From healthcare to logistics: Intelligent automation redefines digital trust and data control

A groundbreaking review published in Computers explores how intelligent automation, driven by AI, blockchain, and FPGA hardware, is transforming the infrastructure of secure, scalable, and compliant data exchange across healthcare, finance, and cloud-based sectors.

The study, titled “Revolutionizing Data Exchange Through Intelligent Automation: Insights and Trends” by Yeison Nolberto Cardona-Álvarez, Andrés Marino Álvarez-Meza, and German Castellanos-Dominguez, delivers a comparative analysis of emerging technologies enhancing data lifecycle processes and digital ecosystems. Through a systematic literature review of 102 studies from 2020–2024, the authors highlight critical shifts in automation, interoperability, data security, and ethical governance that are redefining digital infrastructures.

What technologies are reshaping data exchange infrastructure?

The study identifies three pivotal technologies - blockchain, field-programmable gate arrays (FPGAs), and artificial intelligence (AI) - as the foundation of next-generation data management systems. Each brings unique strengths to a complex ecosystem under pressure to deliver fast, secure, and scalable services.

Blockchain, often coupled with the InterPlanetary File System (IPFS), is praised for its transparency and tamper-resistance in decentralized environments. Applications in medical records, logistics, and smart agriculture illustrate how blockchain ensures traceability and regulatory compliance while protecting sensitive data from unauthorized access.

FPGAs offer high-performance, low-latency solutions for intensive data processing tasks. In key-value stores, caching systems, and ETL pipelines, FPGA-based architectures accelerate data workflows and reduce delays, making them invaluable in real-time applications like telecommunications and cloud services.

AI, especially when integrated with blockchain via smart contracts and aspect-oriented programming, enables adaptive monitoring, predictive analytics, and policy compliance in dynamic environments. Through advanced frameworks like BlockASP, AI ensures intelligent policy enforcement and behavior verification across decentralized networks.

Together, these technologies underpin secure, fast, and auditable data exchanges that can flexibly respond to diverse demands across sectors such as healthcare, finance, logistics, and industrial IoT.

What barriers do current data exchange systems still face?

Despite technological leaps, critical barriers persist. Chief among them are:

• Data security and privacy vulnerabilities, intensified by growing cyber threats;
• Interoperability gaps, arising from mismatched standards and system architectures;
• Scalability concerns, particularly in high-throughput environments;
• Regulatory compliance complexities, driven by evolving global privacy laws like GDPR and HIPAA.

The study emphasizes that while intelligent automation can mitigate many of these challenges, it must be coupled with robust governance frameworks, explainable AI models, and adaptive infrastructure to meet the expectations of data integrity, traceability, and ethical use.

Real-time environments, such as federated learning networks and healthcare monitoring, demand ultra-low latency and fault-tolerant systems. Technologies like Apache Kafka and smart ETL pipelines are explored for their ability to handle large-scale, distributed data streams with minimal delay. Yet, these platforms face trade-offs between consistency, performance, and security that require further refinement.

Furthermore, achieving interoperability, especially across sectors like healthcare, remains a stubborn challenge. Studies within the review note difficulties in aligning semantic metadata and heterogeneous data models despite standards like HL7 FHIR and Structured Data Capture. Semantic AI engines and ontology-based schema matching are highlighted as promising tools to automate this alignment and reduce human overhead.

How can intelligent automation ensure compliance and ethical oversight?

The integration of automation with regulatory and ethical frameworks is essential for long-term sustainability and trust in digital systems. The study highlights several advances:

• Zero-knowledge proofs (ZKPs) and self-sovereign identities are being deployed for privacy-preserving, traceable data sharing.
• Smart contracts enforce user consent and policy alignment autonomously, ensuring compliance with GDPR and other global data laws.
• AI-driven policy engines dynamically adapt data handling procedures to meet evolving legal requirements, reducing the compliance burden on human administrators.

Notably, data exchange technologies are increasingly being guided by global governance models such as the OECD AI Principles and the NIST AI Risk Management Framework. These frameworks prioritize fairness, transparency, accountability, and data minimization. The study notes a growing emphasis on ethical AI design, especially in sectors like healthcare, where algorithmic bias and explainability have direct implications on patient safety and equitable access to services.

Additionally, the authors underscore the need for sustainable and socially acceptable data systems. From anonymization protocols to dynamic consent models, the research advocates for an ethics-first approach in designing secure and inclusive data infrastructures.

Future research must focus on creating adaptive frameworks that can self-regulate based on policy shifts, workload fluctuations, and real-time risk assessments. Interdisciplinary collaboration between technologists, legal experts, and policymakers is essential to shape the next generation of data infrastructures that are not only high-performing but also ethically robust and legally compliant.