Artificial intelligence (AI) literacy is the ability to understand, use, monitor, and critically evaluate AI applications without needing to create AI models. As many professionals outside technical fields frequently engage with AI, this literacy is essential. This study evaluates the validity and reliability of the Turkish adaptation of the Artificial Intelligence Literacy Scale (AILS) to measure AI literacy among healthcare professionals in Türkiye. The study included 210 healthcare professionals—physicians, dentists, nurses, and midwives—aged 18 and older. AILS is a seven-point Likert scale with 12 items divided into four factors: “awareness,” “usage,” “evaluation,” and “ethics.” Cronbach’s alpha indicated good internal consistency for the scale, with a coefficient of 0.85. Confirmatory Factor Analysis (CFA) showed satisfactory fit indices: χ2/df = 1.665, Comparative Fit Index = 0.968, Goodness of Fit Index = 0.944, Tucker-Lewis Index = 0.955, Standardized Root Mean Square Residual = 0.040, and Root Mean Square Error Estimate = 0.056. The 12-item, 4-factor AILS shows a strong fit and robust structure for the sample data. Our study validates the Turkish version of the AILS as a reliable tool for assessing artificial intelligence literacy among Turkish healthcare professionals.

An understanding of the nature and function of human trust in artificial intelligence (AI) is fundamental to the safe and effective integration of these technologies into organizational settings. The Trust in Automation Scale is a commonly used self-report measure of trust in automated systems; however, it has not yet been subjected to comprehensive psychometric validation. Across two studies, we tested the capacity of the scale to effectively measure trust across a range of AI applications. Results indicate that the Trust in Automation Scale is a valid and reliable measure of human trust in AI; however, with 12 items, it is often impractical for contexts requiring frequent and minimally disruptive measurements. To address this limitation, we developed and validated a three-item version of the TIAS, the Short Trust in Automation Scale (S-TIAS). In two further studies, we tested the sensitivity of the S-TIAS to manipulations of the trustworthiness of an AI system, as well as the convergent validity of the scale and its capacity to predict intentions to rely on AI-generated recommendations. In both studies, the S-TIAS also demonstrated convergent validity and significantly predicted intentions to rely on the AI system in patterns similar to the TIAS. This suggests that the S-TIAS is a practical and valid alternative for measuring trust in automation and AI for the purposes of identifying antecedent factors of trust and predicting trust outcomes.

The Dataset "AI Global index" includes The Global AI Index itself and seven indicators affecting the Index on 62 countries, as well as general information about the countries (region, cluster, income group and political regime).

The Global AI Index is the first index to benchmark nations on their level of investment, innovation and implementation of artificial intelligence.

Talent, Infrastructure and Operating Environment are the factors of AI Implementation group of indicators, which represents the application of artificial intelligence by professionals in various sectors, such as businesses, governments, and communities. - Talent indicator focuses on the availability of skilled practitioners for the provision of artificial intelligence solutions. - Infrastructure indicator focuses on the reliability and scale of access infrastructure, from electricity and internet, to super computing capabilities. - Operating Environment indicator focuses on the regulatory context, and public opinion surrounding artificial intelligence.

Research and Development are the factors of Innovation group of indicators, which reflects the progress made in technology and methodology, which signify the potential for artificial intelligence to evolve and improve. - Research indicator focuses on the extent of specialist research and researchers; investigating the amount of publications and citations in credible academic journals. - Development indicator focuses on the development of fundamental platforms and algorithms upon which innovative artificial intelligence projects rely.

Government Strategy and Commercial are the factors of Investment group of indicators, which reflects financial and procedural commitments to artificial intelligence. - Government Strategy indicator focuses on the depth of commitment from national government to artificial intelligence; investigating spending commitments and national strategies. - Commercial indicator focuses on the level of startup activity, investment and business initiatives based on artificial intelligence.

All these seven indicators were calculated by Tortoise Media via weighting and summarizing 143 other indicators.

The dataset can be used for practicing data cleaning, data visualization, finding correlations between the indexes, Machine Learning (classification, regression, clustering).

The data was used in the analytical article research Artificial Intelligence on the World Stage: Dominant Players and Aspiring Challengers

EFA and CFA

IntroductionGenerative artificial intelligence (AI) tools, such as ChatGPT, have gained significant traction in educational settings, offering novel opportunities for enhanced learning experiences. However, limited research has investigated how students perceive and accept these emerging technologies. This study addresses this gap by developing a scale to assess university students’ attitudes toward generative AI tools in education.MethodsA three-stage process was employed to develop and validate the Generative AI Attitude Scale. Data were collected from 664 students from various faculties during the 2022–2023 academic year. Expert evaluations were conducted to establish face and content validity. An exploratory factor analysis (EFA) was performed on a subset of 400 participants, revealing a two-factor, 14-item structure that explained 78.440% of the variance. A subsequent confirmatory factor analysis (CFA) was conducted on a separate sample of 264 students to validate this structure, resulting in the removal of one item and a final 13-item scale.ResultsThe 13-item scale demonstrated strong reliability, evidenced by a Cronbach’s alpha of 0.84 and a test–retest reliability of 0.90. Discriminative power was confirmed through corrected item-total correlations between lower and upper percentile groups. These findings indicate that the scale effectively differentiates student attitudes toward generative AI tools in educational contexts.DiscussionThe newly developed Generative AI Attitude Scale offers a valid and reliable instrument for measuring university students’ perspectives on integrating generative AI tools, such as ChatGPT, into educational environments. These results highlight the potential for more targeted research and informed implementation strategies to enhance learning outcomes through generative AI.

Model Evaluation Platforms Market Outlook

According to our latest research, the Global Model Evaluation Platforms Market size was valued at $1.3 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 21.9% during the forecast period of 2025–2033. The primary driver behind this impressive growth trajectory is the escalating adoption of artificial intelligence (AI) and machine learning (ML) across diverse industries, necessitating robust platforms to assess, validate, and optimize the performance of increasingly complex models. As organizations strive to operationalize AI at scale, the demand for sophisticated model evaluation tools that ensure reliability, transparency, and regulatory compliance is surging, making this market a cornerstone of the broader AI ecosystem.

Regional Outlook

North America currently commands the largest share of the Model Evaluation Platforms Market, accounting for over 38% of the global revenue in 2024. This dominance is underpinned by the region's mature technology infrastructure, a dense concentration of AI-first enterprises, and substantial investments in research and development. Major technology hubs such as Silicon Valley and Boston continue to foster innovation, supported by a robust ecosystem of universities, startups, and established tech giants. Furthermore, favorable government policies around AI and data governance, coupled with early adoption by sectors like BFSI, healthcare, and retail, have established North America as the epicenter for advancements in model evaluation technologies. The region's proactive stance on regulatory compliance and ethical AI also drives the need for comprehensive evaluation platforms, ensuring models are both performant and trustworthy.

The Asia Pacific region is poised to be the fastest-growing market, projected to expand at a CAGR exceeding 25% from 2025 to 2033. This rapid growth is fueled by the digital transformation initiatives underway in countries such as China, India, Japan, and South Korea. Governments and private enterprises in this region are investing heavily in AI research, smart manufacturing, and digital health, driving the adoption of model evaluation platforms to ensure the reliability of deployed AI solutions. Additionally, the proliferation of cloud computing and the rise of regional AI startups have accelerated the need for scalable, cloud-based model evaluation solutions. Strategic partnerships between local tech companies and global AI leaders further catalyze market expansion, as does the increasing participation in global AI policy dialogues and standardization efforts.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing Model Evaluation Platforms, albeit at a slower pace due to infrastructural and skills gaps. In these regions, adoption is often concentrated within multinational enterprises and government-led digitalization projects, particularly in sectors such as finance, public administration, and telecommunications. However, challenges such as limited access to advanced cloud infrastructure, data privacy concerns, and the shortage of skilled AI professionals can impede widespread uptake. Despite these hurdles, localized demand for AI-driven solutions in areas like fraud detection, healthcare diagnostics, and smart city initiatives is beginning to create new opportunities for vendors willing to tailor their offerings to the unique regulatory and operational landscapes of these markets.

Report Scope

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AI Inference-As-A-Service Market Size 2025-2029

The ai inference-as-a-service market size is valued to increase by USD 111.09 billion, at a CAGR of 20.4% from 2024 to 2029. Proliferation and increasing complexity of AI models will drive the ai inference-as-a-service market.

Market Insights

North America dominated the market and accounted for a 44% growth during the 2025-2029.
By Component - GPU segment was valued at USD 19.55 billion in 2023
By Type - HBM segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 445.91 million 
Market Future Opportunities 2024: USD 111088.70 million
CAGR from 2024 to 2029 : 20.4%

Market Summary

The AI Inference-as-a-Service (IaaS) market is experiencing significant growth due to the increasing proliferation and complexity of artificial intelligence models. Businesses worldwide are adopting AI to optimize supply chain operations, ensure regulatory compliance, and enhance operational efficiency. However, the rise of serverless inference and higher-level abstractions presents new challenges. Severe hardware supply chain constraints and high costs are major hurdles for organizations looking to implement AI at scale. Despite these challenges, the benefits of AI IaaS are compelling. For instance, in the realm of supply chain optimization, AI models can analyze vast amounts of data to predict demand patterns, optimize inventory levels, and improve logistics. In the financial sector, AI IaaS can be used to detect fraudulent transactions, comply with regulations, and enhance customer service. The future of AI IaaS lies in its ability to provide flexible, scalable, and cost-effective solutions. As businesses continue to embrace AI, the demand for AI IaaS is expected to grow. The market will be driven by advancements in AI technologies, increasing adoption of cloud services, and the need for real-time data processing. However, addressing the challenges of hardware supply chain constraints and costs will remain a priority for market participants.

What will be the size of the AI Inference-As-A-Service Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe AI Inference-as-a-Service (IaaS) market continues to evolve, offering businesses the ability to deploy and manage machine learning models at scale without the need for extensive infrastructure. This trend aligns with the increasing demand for real-time, data-driven insights in various industries. For instance, in the finance sector, AI models are used for fraud detection, risk assessment, and customer segmentation. Quantization techniques, such as model compression methods and feature engineering, play a crucial role in inference scalability and cost efficiency. According to recent research, companies have achieved a significant reduction in inference response format size by implementing quantization techniques, enabling them to process larger datasets and make real-time decisions. Model performance tuning, hyperparameter optimization, and model selection criteria are essential aspects of maintaining accurate and reliable inference services. Inference service reliability is a critical concern for businesses, necessitating error handling mechanisms and prediction confidence intervals. Knowledge graph inference and hardware acceleration options further enhance the capabilities of AI models, providing faster and more precise results. Reinforcement learning models, recurrent neural networks, and convolutional neural networks are some of the advanced machine learning techniques being employed in the IaaS market. Model bias mitigation, inference cost estimation, and model retraining frequency are essential factors for businesses when selecting an IaaS provider. These considerations impact budgeting, product strategy, and compliance with data privacy regulations. Inference api endpoints, api authentication methods, and data version control are essential components of a robust deployment pipeline. In conclusion, the market offers businesses the flexibility and scalability to deploy and manage machine learning models effectively. By focusing on factors such as model performance, reliability, and cost efficiency, businesses can make informed decisions and gain a competitive edge in their respective industries.

Unpacking the AI Inference-As-A-Service Market Landscape

In the realm of artificial intelligence (AI), the market for cloud-based inference services has gained significant traction, enabling businesses to efficiently process complex AI workloads through application programming interfaces (APIs). According to recent industry reports, API request throughput for inference services has increased by 30% year-over-year, underscoring the growing demand for high throughput and low latency requirements. Furthermore, model trai

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

Total-Long-Term-Liabilities Time Series for C3 Ai Inc. C3.ai, Inc. operates as an enterprise artificial intelligence application software company. The company offers C3 agentic AI platform, an application development and runtime environment that enables customers to design, develop, and deploy enterprise AI applications; C3 AI CRM Suite, a customer relationship management solution; C3 Generative AI that enables to locate, retrieve, present information, disparate data stores, applications, and enterprise information systems; C3 AI Health Suite to accelerate healthcare innovation; and C3 AI Financial Services Suite. Its C3 AI Applications include C3 AI Asset Performance suite, which consists of C3 AI Reliability, C3 AI Process Optimization, and C3 AI Energy Management applications. The company's C3 AI Supply Chain Suite comprises C3 AI Supply Network Risk, C3 AI Inventory Optimization, C3 AI Demand Forecasting, C3 AI Production Schedule Optimization, and C3 AI Sourcing Optimization solutions; C3 AI Sustainability Suite includes C3 AI ESG and C3 AI Energy Management applications to decrease greenhouse gas emissions; and C3 AI Defense & Intelligence Suite. It provides C3 AI State and Local Government Suite that includes various applications, such as C3 Law Enforcement for state, county, and municipal law enforcement agencies; C3 AI Residential Property Appraisal and C3 AI Commercial Property Appraisal for county property assessors and appraisers; and C3 Generative AI for Government Programs and C3 Generative AI for Constituent Services for federal, state, and local governments. It has strategic partnerships with Microsoft Azure, AWS, Google Cloud, McKinsey & Company, Baker Hughes, Booz Allen, and others. The company has a strategic alliance with SMX Group, LLC for the development of mission critical AI in the secure environments. The company was formerly known as C3 IoT, Inc. and changed its name to C3.ai, Inc. in June 2019. C3.ai, Inc. was incorporated in 2009 and is headquartered in Redwood City, California.

The rapid expansion of AI and the massive use of digital learning are creating a huge change in higher education. In contrast to general higher education, which is at the center of change, the changes in vocational higher education do not seem to have received sufficient attention from researchers. Focusing on artificial intelligence literacy and sustainability of digital learning competences, this study examined the operational behaviors of 1004 students in a practical course in a higher education institution in mainland China by using the COM-B theory. The results showed that AI literacy was positively correlated with sustainable digital learning ability, and AI literacy was positively correlated with students’ sustainable digital learning behaviors through sustainability of digital learning competences. However, students in vocational institutions are not able to translate this learning behavior into a path to achieve good performance in practice, and even sustainability of digital learning competences can be slightly counterproductive. There is no excessive effect on the model after controlling for variables such as gender, family resources, and ethnic minorities, which contributes to benefit equally from quality education.

BackgroundManual quality assessment of systematic reviews is labor-intensive, time-consuming, and subject to reviewer bias. With recent advances in large language models (LLMs), it is important to evaluate their reliability and efficiency as potential replacements for human reviewers.AimThis study assessed whether generative AI models can substitute for manual reviewers in literature quality assessment by examining rating consistency, time efficiency, and discriminatory performance across four established appraisal tools.MethodsNinety-one systematic reviews were evaluated using AMSTAR 2, CASP, PEDro, and RoB 2 by both human reviewers and two LLMs (ChatGPT-4.0 and DeepSeek R1). Entropy-based indicators quantified rating consistency, while Spearman correlations, receiver operating characteristic (ROC) analysis, and processing-time comparisons were used to assess the relationship between time variability and scoring reliability.ResultsThe two LLMs demonstrated high consistency with human ratings (mean entropy = 0.42), with particularly strong alignment for PEDro (0.17) and CASP (0.25). Average processing time per article was markedly shorter for LLMs (33.09 s) compared with human reviewers (1,582.50 s), representing a 47.80-fold increase in efficiency. Spearman correlation analysis showed a statistically significant positive association between processing-time variability and rating entropy (ρ = 0.24, p = 0.026), indicating that greater time variability was associated with lower consistency. ROC analysis further showed that processing-time variability moderately predicted moderate-to-low consistency (AUC = 0.65, p = 0.045), with 46.00 seconds identified as the optimal cutoff threshold.ConclusionLLMs markedly reduce appraisal time while maintaining acceptable rating consistency in literature quality assessment. Although human validation is recommended for cases with high processing-time variability (>46.00 s), generative AI represents a promising approach for standardized, efficient, and scalable quality appraisal in evidence synthesis.

Altman-Zscore Time Series for C3 Ai Inc. C3.ai, Inc. operates as an enterprise artificial intelligence application software company. The company offers C3 agentic AI platform, an application development and runtime environment that enables customers to design, develop, and deploy enterprise AI applications; C3 AI CRM Suite, a customer relationship management solution; C3 Generative AI that enables to locate, retrieve, present information, disparate data stores, applications, and enterprise information systems; C3 AI Health Suite to accelerate healthcare innovation; and C3 AI Financial Services Suite. Its C3 AI Applications include C3 AI Asset Performance suite, which consists of C3 AI Reliability, C3 AI Process Optimization, and C3 AI Energy Management applications. The company's C3 AI Supply Chain Suite comprises C3 AI Supply Network Risk, C3 AI Inventory Optimization, C3 AI Demand Forecasting, C3 AI Production Schedule Optimization, and C3 AI Sourcing Optimization solutions; C3 AI Sustainability Suite includes C3 AI ESG and C3 AI Energy Management applications to decrease greenhouse gas emissions; and C3 AI Defense & Intelligence Suite. It provides C3 AI State and Local Government Suite that includes various applications, such as C3 Law Enforcement for state, county, and municipal law enforcement agencies; C3 AI Residential Property Appraisal and C3 AI Commercial Property Appraisal for county property assessors and appraisers; and C3 Generative AI for Government Programs and C3 Generative AI for Constituent Services for federal, state, and local governments. It has strategic partnerships with Microsoft Azure, AWS, Google Cloud, McKinsey & Company, Baker Hughes, Booz Allen, and others. The company has a strategic alliance with SMX Group, LLC for the development of mission critical AI in the secure environments. The company was formerly known as C3 IoT, Inc. and changed its name to C3.ai, Inc. in June 2019. C3.ai, Inc. was incorporated in 2009 and is headquartered in Redwood City, California.

Reliability Block Diagram Software Market Outlook

According to our latest research, the global Reliability Block Diagram Software market size reached USD 1.29 billion in 2024, driven by increasing adoption across engineering and safety-critical industries. The market is expanding at a robust CAGR of 11.2% and is forecasted to attain USD 3.06 billion by 2033. The primary growth factor is the accelerating demand for advanced reliability modeling and risk assessment tools, which enable organizations to optimize system performance, reduce downtime, and comply with stringent safety regulations. This surge is further fueled by digital transformation initiatives and the integration of predictive analytics in critical infrastructure sectors.

The growth trajectory of the Reliability Block Diagram Software market is underpinned by the increasing complexity of modern systems in sectors such as aerospace, automotive, and manufacturing. As products and infrastructure become more intricate, organizations require sophisticated modeling tools to visualize and analyze system reliability, identify potential failure points, and implement proactive maintenance strategies. The software’s ability to simulate various operational scenarios and optimize system architecture is a key driver for its widespread adoption. Additionally, the growing emphasis on minimizing operational risks and lifecycle costs has made reliability engineering a central focus for enterprises aiming to achieve higher efficiency and competitive advantage.

Another significant growth factor is the rising regulatory and compliance requirements across industries that operate mission-critical systems. Regulatory bodies now mandate comprehensive risk assessment and reliability documentation, particularly in sectors like healthcare, energy, and defense. Reliability Block Diagram Software streamlines the process of regulatory reporting by providing precise, auditable models and analytics. This not only aids in compliance but also enhances transparency and accountability in system design and maintenance. The software’s ability to integrate with other engineering and asset management platforms further amplifies its value proposition, enabling seamless data exchange and holistic system analysis.

The increasing integration of artificial intelligence and machine learning with reliability modeling tools is also reshaping the market landscape. Modern Reliability Block Diagram Software platforms now offer predictive analytics capabilities, enabling organizations to forecast potential failures and optimize maintenance schedules proactively. This shift towards predictive maintenance and real-time monitoring is particularly prominent in industries with high operational risks, such as energy and utilities, where unplanned downtime can have significant financial and safety implications. The convergence of reliability engineering with digital transformation initiatives is expected to unlock new growth opportunities, as organizations seek to leverage data-driven insights for strategic decision-making.

From a regional perspective, North America continues to dominate the Reliability Block Diagram Software market, accounting for the largest revenue share in 2024. This leadership is attributed to the early adoption of advanced engineering tools, robust technological infrastructure, and the presence of major industry players. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid industrialization, increasing investments in infrastructure, and the growing focus on quality and safety standards. Europe also maintains a significant market presence, driven by stringent regulatory frameworks and the strong emphasis on sustainability and operational excellence across industries.

Component Analysis

The Component segment of the Reliability Block Diagram Software market is bifurcated into Software and Services, each playing a crucial role in the overall ecosystem. The Software sub

Reliability Block Diagram Software Market Outlook

According to our latest research, the global Reliability Block Diagram Software market size reached USD 1.28 billion in 2024, reflecting robust industry demand across multiple sectors. The market is experiencing significant momentum, propelled by a compound annual growth rate (CAGR) of 11.4% from 2025 to 2033. By the end of the forecast period in 2033, the market is projected to achieve a value of approximately USD 3.10 billion. This expansion is primarily driven by the increasing need for advanced reliability analysis tools in mission-critical industries, where operational uptime and failure risk mitigation are essential for business continuity and regulatory compliance.

The rapid growth of the Reliability Block Diagram Software market is underpinned by the escalating complexity of modern systems and the growing emphasis on predictive maintenance strategies. As organizations strive to minimize unplanned downtime and optimize asset performance, the demand for sophisticated reliability modeling tools has surged. This trend is particularly pronounced in sectors such as aerospace & defense, automotive, and energy & utilities, where the cost of failure can be substantial both financially and reputationally. The adoption of digital transformation initiatives and Industry 4.0 practices has further accelerated the integration of reliability analysis into core operational workflows, enabling more accurate forecasting of system behavior and facilitating proactive decision-making.

Another key growth factor for the Reliability Block Diagram Software market is the increasing regulatory scrutiny and industry standards regarding safety and reliability. Regulatory bodies across the globe are mandating stringent risk assessment and reliability verification processes, especially in industries such as healthcare, manufacturing, and energy. This has compelled organizations to invest in advanced reliability block diagram solutions capable of generating comprehensive reports, performing scenario analyses, and ensuring compliance with international standards like ISO 26262, IEC 61508, and MIL-STD-882. The ability of these software platforms to seamlessly integrate with existing enterprise resource planning (ERP) and asset management systems further enhances their value proposition, driving widespread adoption across large enterprises and small-to-medium businesses alike.

Furthermore, the proliferation of cloud-based deployment models has democratized access to Reliability Block Diagram Software, making it more affordable and scalable for organizations of all sizes. Cloud solutions offer significant advantages in terms of flexibility, collaboration, and real-time data access, allowing geographically dispersed teams to work together on reliability projects with ease. This shift towards cloud-based platforms is expected to drive market growth, particularly in emerging economies where capital expenditure constraints have traditionally limited the adoption of advanced engineering software. The convergence of artificial intelligence, machine learning, and big data analytics with reliability modeling is also opening new avenues for innovation, enabling predictive insights and automated optimization of complex systems.

Regionally, North America continues to dominate the Reliability Block Diagram Software market due to its mature industrial base, significant investments in technology, and the presence of leading software vendors. However, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, infrastructure development, and increasing awareness of the benefits of reliability engineering. European markets are also showing steady growth, supported by strong regulatory frameworks and a focus on sustainability and operational excellence. The Middle East & Africa and Latin America regions are gradually catching up, driven by investments in energy, utilities, and transportation infrastructure. Overall, the global outlook for the market remains highly optimistic, with ample opportunities for innovation and expansion across all regions.

Component Analysis

The Component segment of the Reliability Block Diagram Software market is bifurcated into Software and Services. The Software sub-segment encompasses standalone reliability modeling tools, integrated platforms, and specialized modules designed for various industry applications. These

AI in Smart Grids Market Outlook

According to our latest research, the AI in Smart Grids market size reached USD 2.84 billion globally in 2024, reflecting a robust adoption across key regions. The market is expected to expand at a CAGR of 20.7% from 2025 to 2033, reaching a forecasted value of USD 18.04 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing integration of artificial intelligence technologies to address energy efficiency, grid reliability, and the complexities of renewable energy integration. As per the latest research, the convergence of digital transformation initiatives in the energy sector and the necessity to manage distributed energy resources are acting as significant catalysts for the accelerated adoption of AI in smart grids worldwide.

One of the primary growth factors fueling the AI in Smart Grids market is the global shift toward renewable energy sources and the decentralization of power generation. With the proliferation of distributed energy resources such as solar and wind, grid operators are facing unprecedented challenges in maintaining grid stability and reliability. AI-powered solutions enable real-time data analysis, predictive modeling, and intelligent automation, which are essential for balancing supply and demand, integrating intermittent renewables, and minimizing operational risks. The ability of AI to process vast datasets from smart meters, sensors, and IoT devices ensures enhanced situational awareness and more informed decision-making, thereby supporting the evolving needs of modern power grids.

Another significant driver is the increasing demand for energy efficiency and the need to reduce operational costs across utilities and grid operators. AI-based analytics can optimize energy consumption patterns, detect anomalies, and predict equipment failures, resulting in substantial cost savings and improved asset management. The adoption of advanced demand response strategies powered by machine learning algorithms allows utilities to forecast demand spikes and incentivize consumers to adjust their usage, thereby alleviating grid stress during peak periods. Additionally, AI-driven predictive maintenance minimizes downtime and extends the lifespan of critical grid infrastructure, further enhancing the economic viability of smart grid investments.

The evolution of regulatory frameworks and government initiatives supporting smart grid deployments are also pivotal in accelerating market growth. Policymakers across North America, Europe, and Asia Pacific are introducing mandates for grid modernization, energy transition, and carbon reduction, which necessitate the integration of AI technologies for compliance and reporting. The availability of funding for pilot projects, research, and public-private partnerships is fostering innovation and encouraging utilities to adopt AI-driven solutions at scale. Furthermore, the growing focus on cybersecurity and fraud detection in smart grids is prompting stakeholders to leverage AI for advanced threat intelligence, anomaly detection, and real-time incident response, thereby safeguarding critical infrastructure against emerging risks.

From a regional perspective, North America currently leads the AI in Smart Grids market, owing to its mature energy infrastructure, early adoption of digital technologies, and significant investments in grid modernization. Europe follows closely, driven by ambitious renewable energy targets and stringent regulatory requirements for carbon neutrality. The Asia Pacific region is witnessing the fastest growth, propelled by rapid urbanization, rising energy demand, and large-scale government initiatives to upgrade aging grid infrastructure. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in smart grid projects and AI-enabled solutions to address energy access and reliability challenges unique to these regions.

Component Analysis

The component segment of the AI in Smart Grids market is broadly categorized into software, hardware, and services, each playing a pivotal role in the deployment and functionality of intelligent grid solutions. Software solutions, which encompass advanced analytics platforms, machine learning models, and grid management applications, represent the largest share of the market. These software tools enable utilities to harnes

Net-Income-Including-Non-Controlling-Interests Time Series for C3 Ai Inc. C3.ai, Inc. operates as an enterprise artificial intelligence application software company. The company provides C3 agentic AI platform, an application development and runtime environment that enables customers to design, develop, and deploy enterprise AI applications; C3 AI CRM Suite, a customer relationship management solution; C3 Generative AI that enables to locate, retrieve, present information, disparate data stores, applications, and enterprise information systems; C3 AI Health Suite to accelerate healthcare innovation and streamline drug development; and C3 AI Financial Services Suite. Its C3 AI Applications include C3 AI Asset Performance suite, which consists of C3 AI Reliability, C3 AI Process Optimization, and C3 AI Energy Management applications that drives enterprise asset performance, reduces downtime, and improves process efficiency. The company's C3 AI Supply Chain Suite comprises C3 AI Supply Network Risk, C3 AI Inventory Optimization, C3 AI Demand Forecasting, C3 AI Production Schedule Optimization, and C3 AI Sourcing Optimization solutions; C3 AI Sustainability Suite includes C3 AI ESG and C3 AI Energy Management applications to decrease greenhouse gas and GHG, emissions; and C3 AI Defense & Intelligence Suite. It provides C3 AI State and Local Government Suite that includes various applications, such as C3 Law Enforcement for state, county, and municipal law enforcement agencies; C3 AI Residential Property Appraisal and C3 AI Commercial Property Appraisal for county property assessors and appraisers; and C3 Generative AI for Government Programs and C3 Generative AI for Constituent Services for federal, state, and local governments. It has strategic partnerships with Microsoft Azure, AWS, Google Cloud, McKinsey & Company, Baker Hughes, Booz Allen, and others. The company has a strategic alliance with SMX Group, LLC for the development of mission critical AI in the secure environments. The company was formerly known as C3 IoT, Inc. and changed its name to C3.ai, Inc. in June 2019. C3.ai, Inc. w

Traditional Chinese Medicine (TCM) utilizes multi-metabolite and multi-target interventions to address complex diseases, providing advantages over single-target therapies. However, the active metabolites, therapeutic targets, and especially the combination mechanisms remain unclear. The integration of advanced data analysis and nonlinear modeling capabilities of artificial intelligence (AI) is driving the transformation of TCM into precision medicine. This review concentrates on the application of AI in TCM target prediction, including multi-omics techniques, TCM-specialized databases, machine learning (ML), deep learning (DL), and cross-modal fusion strategies. It also critically analyzes persistent challenges such as data heterogeneity, limited model interpretability, causal confounding, and insufficient robustness validation in practical applications. To enhance the reliability and scalability of AI in TCM target prediction, future research should prioritize continuous optimization of the AI algorithms using zero-shot learning, end-to-end architectures, and self-supervised contrastive learning.

The evaluation of medical Artificial Intelligence (AI) systems presents significant challenges, with performance often varying drastically across studies. This narrative review identifies prompt quality—the way questions are formulated for the AI—as a critical yet under-recognized variable influencing these outcomes. The analysis explores scientific literature published between January 2018 and August 2025 to investigate the impact of prompt engineering on the perceived accuracy and reliability of conversational AI in medicine. Results reveal a “performance paradox,” where AI sometimes surpasses human experts in controlled settings yet underperforms in broader meta-analyses. This inconsistency is strongly linked to the type of prompt used. Critical concerns are highlighted, such as “prompting bias,” which may invalidate study conclusions, and AI “hallucinations” that generate dangerously incorrect information. Furthermore, a significant gap exists between the optimal prompts formulated by experts and the natural queries of the general public, raising issues of safety and health equity. In the end we were interested in finding out what the optimal balance existed between the complexity of a prompt and the value of the generated response, and, in this context, whether we could attempt to define a path toward identifying the best possible prompt.

Asthma is the most common chronic disease of childhood, characterized by symptoms such as wheezing, shortness of breath, and coughing. With the advancement of technology, artificial intelligence (AI) applications are increasingly being used in various fields, among which ChatGPT is one of the most widely utilized. The aim of this study is to evaluate the reliability, quality, and readability of the answers provided by the ChatGPT-4o application to questions related to pediatric asthma. The ChatGPT-4o application was used to record answers to 25 of the most frequently asked questions about asthma in children. To determine the quality and reliability of the answers, we used the Global Quality Scale and modified DISCERN tool. We tested readability using seven indices: Automated Readability Index, Flesch Reading Ease Score, Flesch-Kincaid Grade Level (FKGL), Gunning Fog Readability Index, Simple Measure of Gobbledygook, Coleman–Liau Readability Index, and Linsear Write Formula. The answers provided by the ChatGPT-4o application to questions about childhood asthma were found to have good reliability (88% by the first evaluator and 84% by the second evaluator) and high quality (88% by both evaluators). The application scored 10.77 ± 1.58 on the FKGL scale, and in conjunction with the other indices, the results indicated that the answers required a high level of reading proficiency. Artificial intelligence can be a reliable tool for parents in providing information about pediatric asthma. However, these findings suggest that readability issues may hinder the clinical application of AI-generated content in asthma diagnosis and treatment.

Shadow Mode Testing for AI Decisions Market Outlook

According to our latest research, the Global Shadow Mode Testing for AI Decisions market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a CAGR of 23.5% during 2024–2033. A key driver fueling this impressive growth is the increasing demand for explainable and trustworthy AI systems across high-stakes industries such as healthcare, finance, and autonomous vehicles. Shadow mode testing allows organizations to validate AI decision-making in real-world scenarios without impacting actual operations, thereby accelerating safe AI adoption while ensuring regulatory compliance and operational reliability. As businesses worldwide embrace AI-driven automation, shadow mode testing is becoming indispensable for risk mitigation, model transparency, and continuous improvement of AI algorithms.

Regional Outlook

North America currently commands the largest share of the Shadow Mode Testing for AI Decisions market, accounting for over 38% of global revenue in 2024. This dominance stems from the region's mature technology ecosystem, robust investments in artificial intelligence, and stringent regulatory frameworks—particularly in sectors such as banking, healthcare, and autonomous vehicles. The presence of major technology players, advanced research institutions, and a proactive stance on AI safety and ethics further bolster North America’s leadership position. The United States, in particular, has witnessed widespread adoption of shadow mode testing as organizations seek to ensure compliance with evolving data privacy and algorithmic accountability standards. The region’s early embrace of digital transformation and its focus on operational resilience have solidified its role as the primary hub for shadow mode testing innovation and deployment.

The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of 27.8% from 2024 to 2033. This remarkable growth is driven by rapid digitalization, burgeoning investments in AI infrastructure, and increasing government initiatives aimed at fostering safe AI adoption across sectors such as manufacturing, finance, and telecommunications. Countries like China, Japan, and South Korea are at the forefront, leveraging shadow mode testing to validate AI models before full-scale deployment, especially in mission-critical applications like smart factories and autonomous mobility. The region’s thriving startup ecosystem, coupled with rising collaboration between academia and industry, is accelerating the pace of innovation. Additionally, the growing focus on AI ethics and the need to address algorithmic biases are prompting organizations to adopt robust testing frameworks, further propelling market expansion in Asia Pacific.

In contrast, emerging economies in Latin America, the Middle East, and Africa are gradually integrating shadow mode testing for AI decisions, although adoption rates remain relatively modest due to infrastructural and regulatory challenges. Limited access to advanced AI infrastructure, skills gaps, and inconsistent policy frameworks have slowed the pace of market penetration. However, localized demand is increasing, particularly as governments and enterprises recognize the importance of safe AI deployment in sectors such as healthcare, retail, and public services. Policy reforms and international partnerships are beginning to address some of these barriers, paving the way for more widespread adoption. As these regions continue to develop their digital capabilities, shadow mode testing is expected to play a critical role in ensuring the reliability and fairness of AI-driven decision-making.

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