trials. A device approved in the EU might need to undergo redundant testing in South Korea, driving up costs and delaying access. The IMDRF is pushing for mutual recognition agreements, but political tensions are slowing things down.1,2
Emerging devices, like biodegradable biosensors, bring new questions around safety and environmental impact. Meanwhile, neuroprosthetics with biosensing capabilities blur the lines between diagnostics and therapeutics—requiring joint oversight from agencies like the FDA and National Institute of Health (NIH).5
Real-world evidence (RWE) is becoming a critical tool for regulators. The EU’s DARWIN project is already collecting data from wearables to support faster approvals, especially for rare diseases. But biases in RWE—like underrepresentation of rural populations—can reinforce existing inequalities unless regulators push for inclusive data practices.6
Conclusion
The future of biosensors looks incredibly promising—but only if regulation keeps up. Right now, we’re at a crossroads. On one side: innovation that can truly transform care. On the other: real risks around safety, privacy, and fairness.
To make it work, regulators, developers, and end users need to work together. That means building harmonized global frameworks, tightening cybersecurity, and designing for inclusion from the start.
Because as biosensors become central to healthcare, the way we regulate them will shape not just innovation—but who benefits from it.
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References and Further Reading
- Navigating the Regulatory Landscape: The Evolution of Medical Device Regulation in the Medtech Industry. MedTech Business Review: Business and Technology Magazine for MedTech. https://www.medtechbusinessreview.com/news/navigating-the-regulatory-landscape-the-evolution-of-medical-device-regulation-in-the-medtech-industry-nwid-199.html
- Bouderhem, R. (2024). Ethical and Regulatory Challenges for AI Biosensors in Healthcare. Proceedings, 104(1), 37. DOI:10.3390/proceedings2024104037. https://www.mdpi.com/2504-3900/104/1/37
- Flors-Sidro, J. J. et al. (2021). Analysis of diabetes apps: a systematic search of apps to assess privacy-related permissions (Preprint). JMIR Diabetes. DOI:10.2196/16146. https://diabetes.jmir.org/2021/1/e16146/
- Kim, J., Campbell, A. S., & Wang, J. (2019). Wearable biosensors for healthcare monitoring. Nature Biotechnology, 37(4), 389. DOI:10.1038/s41587-019-0045-y. https://www.nature.com/articles/s41587-019-0045-y
- Smith, A. A., Li, R., & Tse, Z. T. (2023). Reshaping healthcare with wearable biosensors. Scientific Reports, 13(1), 1-16. DOI:10.1038/s41598-022-26951-z. https://www.nature.com/articles/s41598-022-26951-z
- Chakraborty, T. et al. (2024). Current challenges and future prospects for biosensor application in healthcare. Applications of Biosensors in Healthcare, 731-749. DOI:B978-0-443-21592-6.00003-3. https://www.sciencedirect.com/science/article/abs/pii/B9780443215926000033
- Yunus, G. et al. (2023). Electrochemical biosensors in healthcare services: bibliometric analysis and recent developments. PeerJ, 11, e15566. DOI:10.7717/peerj.15566. https://peerj.com/articles/15566/
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