The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

International Journal of Artificial Intelligence Impact Factor 2024-2025 - ResearchHelpDesk - The main aim of the International Journal of Artificial Intelligence™ (ISSN 0974-0635) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Artificial Intelligence (IJAI) is a peer-reviewed online journal and is published in Spring and Autumn i.e. two times in a year. The International Journal of Artificial Intelligence (ISSN 0974-0635) was reviewed, abstracted and indexed in the past by the INSPEC The IET, SCOPUS (Elsevier Bibliographic Databases), Zentralblatt MATH (io-port.net) of European Mathematical Society, Indian Science Abstracts, getCITED, SCImago Journal & Country Rank, Newjour, JournalSeek, Math-jobs.com’s Journal Index, Academic keys, Ulrich's Periodicals Directory, IndexCopernicus, and International Statistical Institute (ISI, Netherlands)Journal Index. The IJAI is already in request process to get reviewed, abstracted and indexed by the Clarivate Analytics Web of Science (Also known as Thomson ISI Web of Knowledge SCI), Mathematical Reviews and MathSciNet of American Mathematical Society, and by other agencies.

Objectives: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses. Study design: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals’ JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. Results: Of the 3999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n=64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs 3.5, P<0.001), and screened more abstracts per author (302.2 vs 140.3, P=0.009) and per included study (189.0 vs 365.8, P<0.001) while inspecting less full texts per author (5.3 vs 14.0, P=0.005). No differences were found in citation counts (0.5 vs 0.6, P=0.600), inspected full texts per included study (3.8 vs 3.4, P=0.481), completion times (74.0 vs 123.0, P=0.205) or AMSTAR-2 (7.5 vs 6.3, P=0.119). Conclusion: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews. Methods Dataset produced from bibliographic references to COVID-19 systematic reviews obtained from the COVID-19 Living Overview of Evidence database. We obtained accessibility information and download links from the Unpaywall database, and indexed the resulting downloaded files with the OpenSemanticSearch search engine.

International journal of machine learning and computing Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Machine Learning and Computing - IJMLC is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of machine learning and computing. The focus is to publish papers on state-of-the-art machine learning and computing. Submitted papers will be reviewed by technical committees of the Journal and Association. The audience includes researchers, managers and operators for machine learning and computing as well as designers and developers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing. IJMLC is an open access journal which focus on publishing original and peer reviewed research papers on all aspects of machine learning and computing. And the topics include but not limited to: Adaptive systems Business intelligence Biometrics Bioinformatics Data and web mining Intelligent agent Financial engineering Inductive learning Geo-informatics Pattern Recognition Logistics Intelligent control Media computing Neural net and support vector machine Hybrid and nonlinear system Fuzzy set theory, fuzzy control and system Knowledge management Information retrieval Intelligent and knowledge based system Rough and fuzzy rough set Networking and information security Evolutionary computation Ensemble method Information fusion Visual information processing Computational life science Abstract & indexing Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.

BackgroundInpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations.MethodsFollowing Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).ResultsWe identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data.ConclusionDespite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice.Systematic review registration[www.crd.york.ac.uk/prospero/], identifier [CRD42022310410].

AI-Enhanced Product Review Moderation Market Outlook

According to our latest research, the AI-Enhanced Product Review Moderation market size reached USD 1.92 billion globally in 2024, with a robust year-on-year growth driven by the increasing adoption of artificial intelligence in digital commerce. The market is expected to expand at a CAGR of 17.2% from 2025 to 2033, reaching a projected value of USD 8.45 billion by the end of the forecast period. This exceptional growth is primarily attributed to the exponential rise in online transactions and the critical need for brands and platforms to maintain the integrity, authenticity, and trustworthiness of user-generated content. As per our latest research, the integration of advanced AI technologies for automated content moderation is transforming customer experience and operational efficiency across diverse industry verticals.

The growth trajectory of the AI-Enhanced Product Review Moderation market is underpinned by several compelling factors. Firstly, the surge in e-commerce and digital retail has precipitated an unprecedented volume of user-generated product reviews, making manual moderation both resource-intensive and inefficient. AI-driven moderation tools leverage natural language processing (NLP), sentiment analysis, and machine learning algorithms to automatically detect spam, inappropriate content, and fake reviews in real time. This not only streamlines the review management process but also ensures a safer and more reliable shopping environment for consumers. As a result, businesses are increasingly investing in AI-enhanced moderation solutions to safeguard their brand reputation, comply with evolving regulatory requirements, and foster consumer trust.

Another significant driver accelerating market growth is the rapid evolution of AI technologies, especially in the context of deep learning and contextual analysis. Modern AI moderation platforms are now capable of understanding nuanced language, cultural references, and even subtle forms of manipulation or bias in product reviews. This technological sophistication allows for more accurate and context-aware moderation, minimizing the risk of false positives or negatives that can undermine user experience. Furthermore, AI-powered solutions offer scalability and adaptability, enabling businesses to handle fluctuating review volumes during peak seasons or promotional events without compromising on moderation quality. This operational agility is a key differentiator in todayÂ’s highly competitive digital marketplace.

Moreover, the rising emphasis on regulatory compliance and data privacy is catalyzing the adoption of AI-enhanced review moderation tools. Governments and industry bodies across the globe are introducing stricter norms to combat deceptive practices, misinformation, and harmful content in online reviews. AI-based moderation platforms offer robust audit trails, transparency, and customizable filters that help organizations adhere to these regulations efficiently. Additionally, the growing awareness among consumers regarding the prevalence of fake reviews and their impact on purchasing decisions is prompting brands to prioritize trustworthy review ecosystems. This shift in consumer expectations, combined with regulatory pressure, is fueling sustained investment in AI-enhanced moderation technologies.

AI-Generated Product Review systems are becoming increasingly prevalent as businesses strive to enhance the authenticity and reliability of user feedback. These systems utilize advanced algorithms to generate product reviews that mimic human writing, providing a scalable solution for platforms with high volumes of products and limited customer engagement. By leveraging AI-generated reviews, businesses can ensure that their product pages are populated with informative content, even in the absence of substantial user-generated input. This approach not only helps in maintaining a vibrant review ecosystem but also aids in search engine optimization, driving more traffic to product pages. However, it is crucial for companies to balance AI-generated content with genuine user reviews to maintain trust and transparency with consumers.

From a regional perspective, North America currently leads the AI-Enhanced Product Review Moderation market due to the early adoption of AI technologies, a mature

This dataset contains bibliometric and altimetric data from 175 articles on the use of Artificial Intelligence (AI) in cariology. It includes article titles, authors, study designs, diagnostic methods, citation counts, journal impact factors, publication years, and author affiliations. The data were collected on February 12, 2024, from the Web of Science Core Collection and Dimensions databases. Additionally, the dataset includes the flowchart following the BIBLIO methodology and the corresponding checklist, providing a detailed overview of the data collection and analysis process.

AI-Generated Product Review Market Outlook

According to our latest research, the AI-Generated Product Review market size reached USD 1.12 billion globally in 2024, with a robust growth trajectory reflected in a CAGR of 27.6% from 2025 to 2033. This remarkable expansion is driven by the increasing integration of artificial intelligence in digital commerce and the rising demand for scalable, authentic, and personalized product feedback. By 2033, the market is projected to attain a value of USD 9.14 billion, underscoring the transformative impact of AI-driven content generation on consumer engagement and purchasing decisions across industries.

The primary growth factor fueling the AI-Generated Product Review market is the exponential rise of e-commerce and digital retail platforms globally. As online shopping becomes ubiquitous, consumers are increasingly reliant on product reviews to inform their purchasing decisions. Retailers and brands are leveraging AI-powered review generation tools to address the challenge of review scarcity, mitigate fraudulent or biased feedback, and deliver a consistent stream of high-quality, relevant reviews. These AI solutions utilize natural language processing (NLP) and machine learning algorithms to generate reviews that mimic human tone, style, and sentiment, thereby enhancing consumer trust and improving conversion rates. Furthermore, AI-generated reviews enable rapid scaling across vast product catalogs, providing comprehensive coverage and supporting global expansion efforts.

Another significant driver is the growing sophistication and accessibility of AI technologies. Advances in generative AI, particularly large language models, have made it possible to create nuanced, context-aware product reviews that closely resemble authentic customer feedback. This technological evolution is lowering barriers for small and medium enterprises (SMEs) to adopt such solutions, empowering them to compete with larger players by enriching their digital presence. Additionally, the integration of AI-generated reviews with omnichannel marketing strategies allows brands to maintain a unified voice across multiple touchpoints, including websites, social media, and mobile apps. This seamless integration not only streamlines content creation but also enhances the overall customer experience, fostering brand loyalty and repeat purchases.

Regulatory compliance and ethical considerations are also shaping the market landscape. As governments and industry bodies introduce guidelines to ensure transparency and authenticity in online reviews, AI-generated product review providers are investing in solutions that clearly disclose the synthetic nature of the content. These measures help mitigate risks associated with consumer deception and legal liabilities, while simultaneously building trust with end-users. The market is also witnessing the emergence of hybrid models that blend AI-generated content with human moderation, striking a balance between scalability and credibility. This trend is particularly pronounced in regulated industries such as healthcare and automotive, where the accuracy and reliability of product feedback are paramount.

From a regional perspective, North America holds the largest share of the AI-Generated Product Review market, accounting for over 38% of global revenue in 2024. The region's dominance is attributed to the high concentration of e-commerce giants, advanced AI infrastructure, and proactive regulatory frameworks. Europe follows closely, driven by stringent consumer protection laws and a strong emphasis on digital innovation. Meanwhile, the Asia Pacific region is experiencing the fastest growth, propelled by rapid digitalization, expanding internet penetration, and the proliferation of online retail platforms. Latin America and the Middle East & Africa are also witnessing steady adoption, supported by increasing investments in digital transformation and a burgeoning middle-class consumer base.

Component Analysis

The AI-Generated Product Review market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment encompasses AI algorithms, natural language processing engines, and review generation platforms that automate the creation of product feedback. This segment currently dominates the market, capturing more than 65% of total revenue in 2024. The surge in demand

Journal of theoretical and applied computer science Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Theoretical and Applied Computer Science is published by the Computer Science Commision, operating within the Gdansk Branch of Polish Academy of Sciences and located in Szczecin, Poland. JTACS is an open access journal, publishing original research and review papers from the variety of subdiscplines connected to theoretical and applied computer science, including the following: Artificial intelligence Computer modelling and simulation Data analysis and classification Pattern recognition Computer graphics and image processing Information systems engineering Software engineering Computer systems architecture Distributed and parallel processing Computer systems security Web technologies Bioinformatics Abstract and indexing Doaj (Dicretroy of open access journals) Index copurnicus Baztech Google scholar

AI After-Action Review Engine Market Outlook

According to our latest research, the global AI After-Action Review Engine market size reached USD 1.42 billion in 2024, demonstrating robust expansion driven by the increasing adoption of artificial intelligence in performance evaluation and training environments. The market is projected to grow at a CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 7.38 billion by 2033. The primary growth factor fueling this trajectory is the rising demand for real-time, data-driven insights to optimize decision-making and operational efficiency across diverse sectors such as military, corporate, healthcare, and education.

One of the most significant growth drivers in the AI After-Action Review Engine market is the escalating complexity of operations across industries. As organizations face increasingly dynamic and unpredictable scenarios, the need for advanced tools to analyze, interpret, and learn from past actions has become paramount. AI-powered after-action review engines facilitate the systematic analysis of complex events, offering granular insights that traditional review processes often overlook. These engines leverage advanced machine learning and natural language processing capabilities to identify patterns, root causes, and actionable recommendations, thereby enhancing organizational learning and resilience. The growing reliance on data-driven decision-making, coupled with the proliferation of digital transformation initiatives, further amplifies the demand for sophisticated after-action review solutions.

Another key factor bolstering market growth is the widespread integration of AI technologies into training and simulation environments. In sectors such as defense, emergency response, and healthcare, the ability to rapidly analyze actions and outcomes post-exercise is critical for continuous improvement and mission readiness. AI After-Action Review Engines enable automated, unbiased assessments of performance, reducing the time and resources required for manual reviews. This automation not only accelerates the feedback loop but also ensures consistency and objectivity in evaluations. The increasing focus on operational efficiency and cost reduction, especially in resource-constrained sectors, is prompting organizations to invest in AI-driven review solutions that deliver measurable improvements in training outcomes and organizational preparedness.

The surge in remote work and distributed teams has also contributed to the growing adoption of AI After-Action Review Engines. As organizations adapt to hybrid and remote operational models, the challenge of capturing, analyzing, and disseminating lessons learned from virtual or geographically dispersed teams has intensified. AI-powered review engines offer scalable, cloud-based platforms that facilitate seamless collaboration and knowledge sharing across locations. These solutions enable organizations to maintain high standards of performance evaluation and continuous learning, regardless of physical boundaries. The convergence of AI, cloud computing, and collaborative technologies is expected to further accelerate market expansion in the coming years.

From a regional perspective, North America currently dominates the AI After-Action Review Engine market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed significant adoption in defense and corporate training environments, driven by substantial investments in AI research and development. Europe is experiencing steady growth, fueled by increasing demand in healthcare and emergency response applications. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid digitalization, government initiatives, and expanding defense budgets. Latin America and the Middle East & Africa are also showing promising potential, albeit at a relatively nascent stage. The regional dynamics underscore the global relevance and transformative impact of AI-powered after-action review solutions across diverse sectors.

Component Analysis

The Component segment of the AI After-Action Review Engine market is broadly categorized into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The software component, which includes AI algorithms, analytics platforms, and user interfaces, represents the largest share of the market. This dominance can be attributed to

EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation Processes Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits. During the recalculation process, the program implements specific measures based on the outcomes: - In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall. - Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount. - When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged. The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change. This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement. This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM). These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards. In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted. The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years. This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks. A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated.

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.

AI-Powered Contract Review for Banking Market Outlook

According to our latest research, the AI-powered contract review for banking market size reached USD 1.37 billion globally in 2024, driven by the increasing integration of artificial intelligence technologies in the financial sector. The market is exhibiting robust momentum, with a recorded CAGR of 24.1% during the forecast period. By 2033, the global market is projected to attain a value of USD 10.83 billion, reflecting the transformative impact of AI on contract analysis, risk mitigation, and compliance management within banking institutions. This growth is primarily attributed to the surging demand for automation, accuracy, and efficiency in handling complex banking contracts, underpinned by evolving regulatory requirements and the necessity for enhanced fraud detection mechanisms.

One of the principal growth factors for the AI-powered contract review for banking market is the escalating volume and complexity of contracts managed by financial institutions. Banks are increasingly dealing with a myriad of contracts related to loans, derivatives, partnerships, and customer agreements, each subject to stringent regulatory scrutiny. The manual review of these documents is not only time-consuming but also prone to human errors, potentially leading to compliance lapses and operational risks. AI-powered solutions leverage natural language processing (NLP) and machine learning algorithms to automate contract extraction, clause identification, and risk assessment. This automation significantly reduces turnaround times, enhances accuracy, and enables banks to efficiently scale their operations without proportionally increasing their workforce, thereby driving widespread adoption.

Another significant factor fueling market expansion is the growing emphasis on regulatory compliance and risk management. Regulatory bodies across the globe are intensifying their oversight, compelling banks to adopt advanced technologies for compliance management and audit preparedness. AI-powered contract review platforms offer real-time monitoring and flagging of non-compliant clauses, facilitating proactive risk mitigation and ensuring adherence to evolving legal frameworks. These platforms also provide comprehensive audit trails, enabling banks to demonstrate compliance during regulatory inspections. The ability of AI solutions to adapt to new regulations and learn from historical data further enhances their value proposition, making them indispensable tools in the modern banking landscape.

Additionally, the integration of AI in contract review processes is playing a pivotal role in fraud detection and prevention. Traditional manual processes often fail to identify subtle patterns indicative of fraudulent activities, such as duplicate contracts, unauthorized amendments, or anomalous terms. AI-powered platforms utilize advanced analytics and pattern recognition to scrutinize vast volumes of contract data, uncovering hidden risks and potential fraud scenarios. This proactive approach not only safeguards banks from financial losses but also strengthens customer trust and institutional reputation. The continuous advancements in AI technology, coupled with increasing investments by banks in digital transformation initiatives, are expected to further accelerate market growth in the coming years.

From a regional perspective, North America currently leads the AI-powered contract review for banking market, owing to its mature banking infrastructure, early adoption of AI technologies, and stringent regulatory environment. Europe follows closely, driven by robust compliance mandates and a strong focus on data privacy. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitization of banking services, expanding fintech ecosystem, and increasing regulatory pressures. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a slower pace, as regional banks recognize the strategic benefits of AI-driven contract management in mitigating risks and enhancing operational efficiency.

Component Analysis

The Component segment of the AI-powered contract review for banking market is bifurcated into Software and Services, each playing a distinct role in driving market adoption and value creation. The software segment encompasses advanced AI platforms equipped with

According to Cognitive Market Research, the global Artificial Intelligence in Drug Discovery market size is USD 815.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 40.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 326.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 38.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 244.56 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 187.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 42.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 40.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 39.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 16.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 39.7% from 2024 to 2031.
The services held the highest Artificial Intelligence in Drug Discovery market revenue share in 2024.

Market Dynamics of Artificial Intelligence in Drug Discovery Market

Key Drivers for Artificial Intelligence in Drug Discovery Market

Increasing Demand for Personalized Medicine will Boost the Market Growth

Customised medication, fitting medicines to individual patients in the opinion of their hereditary cosmetics and different elements, is picking up speed because of its capability to develop results further and limit unfavorable impacts. Simulated intelligence assumes an urgent role in this change in outlook by dissecting tremendous datasets enveloping genomics, proteomics, and clinical records. AI calculations filter through this information to recognize examples and connections, supporting the revelation of biomarkers for sickness inference and guessing. Regular language handling empowers the abstraction of significant experiences from unstructured clinical notes and examination writing. By utilizing computer-based intelligence, specialists can foster designated treatments that address the particular sub-atomic qualities of a patient's illness, improving treatment viability and patient outcomes in a period progressively centered around customized medical services.

Growing Complexity of Drug Development Process will Augment the Market Growth

Conventional medication discovery faces difficulties originating from the difficulty of illnesses, high disappointment rates in clinical preliminaries, and rising improvement costs. Simulated intelligence offers inventive answers to assist different phases of medication advancement by outfitting the force of computational calculations and huge information investigation. AI calculations break down different datasets, for example, genomic successions and compound designs, to anticipate drug-target collaborations and distinguish promising competitors. Besides, artificial intelligence-driven models smooth out lead streamlining and harmfulness expectations, lessening the time and assets expected for preclinical testing. By speeding up the speed of medication disclosure and advancing asset assignment, artificial intelligence advancements moderate dangers and improve the productivity of medication improvement.

Restraint Factor for the Artificial Intelligence in Drug Discovery Market

Regulatory Compliance and Ethical Considerations will Hinder the Market Growth

One critical limitation in the Man-made reasoning in the medication discovery market is the test of accomplishing adequate brilliance and picture quality in conservative and compact gadgets. Because of their small size and appreciative power sources, Man-made consciousness in Medication Revelation frequently battles to convey a similar degree of splendor and picture lucidity as bigger, fixed projectors. This impediment can obstruct their viability in brilliantly lit conditions or while projecting onto bigger screens, lessening their common sense for specific applications like proficient introductions or outside occasions. While progressions in Drove and laser projection innovation have further developed brilliance levels in Man-made brainpower in Medication Disclosure, accomplishing great pictures without compromising versatility remains a critical test for makers.

Impact of Covid-19 on the...

Nature Medicine Impact Factor 2024-2025 - ResearchHelpDesk - Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine on the basis of its originality, timeliness, interdisciplinary interest and impact on improving human health. Nature Medicine also publishes commissioned content, including News, Reviews and Perspectives, aimed at contextualizing the latest advances in translational and clinical research to reach a wide audience of M.D. and PhD readers. All editorial decisions are made by a team of full-time professional editors. Nature Medicine publishes research that addresses the needs and goals of contemporary medicine. Original research ranges from new concepts in human biology and disease pathogenesis to robust preclinical bases for new therapeutic modalities and drug development to all phases of clinical work, as well as innovative technologies aimed at improving human health. Current areas of interest also include, but are not limited to: Gene and cell therapies Clinical genomics Regenerative medicine High-definition medicine Effects of the environment in human health Artificial intelligence in health care Smart wearable devices Early disease diagnosis Microbiome Aging Nature Medicine also publishes Reviews, Perspectives and other content commissioned from leading scientists in their fields to provide expert and contextualized views of the latest research driving the progress of medicine. The Magazine section is editorially independent and provides topical and timely reporting of upcoming trends affecting medicine, researchers and the general audience.

AI in Social Listening Market Outlook

According to our latest research, the global AI in Social Listening market size reached USD 2.17 billion in 2024, reflecting robust demand for advanced analytics and real-time consumer insights across industries. The market is expected to grow at a CAGR of 23.7% from 2025 to 2033, reaching a forecasted value of USD 16.44 billion by 2033. The rapid adoption of artificial intelligence-driven solutions for sentiment analysis, brand monitoring, and customer engagement is fueling this impressive growth. As organizations increasingly recognize the value of actionable insights derived from social media, the AI in Social Listening market is poised for sustained expansion.

One of the primary growth factors driving the AI in Social Listening market is the exponential increase in social media data and digital interactions. With billions of users actively engaging on platforms such as Twitter, Facebook, Instagram, and LinkedIn, organizations are faced with vast amounts of unstructured data that require sophisticated tools for effective analysis. AI-powered social listening platforms leverage natural language processing (NLP), machine learning, and sentiment analysis to extract meaningful insights from this data, enabling businesses to understand customer preferences, emerging trends, and potential crises in real time. This capability not only enhances customer experience management but also supports proactive decision-making in marketing, product development, and risk mitigation, which is why enterprises across sectors are rapidly adopting these solutions.

Another significant driver is the growing emphasis on brand reputation management in the digital era. In today’s interconnected world, a single negative review or viral post can significantly impact a company’s image and market value. AI in Social Listening empowers organizations to monitor brand mentions, analyze sentiment, and detect potential threats before they escalate. By automating the process of tracking online conversations and identifying influencers or detractors, these platforms allow companies to respond swiftly and strategically. Furthermore, the integration of AI with social listening tools enhances the accuracy and scalability of monitoring efforts, making it feasible for businesses of all sizes to safeguard their reputation and maintain a competitive edge.

The proliferation of omnichannel marketing strategies and personalized customer engagement initiatives has further accelerated the adoption of AI in Social Listening. As businesses strive to deliver tailored experiences and targeted campaigns, the need for real-time, data-driven insights becomes paramount. AI-driven social listening platforms provide granular analysis of customer sentiment, preferences, and feedback across multiple channels, enabling marketers to refine their messaging, optimize content, and measure campaign effectiveness. This capability is particularly valuable in highly competitive sectors such as retail, e-commerce, and financial services, where customer loyalty and brand differentiation are critical to success. As a result, investment in AI-powered social listening solutions continues to rise, driving market growth.

From a regional perspective, North America currently dominates the AI in Social Listening market, accounting for the largest share in 2024. The region's leadership is attributed to the high concentration of technology innovators, advanced digital infrastructure, and widespread adoption of AI-driven analytics across industries. Europe follows closely, benefiting from stringent regulatory requirements around data privacy and consumer protection, which have prompted organizations to invest in compliant and sophisticated social listening solutions. The Asia Pacific region is witnessing the fastest growth, fueled by the rapid digitalization of economies, increasing social media penetration, and rising demand for advanced analytics among enterprises. Meanwhile, Latin America and the Middle East & Africa are emerging markets, gradually embracing AI in Social Listening as digital transformation initiatives gain momentum.

Component Analysis

The AI in Social Listening market is segmented by component into software and services, both of which play pivotal roles in shaping the industry landscape. The software segment, comprising AI-powered platforms and tools, holds the lion’s share of the market. These solutions are designed to automate the collection, processing, and analysis of vast vol

Review Management Platform Market Outlook

According to our latest research, the global review management platform market size in 2024 stood at USD 4.3 billion, with robust momentum expected over the coming years. The market is forecasted to reach USD 12.8 billion by 2033, expanding at a strong CAGR of 12.7% from 2025 to 2033. This growth is primarily driven by the increasing digitalization of businesses, the rising importance of online reputation, and the demand for real-time customer feedback analytics across diverse industry verticals. As enterprises and individuals alike recognize the value of managing and leveraging customer reviews, the review management platform market is poised for significant expansion globally.

One of the primary growth drivers for the review management platform market is the accelerating shift towards digital consumerism. As more consumers rely on online reviews to inform purchasing decisions, businesses are compelled to invest in robust review management solutions to monitor, analyze, and respond to customer feedback promptly. The proliferation of e-commerce platforms and digital marketplaces has further heightened the necessity for businesses to maintain a positive online reputation, as even a single negative review can significantly impact sales and brand perception. Review management platforms offer comprehensive tools for aggregating reviews from multiple sources, automating responses, and generating actionable insights, thereby enabling businesses to enhance customer satisfaction and loyalty while mitigating reputational risks.

Another significant factor fueling market growth is the increasing adoption of artificial intelligence and machine learning within review management platforms. These advanced technologies empower organizations to automate sentiment analysis, detect fake or malicious reviews, and derive meaningful trends from vast volumes of unstructured data. As businesses seek to harness the power of data-driven decision-making, the integration of AI-driven analytics within review management platforms has become a key differentiator. This not only streamlines review monitoring processes but also enables predictive analytics, helping organizations proactively address potential issues and capitalize on emerging opportunities. Consequently, the demand for intelligent review management solutions is expected to surge across both large enterprises and small and medium-sized enterprises (SMEs).

Furthermore, the growing regulatory emphasis on transparency and consumer protection is contributing to the expansion of the review management platform market. Regulatory bodies in several regions are introducing guidelines to ensure the authenticity of online reviews and penalize deceptive practices, compelling businesses to adopt compliant review management practices. Platforms equipped with verification mechanisms, fraud detection, and audit trails enable organizations to maintain compliance while fostering trust among customers. Additionally, the increasing prevalence of mobile internet usage and the rise of omni-channel customer engagement strategies are prompting organizations to seek comprehensive solutions that can seamlessly monitor and manage reviews across web, mobile, and social media platforms.

In the realm of review management, Fake Review Detection has become an essential feature for platforms aiming to maintain credibility and trust. As online reviews significantly influence consumer decisions, the presence of fake reviews can distort market perceptions and mislead potential buyers. Advanced algorithms and machine learning models are now being integrated into review management platforms to identify and filter out fraudulent reviews. These technologies analyze patterns, language, and user behavior to detect anomalies that may indicate inauthentic feedback. By ensuring the authenticity of reviews, businesses can protect their reputation and foster genuine customer relationships, ultimately enhancing consumer confidence in their brand.

From a regional perspective, North America currently dominates the review management platform market, accounting for the largest revenue share in 2024. This leadership is attributed to the regionÂ’s advanced digital infrastructure, high internet penetration, and the strong presence of technology-driven enterprises. Europe follows cl

Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits. During the recalculation process, the program implements specific measures based on the outcomes: In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall. Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount. When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged. The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change. This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement. This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM). These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards. In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted. The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years. This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks. A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated.

The Enterprise Internet Reputation Management (EIRM) market is experiencing robust growth, driven by the increasing reliance of businesses on online platforms and the significant impact online reputation has on brand perception, customer acquisition, and overall profitability. The market's expansion is fueled by several key factors, including the rise of social media, the proliferation of online review platforms, and the growing sophistication of online reputation management tools and services. Businesses across diverse sectors are investing heavily in EIRM solutions to proactively monitor their online presence, address negative feedback, and enhance their digital brand image. This proactive approach is becoming increasingly crucial in a world where a single negative review can significantly impact a company's bottom line. The market is segmented by solutions (monitoring, review management, crisis communication), deployment (cloud, on-premise), and industry verticals (finance, healthcare, retail, etc.), each offering unique growth opportunities. We estimate the market size to be approximately $5 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth reflects the continuous evolution of online communication and the increasing need for businesses to effectively manage their digital footprint. The competitive landscape of the EIRM market is characterized by a mix of established players and emerging technology providers. Companies like Reputation.com, SEO Image, WebpageFX, Digital Current, Netmark, FEI, and SEOValley are prominent players, each offering a unique suite of services and catering to specific market segments. The market's future growth will depend on several factors, including technological advancements, increasing awareness of the importance of online reputation management, and the evolution of online review platforms and social media. The ongoing development of artificial intelligence (AI) and machine learning (ML) technologies for reputation monitoring and analysis will further drive market growth by improving efficiency and effectiveness. Furthermore, the increasing regulatory scrutiny around online content and data privacy will shape the strategic direction of EIRM providers, demanding greater transparency and accountability.