Journal of Artificial Intelligence and Big Data Disciplines
The Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD) is an international, peer-reviewed, open-access journal that publishes original research, reviews, and case studies in Artificial Intelligence, Machine Learning, Deep Learning, Big Data Analytics, and related emerging technologies. JAIBDD provides researchers, academicians, and industry professionals worldwide with a credible platform to share innovative findings that advance intelligent computing and data-driven solutions. Guided by a rigorous, transparent peer-review process and strong publication ethics, the journal bridges theoretical research with practical, real-world applications — fostering global collaboration and knowledge exchange across the fields of AI and Big Data.
Over the past decade, claims processing has emerged as a primary area of interest for insurers exploring how artificial intelligence (AI) can reduce costs or enhance service quality. Several factors motivate this focus. First, processing claims represents a major operational expense—one that, if reduced, is likely to translate into greater profitability. Second, claims processing often significantly influences the customer experience and may even determine clients’ future purchase decisions. Finally, AI technologies tailored for claims processing have demonstrated the ability to deliver faster settlement of smaller claims without sacrificing accuracy. Provided that AI is applied within a carefully monitored, ethically governed framework, its integration into claims processing represents a genuine win-win opportunity. But increasing the efficiency of claims processing is only half the story. Unless these savings translate into improved customer experience and satisfaction, the long-term gains may be illusory. Customers today demand – and expect – faster claims settlements. Organisations that can consistently meet these expectations are likely to enjoy a competitive edge, gaining not only customer satisfaction but also long-term loyalty. Conversely, companies that fail to deliver speedy settlements will, over time, see an increased rate of customer attrition, particularly among their high-value clients. Insurers that can harness new AI capabilities to manage these twin objectives – faster settlement with greater consistency – stand to capture both the operating and customer experience benefits.
Copyright (c) 2026 Journal of Artificial Intelligence and Big Data Disciplines
Behavioral AI for Adaptive Payment Authorization in Cloud Finance
Cloud-based platforms are increasingly incorporating Payment as a Service (PaaS) that empowers various organizations to swiftly offer payment services to their end users—most in a win-win manner. However, the growing violence and higher money flowing into crime severs the win-win situation and reduces the reception by the organizations. Payment transactions can never be completely anonymized. To keep the cloud-based transaction services flourish in the ecosystem, a technology that tracks criminals’ abnormal behaviors and predicts potential attacks before they happen is essential for sensitive, high-value, and risky transactions. Protecting the merchants and users by incorporating intelligent, machine learning (ML)-powered behavioral analytics for real-time authorization of sensitive transactions is a crucial and hot research topic; and their integrated receiving and sending decision policies should be taken care of in a balanced way. To meet these needs and offer intelligent risk sensing and scoring services, the integration of denser behavioral signals in addition to the transactional and contextual ones is necessary. Various machine-learning-anomaly-detection algorithms are compared to select the most suitable model. Moreover, denser behavioral signals are searched and experimented with to attain ML models of higher accuracy. The normal classes are defined by both supervised learning for benign actions of authorized users and by semi-supervised clustering with a few labeled instances only for malicious actions of non-users. The models are label-free in the testing phase. Enriching high-risk monetary transfer authorization with intelligent decision-making is accomplished by ML-based risk scoring, where pleasing sensitivity and specificity are achieved through careful calibration of the risk scoring threshold.
Copyright (c) 2025 Journal of Artificial Intelligence and Big Data Disciplines
Clinical Decision Support systems based on historical patient data represent a model for investigation of a category of Clinical Decision Support (CDS) applications that have recently gained traction — consumption of historical data to support clinical decision making. An overview of the category is provided and addressed based on five research questions: What are the properties of historical data that provide insights? How are these insights used in Analytical Methods for Clinical Decision Support based on Historical Data? How is the architecture of the Clinical Decision Support System represented? How is clinical validation and evaluation achieved? How is the presentation and user interaction with predictions controlled? Despite being a nascent area, numerous published works are available on the topic, and clinical validation efforts are in various stages of maturity. Historical data have become increasingly more available, are generally regarded as safe, and, unlike predictive models constructed on Future Data, do not raise questions regarding biases introduced in the construction of supervised learning models. However, while safety and bias concerns are reduced, historical data are prone to their own logical shortcomings. Therefore, users must exercise caution when interpreting the insights derived from Historical Data Analytics. Properly defined and presented, such insights do not impose a decision making burden on clinicians but instead ease the decision making process. The existence of a predictive model that maps the Clinical Data relevant to a Clinical Decision to the Clinical Decision itself is often regarded as a requirement for CDS to be genuinely useful for everyday clinical practice. Such a statement suffers from circular reasoning. In summary, because no method of construction of predictive models can claim to be free from bias, the pannational nature of probability suggests that, provided sufficient Causally Uncorrelated Data points are available notwithstanding time, space and biological differences, the possibility of identifying and using a Supervised Learning Model that accurately predicts the Decision of a Clinician can not be excluded.
Copyright (c) 2024 Journal of Artificial Intelligence and Big Data Disciplines
Cross-OEM Federated Learning for Autonomous Driving
The growing interest in autonomous vehicle technology presents opportunities for manufacturers to pursue plans based on machine learning to develop their own self-driving solutions while maintaining their competitive edge. Collaborative artificial intelligence presents manufacturers with the possibility of cooperation without sharing private user data. Federated learning is a distributed machine-learning framework that enables multiple parties to cooperate on a common model while training locally on their own private data. Despite the similarities in the underlying technology, the application of federated learning for autonomous driving has attracted limited interest. Numerous challenges arise and need to be addressed: Different autonomous driving systems develop independently, and hence their models differ significantly. Vehicle manufacturers may use proprietary data formats that ensure the privacy of user data, and the necessary training data remain in the manufacturer domain. Communication efficiency is critical, as the resources of vehicles are constrained compared with fixed servers. Training data remain local, and agent-specific knowledge cannot be transferred directly. Collaborative autonomous driving powered by federated learning could fill the perceptual gaps during periods of limited visibility and ensure the safe crossing of busy intersections. A federated-learning framework has been developed that integrates several key components and techniques for training across a multi-manufacture federated vehicular network. It transmits only key information that preserves the privacy of the drivers while enabling safe autonomous driving in a limited-visibility environment. Powers and constraints differ across manufacturers, so establishing a top-level data flow is also necessary.
Governed RAG-Agent Architecture for Autonomous Enterprise Decisions
Enterprises seek to harness emerging technologies such as generative AI to enable decision automation in business operations while maintaining governance, risk, and oversight. Existing architectures for building Autonomous Agents in Information Systems work well for narrow-band decision problems but fall short when adapting generative AI methods to solve problems requiring high governance and compliance risk mitigation. Existing ServiceNow implementations operate at ChatOps levels of automation but lack the otherwise available major platform functionality. Governance-Embedded Action-Policy Decision Intelligent architecture provides an end-to-end pipeline, embedded policy enforcement, built-in audit/traceability, and governance oversight of decoupled/parameterized ServiceNow Decision Intelligent Workflows. The concept is being explored and tested in a large enterprise with the necessary conditions for success. The initial focus is business-process auto-detection and classification, with connector integration into enterprise systems such as global knowledge repositories, tracking systems, and data quality monitoring. These endpoints provide situational updates for Business-Process Decision Intelligence reflex workflows.
AI-Based Liquid Biopsy for Reproductive Precision Medicine
Cell-free DNA (cfDNA) occupies a unique place for various purposes in precision reproductive and preventive healthcare, as a product of metabolism and apoptosis of cells housed in different tissues and organs. Given the hitherto unusual success of deep learning (DL) in the domain of unconstrained problems across scientific fields, its application into cfDNA analytics also appears promising, potentially achieving clinical signals with similar efficiencies. Core principles are defined and linked to cfDNA functions in reproduction, suggesting cfDNA analysis for the achievement of related objectives. In particular, the sought-after conditions for DL introduction are discussed and proposed for the specific case of precision reproductive healthcare, whereby upcoming studies could be designed and assessed using these same elements. CFDNA is a biological fluid with two main clinical applications: sex determination of the fetus and non-invasive prenatal testing (NIPT) of fetal aneuploidies. Although both applications rely on a similar cfDNA approach, they target different clinical signals and rest on different models of CFDA analysis. Nevertheless, four fundamental requirements are common to both applications, conditions that have led to their indeed successful development and implementation. Two core conditions, related to cfDNA biology and the classification of the clinical signal under consideration, seem to be particularly relevant for DL implementation. These aspects are subsequently addressed, and the conclusions provide a perspective on the future of DL in CFDA.
Generative AI for Smart Agri-Equipment Automation and Pharma-Crop Yield Prediction
Generative AI shows promise in automating intelligent field equipment, enhancing the efficiency of physical work in agriculture. Current research in agricultural machinery automation, encompassing both field equipment and robotic systems, has witnessed groundbreaking advancements aimed at addressing the increasing labor shortage in agriculture. Additionally, autonomous vehicle research has achieved considerable deployment within government and industrial robotics programs. Farm machinery automation primarily employs symbiotic, classical, task-oriented AI techniques, while robotic harvesting and retrieval remains application-specific. These systems rely heavily on robust and specialized sensors to support perception, state estimation, mapping, planning, and control for real-time operation in unstructured outdoor environments. The second area influenced by GAI is pharmaceutical crop yield prediction. Yield forecasting systems tailored to the pharmaceutical supply chain for cannabis and hemp production have recently emerged. These systems leverage novel generative AI techniques for modelling crop growth and inferring phenotypic characteristics through data fusion involving multispectral and hyperspectral imaging, alongside 2D, 3D, and 4D imaging. A scalable system architecture for GAI implementation in agricultural applications has been presented, addressing aspects related to data pipelines, associated sensor infrastructure, and an edge device network for reducing latency, bandwidth requirements, and privacy concerns.
Machine Learning Approaches for Flood and Manufactured Housing Insurance Underwriting
Fulfilling a growing need for climate-aware flood risk management of mobile-home insurance portfolios, this research develops AI-powered predictive analytics that quantify risk and associated capital requirements over time. A comprehensive data analysis prepares a rich database integrating historical claims, exposure characteristics, data records of climate drivers, and additional geospatial, socioeconomic, and business-climate indicators. Using proven methodologies of statistical modeling and machine learning, climate-risk patterns across flood frequency, intensity, and inundation depth are interpreted. Predictive outputs are successively integrated into risk pricing and capital models, stimulating innovations in dynamic risk appetite adaptation, product design, geographical concentration, and operational decision-making. Role players are equipped to monitor evolving tail-risk exposures and arrive at well-grounded, real-time choices. Market forces are pressuring insurers to better serve and support selected vulnerable customer segments, particularly mobile-home communities that routinely live under very high concentrations of climate Risk for flood peril, especially in view of changing climatic trajectories. Climate risk landscapes are being reshaped by factors such as increasing precipitation, longer monsoon seasons, and eventually increasing draughts, all of which differ by region and country. Consequently, demand government and social attention on the part of both regulators and portfolio holders. These growing anxiety are leading role-players to call for comprehensive approaches that enable proactive real-time pricing and service decisions adapted to the fortuitous and/or stress-test-data flow.
The semiconductor testing process is a key bottleneck in the chip manufacturing flow. To address this inefficiency, data engineering pipelines have been built in the cloud that unify the ingest of test vectors and rich parametric telemetry for real-time testing and AI-powered diagnosis. New chips are tested using algorithms that can decide whether to release or scrap a chip based on the test results and preceding telemetry. Because clearance decisions are made during test, failed chips can quickly be relabeled or logged without being held in test. Data science pipelines examine failed chips and use the conclusions from these analysis to detect issues in other chips in near real-time. Such AI-powered diagnostics also have the potential to help prevent future recurrences. Acting on these insights can ultimately improve yield and shorten test time for future designs. Data pipelines are about two orders of magnitude less expensive than the test process. They enable continuous decision-making for testing, clearance, and failure analysis without manual steps and facilitate timely action based on telemetry and failed part diagnostics. Making the testing process more efficient can represent significant cost savings for high-volume products. Ultimately, delivering better and cheaper parts to customers faster improves competitiveness and profitability.
Layered Data Fusion for Federated Enterprise Analytics
Federated Insight Engineering Through Multi-Tier Enterprise Data Harmonization presents a framework for Enterprise Data Architecture and Engineering in accordance with the principles of Data Stewardship as developed by the International Data Space Association. The framework adopts Multi-Tier Architecture as a high level design pattern while the underlying engineering foundation is derived from the principles of Data Harmonization. Emerging from these concepts, Distributed Data Layering governs Data Management, Data Governance and Data Quality across the enterprise data landscape and connects enterprises into Dataphores, gradually culminating in Data Spaces. Business Insights are traditionally computed in a close-world scenario with a single Corporate Data Warehouse. In a Distributed Data Layering context, however, Business Insights are engineered by Federated Insight Engineering rather than explicitly queried. In Federated Insight Engineering, technologies such as Distributed Query Planning and Execution, Distributed Hyper-Querying and Integrated Knowledge Graph Maintenance enable the seamless application of Business Analytics across the Data Layers of Lateral Data Partners, thereby discovering and engineering Business Insights.
Contextual Medical Insight Engine for Smart Care Coordination
Context-aware care navigation systems use real-time context information and service-oriented decision-support systems to deliver personalized health and care services. While these systems offer considerable promise to address the growing complexity and fragmentation of healthcare delivery, their real-world implementation is often hindered by poor data quality and interoperability, insufficient reliability and explainability, and lack of scalability and sustainability. Addressing these challenges requires dedicated efforts on four interdependent fronts: (i) establishing the Foundations of Context-Aware Care Navigation; (ii) developing robust Knowledge Fusion technology, particularly within the domain of Medical Informatics; (iii) implementing practical Architectural Frameworks that enable the incremental deployment of real-world use-case scenarios; and (iv) designing and applying suitable Evaluation and Validation Strategies. Health information is a key enabler of innovative context-aware solutions, particularly for the health and care subdomains. A multitude of organizations worldwide process and publish high-quality health information on a daily basis. However, such information is not always readily available when needed due to diverse factors, including the lack of established semantic interoperability between sources. Intelligent Knowledge Fusion technology can serve as a powerful means to mitigate this limitation.
Adaptive Clinical Networks for Real-Time, Risk-Aware Care
Clinical Interaction Networks represent workflows in real-time clinical settings, enabling the generation of up-to-date metrics and risk estimations that monitor the quality and safety of care and provide decision-support capabilities. These networks act as a clinical control tower, providing notifications and recommendations to improve the effectiveness, efficiency, and safety of healthcare delivery. Without the integration of data governance and model feedback loops into their development, the networks risk becoming disconnected from the changing realities in the health environment for which they are designed. In addressing these considerations, a reliable architecture for ensuring data quality, adapting automatically to changes in data streams and clinical processes, and creating mechanisms for timely clinician feedback and validation has been presented. Self-evolving Clinical Interaction Networks enable data-fusion services that process streaming health data, machine-learning approaches that utilise online or continual learning to produce adaptable network structures, and quality-control procedures capable of integrating clinician judgement into the models, thus enhancing their clinical relevance.
Federated insight engineering offers an industry-agnostic approach to federated analytics by exploiting a multi-tiered architecture for enterprise data within the larger FORGE methodology. The concept is rooted in the view of enterprise data landscapes as a collection of data sources susceptible to local analysis and insight extraction. Data sharing between these sources occurs where partners have a common business interest, and these ephemeral partnerships are governed by collaboration agreements. Few industries have yet mastered the art of federated insight engineering. However, regulatory constraints and the privacy paradox mean that lessons learned in heavily regulated environments are applicable—often with minor adjustments—to other domains confronting different but equally pressing motivations. Tourism & hospitality, online marketplace, and finance & insurance exemplify the early movers in federated insight engineering within the analytics supply chain. Industry exemplars highlight not only common patterns of success and failure but also a reference architecture for federated analytics at the paradigm level.
Federated Compliance Intelligence (FCI) for Enterprise Ecosystems
Enterprise service ecosystems have emerged as a promising organizational model that enables companies to deliver integrated products and services by leveraging the capabilities of partners, customers, and third parties. However, the operation of enterprise-grade service ecosystems at scale is hampered by a number of technical and organizational barriers that create operational frictions. These barriers include the lack of trust between ecosystem partners, the existence of data and system silos, a patchwork of divergent policies and regulations, high costs related to compliance, and the presence of heterogeneous and often incompatible data standards. Emerging engineering methods and standards are already addressing a subset of these barriers and can help support federation at the data level, control level, and decision level. A federated perspective enables the specification of protocols and procedures that facilitate compliance-related activities, such as privacy-preserving telemetry and incident response, and allows for the parallel execution of those activities across jurisdictional borders. Future research agendas and practical roadmaps can be aligned with these developments, with specific directions identified for the short term, medium term, and long term.
Enterprise platforms are commonly built using a combination of components sourced from multiple vendors. Nevertheless, synchronization of the respective services continues to rely on the vendors' default solutions, which often expose data to inconsistencies and unavailability. Adding more synchronization mechanisms complicates the design, making operations harder to manage, and increasing the associated costs. These costs may even be higher for hybrid deployments with inter-platform connections. A way to reduce those costs, which also applies to multi-cloud setups, is to turn service synchronization into a feedback-controlled process that adapts to the current situation. Such a feedback-controlled process executes additional synchronization only when necessary and safe. A framework is proposed for adaptive service synchronization across enterprise infrastructure platforms such as cloud, edge, and intercloud. The adaptive synchronization core ensures service safety guarantees when evaluating the need for synchronization and determines the best method to be employed. Supporting elements handle versioning along the service-provided and service-used paths and facilitate the administration of cloud-edge enterprise scenarios involving geographic distribution. Thanks to the modular definition of the adaptation logic, the resulting solution may be customized for the specific characteristics of the platforms involved. Service synchronization addresses the operation of services that can affect each other despite being executed on different platforms or in different clouds. Therefore, service synchronization is a matter of preserving temporal consistency of service-oriented systems. It is a known problem in multi-cloud deployments and has been the subject of study in many research works. However, the addition of a service synchronization solution to the current services exposed by platforms can be rather expensive because it makes the services less available and can incur a performance overhead. Adaptive service synchronization aims, at any given point in time, to synchronize services only when needed or when it can be done without exposing data to an inconsistent state.
Dimitris Plexousakis Dimitris Plexousakis
Linking Providers and Payers for Better Care
The recent COVID-19 pandemic has emphasized the need for resilience in health service delivery. One avenue to support resilience is through an integrated clinical-payer intelligence infrastructure that leverages all data generated from routine clinical practice and operations, not just for service reimbursement or regulatory compliance, but also for real-time service delivery decision support. Current health data usability is limited by barriers in interoperability, privacy, security, clinical validity, and governance. Addressing these barriers through a unified intelligence fabric—encompassing data commons, analytics pipelines, dashboards, and predictive models that span both clinical and payer data—could enhance the resilience of not only individual health systems but also geographically dispersed systems serving the same population. The continuity of care and quality of clinical outcomes in any health system can deteriorate during unexpected events (e.g., COVID-19, hurricanes, wildfires) that divert resources away from routine functions or when natural disasters severely disrupt the normal operation of health services. A unified clinical-payer intelligence fabric designed to address these challenges has the potential to simulate outcome scenarios for unplanned disruptions and identify clinical conditions or care pathways requiring expedited planning or resource allocation. Transitioning from government-mandated plans (layout of service delivery–insurance claims coordination) to formalized CIA—network-enabled shared responsibility—is also critical for real-time patient, institutional, and payer collaboration to ensure the completion of proposed care orders along the approved pathway, improving service delivery quality at lower cost.
Edge-Driven Predictive Maintenance Architecture for High-Stakes Industrial Networks
A distributed cognitive maintenance framework for mission-critical networks is proposed that integrates existing automation and supervisory systems in order to improve their maintenance. Maintenance is neither an end-user activity nor an operation, but a supported and automated activity that enhances the operation of the mission-critical systems. Maintenance activities include predictive and proactive maintenance, real-time fault detection and diagnosis, resource-aware scheduling and allocation, and human-actor collaboration with supervisory control. To achieve this, data-sensing systems issue data in a sentient fashion to cognitive agents, and reasoning agents send information for collective decision making. Control systems, especially for closed-loop operations, are taken into consideration. Mission-critical networks serve production and product operations in real time. They are deployed and operated in an automation mode that may also ensure adequate redundancy for fault tolerance. Nevertheless, systems and facilities remainebt without any of fundamental principles on how to perform obsolescence management, preventive maintenance or even fault detection and diagnosis. Communication between such systems is commonly limited to supervisory control and the exchange of operational parameters. A different view considers maintenance as a mission-supported activity closely integrated with the operation.
Multi-Agent Reinforcement Learning in Value-Based Care
Agentic artificial intelligence (AI) systems, including those realized through multi-agent reinforcement learning (MARL), are emerging in multiple domains, including information filtering, navigation, gaming, and robotics. These novel approaches may also be relevant to healthcare delivery and policy, as MARL is well suited to complex strategic problems that Richard Thaler characterized as “makeshift solutions to impossibly complex problems.” Population health management is such an arena, particularly in optimizing risk- and performance-based contracts, payment redistribution across agents and cadre types, and performance-related benchmarks for an actor-network consisting of several competing provider-organizations functioning in a shared ecosystem. Health information systems, including such agentic, MARL-based systems, could help realize “better, faster, cheaper, and ‘kinder’ healthcare” by improving risk-adjusted health and cost outcomes across multiple resident cohorts, thereby more effectively realizing the goals of modern econometrics in medicine and public health. Current culture tends to represent healthcare decision-making as a Black Box or an informationally centralized “big brain” where supervision assures correctness and compliance with Thaler’s ideal of omniscience for coordinating activities. Yet actual healthcare organizations are not Black Boxes, much less a single Big Brain, corporate or otherwise. Decision makers are often overpromoted, misinformed, late in their decisions, veto the best ideas of the organization, or are unable to manage rich information effectively. Supporting such overloaded Decision Makers-MD, using training and expertise gained trying to wisely harvest large but incomplete information- is a hard problem. Because the decisions should be seen as Agents making active decisions, it is viewed in terms of Agent-based Systems, or the study of multi-oriented organizations as complex adaptive systems.
Detecting Healthcare Billing Fraud with Graph Neural Networks
Healthcare payment accuracy directly influences both cost and quality of care, yet high rates of fraud, waste, and abuse continue to persist. These challenges are exacerbated by the inherent complexity and lack of transparency surrounding payment analytics. Addressing these issues, this study proposes an effective, data-driven solution for detecting potentially fraudulent or erroneous billing practices, using unsupervised machine learning to analyze payer-provider relationships. The primary motivation is threefold: first, such unsupervised approaches eliminate the need for a labeled training set; second, they take advantage of the richness and heterogeneity of the data, including subrogation and encounter information; and third, they have no bias toward any specific type of anomaly. Core methodological contributions include the novel application of unsupervised and graph-based methods for billing analysis and anomaly detection, and the integration of external data into traditional billing tables to uncover new patterns. Testing demonstrates that model-enriched Normalized Payment Amount can help identify fraudulent encounters, offering long-term potential for both real-time monitoring and assisting state-level agencies responsible for managing Medicaid-funded healthcare.
Continuous Claims Event Streaming for Healthcare Adjudication and Quality Analytics
Healthcare analytics is increasingly expected to operate in real-time. The demand for real-time queries and feedback has propelled the move toward event-driven data streaming architectures, which have been adopted in many industries—commerce, finance, social networking—but have yet to be fully realized in healthcare. Although challenges remain in isolating, modeling, and sharing the right data, there are clear motivations for both clinical care—with the potential for detecting and acting on sudden changes in patient health—and administrative use cases, with the prospect of providing proactive operations dashboards that alert business analysts to deepening problems that they can address before they escalate into costly crises. This paper focuses on two specific use cases: the real-time processing of healthcare claims as they are submitted and a framework for measuring, monitoring, and improving healthcare quality in real-time and at scale. The first addresses data provenance and instrumentation needs for the timely execution of the rules governing claims submission and adjudication. The second defines the requirements—timeliness, accuracy, completeness, and so on—for quality measurement at scale based on New York State Department of Health and Centers for Medicare & Medicaid Services performance indicators and establishes a framework for providing real-time feedback on adherence to these requirements. Although a number of high-level components of a comprehensive real-time analytics architecture have been previously proposed or described, decision support, data , governance, and provisioning are still in their formative stages. Building from previous work outlining an event-driven, micro-batch-enabled architecture for real-time data streaming and analytics at scale, the present discussion focuses on claims adjudication and quality measurement use cases. Other descriptive aspects complete the taxonomy of real-time analytics building blocks. Technical requirements are then explicitly addressed for a claims processing framework that analyzes a claims life by identifying the key events, states, and timing constraints that drive the process, and for a real-time quality measurement system that detects anomalies across key performance indicators and provides closed-loop feedback for quality improvement.
Interpretable ML in Clinical Decision-Making
Artificial intelligence has the potential to augment clinical decision making. By learning patterns of risk and disease directly from empirical data, AI methods offer one solution to the difficulty health care professionals face in considering ever-increasing amounts of information. Clinicians making a medical decision for a patient want not only an accurate estimate of the risks associated with their patient's disease or treatment options but also an understanding of the reasoning behind these risks. This desire for explanation drives the growing interest in explainability in AI, particularly in AI for health care. Explainable Artificial Intelligence (XAI) is defined as methods that generate new AI models for which the behaviour can be understood, directly or indirectly, by humans. The concept of human understanding encompasses three different levels – transparency, interpretability and accountability. The heart of the concern for transparency in AI is the incomprehensibility of the learned representations, the “black box” nature of the complex function learned from the training data.
Evidence-Driven Reengineering of Hospital Operations
Clinical motivation, objectives, data-driven approach, methods, findings, and implications are synthesized. Clinical workflows govern patient management in specialty practices, creating a need for technique deployment to improve operational outcomes and resource utilization. Data-Driven Process Modeling uses systematic data analysis to provide rigorous evidence and formal structure to clinical decision-making. ‡Process mining identifies the real recurrence of patient journeys; machine learning models future event occurrences to replicate desirable flows; simulation of mapped processes quantifies throughput, waits, and cycle times; and hybrid methods combine these techniques. Together, they identify bottlenecks and variations affecting performance. An Emergency Department throughput case shows how analyzed paths reveal long wait times at triage and radiology, together with extended time between physician disposition and patient departure. Measurable improvements result from targeted interventions. In a second application, development of Machine Learning algorithms for segments of an inpatient discharge planning pathway highlights excessive attorney handoffs, leading to simplified processes and faster discharge times. Analysis of quantitative results indicates throughput times, waits, and cycle times are shortened, variance reduced, and changes statistically significant. ‡Qualitative evidence complements numbers by revealing practitioner satisfaction and user acceptance.
Secure Edge AI for Financial Document Processing
Enhancing security and privacy during feature extraction of financial documents is of paramount importance. Conventional concerns of data loss during feature extraction can be addressed in a natural way by placing feature extraction on the edge layer of an edge-cloud ecosystem. Recent developments in Generative and Agentic AI open opportunities for generating features for various tasks without sharing or leaking sensitive details about the data. Such capabilities can be expressed as Generative and Agentic AI-enhanced feature extraction—combining agentic capabilities of Generative AI to supervise the feature extraction process and also provide additional semantics and or representations that help improve the accuracy of the downstream task. This framework is shown to naturally extend to both edge-layer and cloud-layer processing. Security and privacy requirements during the feature-extraction stage are met by suitably applying standard security mechanisms without any changes to the underlying principle or operation of the feature extraction process. Generative AI is an emerging technology with numerous applications in various domains. The recent capability of generating new data and labels from a few or a small set of samples but without sharing or leaking the private details in the original data has opened another important dimension. This capability not only allows the origin of the samples to remain secret, but also helps protect the sensitive nature of the data. Generative AI can thus accentuate the benefits of decentralization by further reducing the role and dominance of central-proxy-service providers in edge and cloud ecosystems. Instead of consuming and storing all the edge- and cloud-layer data, the major job can be reduced to merely providing the infrastructure for empowering other users in the ecosystem with the appropriate private-label Generation AI capability required for their workloads.
Hybrid AI Models for Autonomous Cloud Workflow Automation
Distributed cloud infrastructures integrate multiple services at the edge, fog, and cloud, posing diverse automation and monitoring challenges. Automation aims to simplify tasks and establish migration, scaling, and recovery rules to minimize manual intervention. Monitoring AIOps enables fault prevention and reduced Mean Time to Recovery (MTTR) while assuring service resilience. Hybrid AI blends machine learning (ML) models with symbolic reasoning and optimization techniques to formulate intelligent solutions for these scenarios. Use of such technology is gaining popularity for AIOps. The growing frequency of severe data breaches urges organizations to adopt a Data Loss Prevention (DLP) strategy to reduce the risk of sensitive data exposure. DLP aims to detect potential data breaches and prevent the unauthorized transfer of those assets. According to the Cybersecurity and Infrastructure Security Agency (CISA), configuring DLP controls can help organizations reduce threat surface visibility. Proper implementation and enforcement of DLP controls also offer organizations the ability to identify regulatory compliance issues early. Furthermore, detecting and mitigating data leakage incidents contribute to organizations' ability to comply with regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and reduce their impact. Hybrid AI models enable intelligent automation and monitoring of workflows in distributed clouds by facilitating orchestration, supporting recurrent decision logic, providing data-driven insights, and automating response actions. The use of Hybrid AI for workflow automation is evaluated through automated elastic data ingestion pipelines (ADIPs) capable of ingesting streaming sensors data and migrating it to the cloud for storage, post-processing, and consumption. Moreover, an evaluation of AIOps in distributed cloud environments is performed, addressing data leakage prevention and intrusion detection incidents. The proposed solutions leverage different information sources such as network flows, endpoints, databases, and cloud resources and adopt supervised, unsupervised, and knowledge-based methods for detection, analysis, and mitigation. The presented approaches constitute practical use cases that explore benefits and limitations of Hybrid AI and provide guidance for its effective application.
Cloud AI for Real-Time Global Transit Optimization
A consortium of European and global universities, research institutes, and industry partners is investigating cloud-orchestrated AI systems that analyze and optimize transportation networks and mobility services for urban and intercity journeys. The proposed system employs centrally orchestrated federated intelligence, whereby individual cloud-edge networks are governed by their respective authorities to ensure data residency within defined jurisdictions. Nevertheless, these cloud-edge networks build a trusted multi-cloud layer and high-capacity communication paths to exchange operational data and process contextually relevant information streams in a timely manner. At the same time, the cloud edge of the public transportation and logistics services flowing through these cloud-edge networks is streamed into a common AI runtime, enabling network-wide routing and scheduling decisions in real time. The capital- and energy-intensive nature of transportation infrastructure requires careful planning and, once deployed, calls for continuous real-time traffic management. Transportation systems need to respond not only to common network and service capabilities but also to fluctuations in supply, demand, service quality, and pricing. In a federated intelligence architecture, such responses are implemented in self-contained economic ecosystems. For example, different railway companies and unions do adjust their fares according to demand elasticity and traffic volume but usually do not harmonize prices and timetables. Due to the inherent asymmetries in such self-contained ecosystems, however, the real-time responsiveness of networked transportation services is naturally more limited than those of urban mobility networks, which are also internally interconnected. A solution to this asymmetry is for the operators of these separate systems to collaborate for the common good in a context-aware manner. Supply chain dynamics can then also be included in the optimization exercise.
Federated Health Secure AI Across Borders
Federated AI and cloud data architectures enable secure international cooperation in global healthcare by delivering healthcare intelligence capabilities in a trustworthy manner across organizations, institutions, and countries. Federated AI enables sensitive data to remain with its owner, as AI algorithms can access the data without copying it. Cloud is the backbone technology of today’s economy offering resiliency, scalability, and cost-effectiveness. Clouds help organizations comply with their risk and privacy frameworks while concentrating on their own core offerings. Federated AI and cloud data architectures deployed within this framework allow organizations to utilize the facility offered by cloud service providers while concentrating on their own core offerings. However, incorporating both technologies has its own challenges due to the inherent nature of these environments. Federated AI and cloud data architectures enable secure collaboration in global health by providing healthcare intelligence capabilities in a trustworthy manner. Sensitive data can remain with its owner, and AI algorithms can access the data without copying it, as federated AI enables this functionality. Cloud is the backbone technology of today’s economy, offering resiliency, scalability, and cost-effectiveness. Clouds help organizations comply with their risk and privacy frameworks while concentrating on their core offerings. Federated AI and cloud data architectures deployed within this framework allow organizations to make use of the facilities offered by cloud service providers while concentrating on their core capabilities. However, incorporating both technologies has distinct challenges due to the inherent nature of these environments.
Agentic AI for Enterprise Decision Automation
Autonomous multi-agent AI supports decision intelligence and workflow automation in enterprise settings, enabling active and independent agents that learn from experience, collaborate, negotiate, share knowledge, and jointly solve problems. The architectural design ensures lightweight deployment and integrability with cloud-based or on-premise enterprise IT ecosystems. First, data management, provenance, and governance facilitate reliable enterprise decision systems. Then, agents assist human users in the full decision pipeline, from analysis to execution and control. Autonomy extends workflow automation: parallelizable enterprise tasks are coordinated by techniques from multi-agent systems, and workflows involving multiple actors are represented as task orchestration, where specialized agents negotiate and execute task sequences. An extensive evaluation demonstrates feasibility, potential, and future directions for real-world application. Autonomous multi-agent AI systems provide a new way to structure and automate enterprise organization and interaction. Enterprise processes are supported by the evidence pipeline of decision intelligence: sensory data is transformed into information via analytical processes (theory-driven or machine-learning-based), and through prediction and recommendation into knowledge useful for decision-making and control. Semantic- or causal-based approaches to decision intelligence utilize autonomous visual agents to navigate, collect data, construct a model of the environment, and provide the evidence required for decision-making.
AI-Powered Cloud Infrastructure Automation
Cloud automation fuels efficiency, resilience, and agility, yet many deployments remain only partially automated, hindering their full potential. A cloud environment evolves with rapid growth or unexpected events, demanding continuous operation. Automation ensures that systems react promptly to changes in workload, performance, cost, availability, or security. AI is poised to take automation a step further by driving operations that adapt in real time with minimal human involvement. AI-driven cloud automation blends machine learning and other artificial intelligence domains into a coherent system of techniques and technologies for improved, automated deployment and operation of cloud-based services and applications. Automating the entire life cycle of cloud-based solutions—from design to deployment and operation—requires a complete set of automation capabilities and services, spanning the four primary domains of any cloud automation strategy. Clouds typically accommodate a wide variety of resources, each with its own orchestration and management tooling. Delivering an end-to-end integrated solution involves crossing these boundaries and extending automation into the multiple other areas of the cloud environment not directly managed by the underlying orchestration platform. Integrating these capabilities requires addressing the full life cycle of cloud-based services, including networking, security, and governance.
AI-Driven Big Data Analytics for Personalized Treatment Planning
Big data and AI are poised to transform healthcare due to their capacity for providing personalized predictions and treatment decisions for individuals. Such predictions, often termed risk scores, are commonly used in predictive modeling for risk stratification. Nevertheless, risk scores are only part of a comprehensive predictive analytics strategy for personalized health. Data-driven risk scores must inform the causal-inference models that determine the optimal treatment to maximize the long-term well-being of individuals. Together, these two components can augment clinical decision support for health providers. Despite these advances, the clinical translation of AI-augmented analytics relies critically on real-world evidence derived from their prospective application in clinical practice. Nevertheless, few of the aforementioned AI efforts require evaluation through pilot studies that collect evidence of their practical value. Future work, therefore, focuses on developing a supporting architecture to maintain a real-time repository of prospectively collected evidence. Such work is informed by discussions with health domain experts and ethics approval from an institutional review board.
Smart Data Pipelines for Instant Fraud Detection
Detecting fraudulent activities is a persistent challenge for financial institutions and FinTech companies. For many organizations, monitoring their operations is a significant expenditure. The threat landscape is increasingly dynamic and requires constant vigilance. An attractive alternative is to use real-time data monitoring with predictive analytics to discover anomalies indicative of fraud. Such a system could complement or replace many of the existing detection solutions. If implemented appropriately, it could help with regulatory compliance as well as providing data on new emerging attack vectors. This paper describes a comprehensive architecture designed to enable such detection of possible fraud in FinTech companies. The focus is on data engineering pipelines for supervised, semi-supervised, and anomaly detection based on historical and real-time data. The objective is to build a data framework that can support real-time data ingestion and low-latency decisioning. For the pipelines, common components for ingestion, cleaning, feature storage, and quality are proposed and documented. Existing streaming platforms are evaluated with respect to suitable message schema for real-time decisioning. Road-mapping techniques are discussed to deal with feature-space changes over time. The section is suitable for those considering a data framework to support real-time fraud detection in FinTech or financial companies.