Next-gen point-of-care tests harness AI to strengthen global health security

AI brings its own validation challenges. Unlike static assays, machine learning algorithms evolve as they ingest new data, meaning that performance can change after regulatory approval. This dynamic nature raises critical questions: How can AI-driven diagnostic tools maintain accuracy over time? Who is accountable for model drift, the developer, the manufacturer, or the regulatory body?

The next decade will redefine how infectious diseases are diagnosed and managed globally. A paper by Moustafa Kardjadj, from the CRO Division of Dicentra in Toronto, Canada, highlights how artificial intelligence (AI), microfluidics, and biosensors are transforming point-of-care (POC) diagnostics, enabling faster, more accurate detection while raising urgent questions about regulation, validation, and data integrity.

Published in Diagnostics, the study “Advances in Point-of-Care Infectious Disease Diagnostics: Integration of Technologies, Validation, Artificial Intelligence, and Regulatory Oversight” brings together the latest developments in POC testing technologies, their integration with AI systems, and the policy mechanisms that will determine their safe global deployment.

Revolution in point-of-care technology: The convergence of AI and diagnostic innovation

The global diagnostics market, valued at USD 53.1 billion in 2024 and projected to approach USD 100 billion by the early 2030s, is undergoing structural transformation. Infectious disease POC testing, covering viral, bacterial, and parasitic infections, now represents a rapidly expanding frontier. Kardjadj notes that post-pandemic acceleration in decentralized testing has spurred demand for portable, automated, and digitally connected devices that can deliver accurate results without laboratory infrastructure.

This transformation is powered by immunoassays, nucleic acid amplification tests (NAATs), microfluidic lab-on-chip platforms, and CRISPR-based diagnostics. These technologies have dramatically reduced turnaround time and improved accuracy. AI now acts as an analytical core, embedded across platforms to interpret visual assays, quantify signals, and predict infection patterns in real time.

For instance, AI-driven smartphone readers can interpret faint test lines, detect false negatives, and transmit results directly to healthcare systems. This digital integration minimizes human error and enables automated epidemiological surveillance, allowing public health agencies to detect outbreak trends faster than ever before.

However, the study points out that technological capability must be matched by robust validation and regulatory governance. Without harmonized oversight, the rapid proliferation of unverified AI-enabled devices could threaten diagnostic reliability and patient safety.

Validation and reliability: The crucial test for AI-enabled diagnostics

While the diagnostic ecosystem has embraced innovation, clinical validation remains the Achilles’ heel of POC expansion. Many emerging devices are tested only under controlled laboratory conditions, not in the varied environments where they are ultimately used, rural clinics, mobile units, or outbreak zones.

According to the author, reliability in decentralized testing depends on analytical validation, clinical performance evaluation, and usability studies that assess operator variability. The review identifies persistent gaps in sample diversity, population representativeness, and post-market surveillance, especially for AI-powered systems.

AI brings its own validation challenges. Unlike static assays, machine learning algorithms evolve as they ingest new data, meaning that performance can change after regulatory approval. This dynamic nature raises critical questions: How can AI-driven diagnostic tools maintain accuracy over time? Who is accountable for model drift, the developer, the manufacturer, or the regulatory body?

The author calls for continuous, lifecycle validation frameworks that monitor device performance in real-world conditions. Kardjadj highlights the need for multi-center, multi-population clinical trials, particularly in low- and middle-income countries where disease burden is highest. Without equitable data representation, AI algorithms risk encoding bias that could lead to diagnostic disparities.

The review also discusses the growing role of standardized reference materials, digital quality control, and human factors engineering in ensuring test reliability. These measures aim to bridge the gap between laboratory accuracy and field-level consistency, especially when devices are used by non-specialist healthcare workers.

Regulation, ethics, and the future of diagnostic oversight

The study maps the evolving oversight landscape across jurisdictions, the U.S. Food and Drug Administration (FDA), the European Union’s In Vitro Diagnostic Regulation (IVDR), and the World Health Organization’s Prequalification Programme (WHO PQDx), each introducing new criteria for AI-integrated diagnostic systems.

In the United States, the FDA’s 510(k) and De Novo pathways, coupled with Clinical Laboratory Improvement Amendments (CLIA) waivers, form the backbone of device approval. These frameworks assess analytical validity, clinical performance, and user safety. Yet, AI diagnostics challenge these models by introducing algorithmic opacity, the so-called “black box” problem, where decision-making logic is not fully transparent to regulators or clinicians.

The European IVDR imposes more stringent clinical evidence requirements and post-market surveillance obligations, but manufacturers face increased costs and delays due to overlapping assessments. Kardjadj argues that a harmonized global approach is urgently needed to align AI diagnostic standards, reduce duplication, and accelerate innovation while maintaining patient protection.

Ethical governance is another key concern. The review highlights that data security, algorithmic bias, and informed consent are now integral to medical device compliance. Since many AI-enabled tests rely on cloud-based analysis, ensuring data privacy and cross-border data transfer compliance is critical. Kardjadj advocates for transparent data-sharing policies and patient-centered consent frameworks that empower individuals while supporting global surveillance efforts.

Furthermore, reimbursement policies lag behind innovation. Even when AI-enhanced devices are approved, unclear coverage decisions by insurers and public health agencies slow adoption. The author recommends integrating POC reimbursement into value-based healthcare models, linking financial incentives to improved diagnostic accuracy and public health outcomes.

Public health impact: From rapid response to global preparedness

The broader impact of AI-integrated POC diagnostics extends beyond individual patient care. These technologies are becoming cornerstones of global health security. Kardjadj details how AI-supported testing has enhanced surveillance and response in recent outbreaks, COVID-19, Ebola, malaria, HIV, and influenza, by enabling early case detection, faster isolation, and real-time transmission tracking.

POC testing decentralizes healthcare delivery, especially in resource-limited regions, where laboratory infrastructure is scarce. Portable devices and smartphone connectivity bridge diagnostic gaps by linking field results directly to national health databases. This connectivity supports precision epidemiology, allowing health authorities to deploy targeted interventions and allocate resources efficiently.

AI also enhances quality assurance. Real-time feedback systems can flag operator errors, environmental anomalies, or reagent degradation, ensuring continuous performance monitoring. By integrating diagnostics with telemedicine and digital health platforms, POC testing empowers local healthcare workers to make informed clinical decisions and refer complex cases promptly.

The study notes that sustained impact will depend on interoperability and scalability. Data produced by POC devices must feed seamlessly into global surveillance networks, such as those coordinated by WHO and the Centers for Disease Control and Prevention (CDC). Kardjadj warns that fragmented data ecosystems could limit the collective power of diagnostics in managing cross-border outbreaks.

Moreover, equity remains central to long-term success. The author stresses that AI-enabled diagnostics must not deepen the digital divide. Instead, they should be deployed with a focus on affordability, accessibility, and training to ensure inclusive benefits.

The path forward: Integrating innovation with oversight

The fusion of AI, biosensors, and molecular technologies is reshaping the frontlines of healthcare, but success will hinge on scientific rigor, regulatory coordination, and ethical governance.

To achieve this balance, the author proposes a three-tier strategy:

• Strengthen validation pipelines by embedding real-world testing, human-factor evaluation, and continuous monitoring into approval processes.
• Advance global regulatory convergence to reduce duplication and create shared AI oversight mechanisms across FDA, IVDR, and WHO.
• Build data-driven public health infrastructure that integrates AI-powered diagnostics into surveillance, research, and pandemic preparedness.

If implemented, these measures could transform diagnostics from a reactive tool into a proactive defense system, detecting infections at the community level before they escalate into epidemics.