6G goal-oriented communications: How to coexist with legacy systems?

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The modernization of legacy software is a growing priority for U.S. enterprises that want to remain political or business competitive in a data-driven economy. Although legacy systems often serve a central role in an organization or department, they come with various burdens including high maintenance costs, low elasticity scalability, and a lack of interface capacity with modern technologies. Therefore, like many aspects of innovation and digital technology, Artificial Intelligence (AI) has the potential to transform legacy systems by allowing automated code refactoring, in system optimization, and process decision management. The authors present a comprehensive review of AI-enabled approaches to successfully and cost-effectively modernize legacy systems for enterprise applications. They highlight the need to provide organizations and enterprises with the ability to be adaptive or flexible in a sustained and cost-effective manner for a wide scope of legacy system modernization. Based on new literature in legacy systems using AI, the authors provide examples of applications in enterprise resource planning, smart manufacturing systems, cloud integration and migration, and multi-cloud optimization and cost savings activities. Also presented are frameworks and best practices for successful implementation for each of these new areas, and the opportunistic challenges of analysis complexity of integrated systems, integration to newer technological spaces, and systems knowledge or skills gaps. The data shows that while AI extends current functional capacity of legacy systems, it also calibrates legacy systems within current governance expectations, strategic outcomes for digital transformation, flexible scaling and strategies for secure cloud capabilities, and is the safest and most cost-efficient approach to modernizing legacy systems to enhance or to reduce enterprise risk.

This study presents a meta-analysis of artificial intelligence (AI) applications in predicting and dynamically managing liquidity risk within the commercial banking sector. Drawing from the empirical evidence from peer-reviewed journals, industry reports, and case studies from the years 2015-2025, this research compares the performance of AI models (Long Short-Term Memory (LSTM), XGBoost, and Deep Q-Networks (DQN)) with traditional forecasting methods (ARIMA and Historical Simulation models). Results show that AI models always beat the traditional approach on forecasting accuracy (up to 99.3%), always better on root mean square error (RMSE), and predict with a much shorter lag. Real-world implementations in institutions like JPMorgan Chase and China Construction Bank reveal operational gains in transaction processing speed, cost efficiency, and early warning capabilities. The study further tests AI performance under stressful situations, such as the COVID-19 pandemic and regional banking turbulence, showing that it was resilient. Despite obvious benefits, challenges in legacy systems, ambiguity in the regulation, and interpretability of models in terms of accuracy act as barriers to widespread adoption. In conclusion, this paper states that AI provides a transformative edge for proactive, data-driven liquidity risk management, and banks should, therefore, invest in AI integration, which should complement any transparency and regulatory compliance issues.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial Intelligence (AI) and Machine Learning (ML) affect the creation of new Master Data Management (MDM) systems, which have become so critical in MDM, an organization integrating critical data about customers, products, and employees into a single trusted repository raises the ability to make better decisions, comply with legal requirements, and be more operationally efficient like all levels of IT, healthcare, and supply chain management. The paper examines how AI and ML define MDM by automating data categorization, improving data quality, and making predictions for better decision-making. This enables such technologies to be used in data governance and integration processes to scale up and enhance MDM systems' accuracy and responsiveness in a timely and accurate mode. A healthcare example would be when an AI-powered system improves the accuracy with which patients are cared for through medical records. In such cases, they look at the ML model in SCM and define models that predict demand or optimize inventory management. The study discusses some challenges, including data privacy concerns, integration with legacy systems, and risks of bias in AI models. The conclusion illustrates the prospects of AI and ML in MDM. It seeks to enhance automation and efficiency and help decision-making based on a more informed approach to MDM in all industries. These technologies will evolve to redefined ways of managing the data, allowing organizations to use the data correctly in the fast-paced, data-driven world.

Editorial Commentary: Artificial Intelligence and Language Learning Models Can Be Improved by Curated Input of Medical Training Data but Still Face the Limitations of Available Literature and Require Continued Human Oversight.

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.

The banks are still using legacy systems as the core of their business. One of the reasons is that the financial and operational data are stored here and can be used for decision making. Other reasons are that applications developed for many years and very high costs for these applications to be retired. On the other hand, technology and in the past year, a cataclysmic evolution of technology and client demands. This trade-off between stability on legacy systems or competitive advantage on modern architectures, as there are many known cases of banks that have collapsed due to not adapting quickly enough is still a hot topic for many industries, not just banks. The risk and costs of switching to new system is high enough. The objective of this article is to present the main risks and costs of using legacy systems, the preferences of the bank users in terms of software reliability and subjective assessment of the software systems' modernity, an overview on new intelligent IT infrastructures, the preferences for the modernization of systems and a SOLUTION of the modernization of legacy banking systems using an open architecture [1]. Artificial Intelligence (AI) is now widely acknowledged as one of the most important digital transformation enablers across a significant number of industries including the banking sector. AI technologies are assisting the banks in India in upgrading their operations across the board from accounting to sales to contracts to cybersecurity. This paper aims to analyze the application of AI in Banks and to take a case study based on the virtual Assistant of SBI-SIA. The evolution of banking and the emergence of virtual banking was explained in this and the trends in the modern Banking systems were explained along with a Rationale use of AI in Banking [2].

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

The secondary use of health data for training Artificial Intelligence (AI) models holds immense potential for advancing medical research and healthcare delivery. However, ensuring patient consent for such utilization is paramount to uphold ethical standards and data privacy. Patient informed consent means patients are fully informed about how their data will be collected, used, and protected, and they voluntarily agree to allow their data to be used for AI models. In addition to formal consent frameworks, establishing a social license is critical to foster public trust and societal acceptance for the secondary use of health data in AI systems. This study examines patient consent practices in this domain. In this scoping review, we searched Web of Science, PubMed, and Scopus. We included studies in English that addressed the core issues of interest, namely, privacy, security, legal, and ethical issues related to the secondary use of health data in AI models. Articles not addressing the core issues, as well as systematic reviews, meta-analyses, books, letters, conference abstracts, and study protocols were excluded. Two authors independently screened titles, abstracts, and full texts, resolving disagreements with a third author. Data was extracted using a data extraction form. After screening 774 articles, a total of 38 articles were ultimately included in the review. Across these studies, a total of 178 barriers and 193 facilitators were identified. We consolidated similar codes and extracted 65 barriers and 101 facilitators, which we then categorized into four themes: "Structure," "People," "Physical system," and "Task." We identified notable emphasis on "Legal and Ethical Challenges" and "Interoperability and Data Governance." Key barriers included concerns over privacy and security breaches, inadequacies in informed consent processes, and unauthorized data sharing. Critical facilitators included enhancing patient consent procedures, improving data privacy through anonymization, and promoting ethical standards for data usage. Our study underscores the complexity of patient consent for the secondary use of health data in AI models, highlighting significant barriers and facilitators within legal, ethical, and technological domains. We recommend the development of specific guidelines and actionable strategies for policymakers, practitioners, and researchers to improve informed consent, ensuring privacy, trust, and ethical use of data, thereby facilitating the responsible advancement of AI in healthcare.

Government agencies face escalating challenges in managing vast amounts of documents, including policy papers, legal briefs, and administrative records.Traditional document management systems-reliant on manual classification and keyword-based retrieval-struggle with scalability, accuracy, and security.This study investigates how artificial intelligence (AI), extensive language models (LLMs), can transform government document management by automating classification, enhancing retrieval accuracy, and ensuring compliance with legal standards.A systematic literature review was conducted following the PRISMA guidelines.We searched Google Scholar, IEEE Xplore, ACM Digital Library, and Web of Science for peer-reviewed studies published between 2018 and 2024.Inclusion criteria focused on empirical studies, case analyses, or theoretical frameworks involving LLM or AI applications in government document management.After screening 312 records, 42 studies met the eligibility criteria and were analyzed thematically.Findings reveal that LLMs significantly outperform traditional methods in document classification (accuracy 15-30%) and natural language querying (user satisfaction 40%).Key applications include automated policy analysis, FOIA request processing, and cross-departmental knowledge retrieval.However, implementation barriers persist: technical complexity (52% of cases), staff resistance (38%), and regulatory compliance issues (29%).LLMs offer transformative potential for government document management, but success hinges on domain-specific training, robust governance frameworks, and stakeholder engagement.This study provides a practical roadmap for public sector agencies to adopt LLMs responsibly, emphasizing phased deployment, ethics oversight, and interoperability with legacy systems.

To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA). One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA. Data was divided into a training, validation, and test set. The AI model was trained on the training data to perform bone, synovium, and osteophyte identification on the images. Based on the manually performed image segmentation, an AI was trained to classify the severity of osteophytes according to EOGS from 0 to 3. Percent Exact Agreement (PEA) and Percent Close Agreement (PCA) were assessed on individual joints and overall. PCA allows a difference of one EOGS grade between doctor assessment and AI. A total of 4615 ultrasound images were used for AI development and testing. The developed AI model scored on the test set for the MCP joints a PEA of 76% and PCA of 97%; for PIP, a PEA of 70% and PCA of 97%; for DIP, a PEA of 59% and PCA of 94%, and CMC a PEA of 50% and PCA of 82%. Combining all joints, we found a PEA between AI and doctor assessments of 68% and a PCA of 95%. The developed AI model can perform joint ultrasound image segmentation and severity scoring of osteophytes, according to the EOGS. As proof of concept, this first version of the AI model is successful, as the agreement performance is slightly higher than previously found agreements between experts when assessing osteophytes on hand OA ultrasound images. The segmentation of the image makes the AI explainable to the doctor, who can immediately see why the AI applies a given score. Future validation in hand OA cohorts is necessary though.

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n=435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n=95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.