BackgroundArtificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.Materials and methodsWe conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.ResultsSixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.ConclusionMost physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

International journal of machine learning and computing Acceptance Rate - 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.

This dataset was curated specifically for the study presented in the paper, Decoding Development: The AI Frontier in Policy Crafting - A Systematic Review. It comprises 208 peer-reviewed publications that examine the integration of artificial intelligence (AI) and machine learning (ML) in policy planning and development. Each dataset entry includes detailed metadata, such as planning context, policy planning stage (e.g., problem diagnosis, resource allocation, outcome projection), specific Sustainable Development Goals (SDGs) addressed, and documented applications of AI/ML models. Systematically constructed, the dataset enables cross-sectional and comparative analyses, capturing the distribution and intensity of AI/ML applications across different stages of the policy planning cycle and economic contexts. By organizing data on each publication’s thematic focus and methodological approaches, this dataset facilitates a nuanced analysis of research trends, identifies existing gaps, and examines the role of smart algorithms in advancing development-oriented policy.

The Artificial Intelligence (AI) in Law market, currently valued at approximately $953 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.4% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of legal data, coupled with the need for enhanced efficiency and accuracy in legal processes, is fueling demand for AI-powered solutions. Law firms and legal departments are increasingly adopting AI-driven tools for tasks such as contract review, legal research, due diligence, and e-discovery, leading to reduced operational costs and improved decision-making. Furthermore, the development of sophisticated natural language processing (NLP) and machine learning (ML) algorithms is enabling more precise and insightful analysis of complex legal documents, ultimately enhancing the quality and speed of legal services. The market's growth is also being propelled by advancements in cloud computing, which provides scalable and cost-effective infrastructure for AI applications in the legal sector. However, the market faces certain challenges. Data privacy concerns, especially regarding sensitive client information, remain a significant hurdle. Furthermore, the need for robust validation and verification of AI-driven legal insights is critical to maintain trust and ensure accuracy. The high cost of implementing and maintaining AI systems, as well as the requirement for specialized expertise in both law and AI, may also limit adoption, particularly among smaller legal firms. Despite these restraints, the long-term outlook for the AI in Law market remains positive, with continuous innovation and increasing market acceptance anticipated to drive substantial growth over the forecast period. Major players like AIBrain, Amazon, Anki, CloudMinds, DeepMind, Google, Facebook, IBM, Iris AI, Apple, Microsoft, and Intel are actively contributing to this evolution through their respective AI solutions tailored for the legal industry.

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...

According to Cognitive Market Research, the global Artificial Intelligence in Marketing Market size is USD 12.7 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 23.8% from 2024 to 2031.

Market Dynamics of Artificial Intelligence in Marketing Market

Key Drivers for Artificial Intelligence in Marketing Market



  Increasing demand for predictive analysis - AI can predict consumer behavior, such as purchasing habits and churn rates. This enables marketers to anticipate customer requirements and preferences, allowing them to solve concerns and provide relevant solutions ahead of time. AI allows marketers to provide highly tailored information and offers to individual customers based on their interests, purchasing history, and behavior. Personalization improves consumer engagement, contentment, and loyalty, resulting in more conversions and revenue. As a result, the market is growing due to increased demand for personalization and predictive analytics.


  Rapid adoption of artificial intelligence in the healthcare Application




Key Restraints for Artificial Intelligence in Marketing Market



  Cost and data privacy issues


  Maintaining data privacy and security concerns

Introduction of the Artificial Intelligence in Marketing Market

Artificial intelligence (AI) in marketing is the incorporation of advanced algorithms and machine learning techniques into various marketing processes and tactics. This cutting-edge technology lets businesses to use data-driven insights, automate repetitive operations, and provide personalized experiences to their target audience, resulting in higher customer engagement, efficiency, and ROI. AI's applicability in marketing is diverse, ranging from monitoring consumer behavior and predicting trends to optimizing ad campaigns and improving customer service. The growing usage of artificial intelligence and machine learning to provide social networking platform acceptance, tailored consumer experiences, and the growth of e-commerce are the main drivers driving the market's development.

This is the data associated with the literature review presented in the paper “The Two Faces of AI in Green Mobile Computing:
A Literature Review” accepted at SEAA 2023.

No description was included in this Dataset collected from the OSF

This dataset was curated specifically for the study presented in the paper, Decoding Development: The AI Frontier in Policy Crafting - A Systematic Review. It comprises 208 peer-reviewed publications that examine the integration of artificial intelligence (AI) and machine learning (ML) in policy planning and development. Each dataset entry includes detailed metadata, such as planning context, policy planning stage (e.g., problem diagnosis, resource allocation, outcome projection), specific Sustainable Development Goals (SDGs) addressed, and documented applications of AI/ML models. Systematically constructed, the dataset enables cross-sectional and comparative analyses, capturing the distribution and intensity of AI/ML applications across different stages of the policy planning cycle and economic contexts. By organizing data on each publication’s thematic focus and methodological approaches, this dataset facilitates a nuanced analysis of research trends, identifies existing gaps, and examines the role of smart algorithms in advancing development-oriented policy.

AI in Ratings & Reviews Market Outlook

According to our latest research, the global AI in Ratings & Reviews market size reached USD 1.9 billion in 2024, driven by rapid digital transformation and the increasing adoption of artificial intelligence across various industries. The market is projected to grow at a robust CAGR of 22.3% from 2025 to 2033, with the market size expected to reach USD 14.2 billion by 2033. Key growth factors include the surge in online consumer activity, the need for real-time sentiment analysis, and the growing importance of data-driven decision-making in both B2B and B2C environments. The proliferation of e-commerce platforms and the hospitality sector’s focus on customer experience are further fueling demand for AI-powered ratings and reviews solutions.

One of the primary growth drivers for the AI in Ratings & Reviews market is the exponential rise in online transactions and consumer engagement across digital platforms. As consumers increasingly rely on digital channels for purchasing decisions, businesses are compelled to leverage AI to analyze vast volumes of customer feedback efficiently. AI-powered tools can extract actionable insights from unstructured data, enabling organizations to enhance customer experience, improve product offerings, and optimize marketing strategies. The ability of AI to process and interpret reviews in multiple languages and across different platforms further amplifies its value for global enterprises, making it an indispensable component of modern customer experience management.

Another significant factor propelling market growth is the shift towards personalized and real-time engagement. AI-driven ratings and reviews platforms empower organizations to provide tailored recommendations, detect fraudulent reviews, and respond promptly to customer concerns. This real-time analysis not only boosts customer satisfaction but also helps companies build trust and credibility in a competitive marketplace. Moreover, advancements in natural language processing (NLP) and machine learning have enhanced the accuracy and reliability of sentiment analysis, enabling businesses to gain deeper insights into customer preferences and pain points. As a result, industries such as healthcare, automotive, and media & entertainment are increasingly integrating AI into their ratings and reviews systems to maintain a competitive edge.

The growing emphasis on regulatory compliance and data privacy is also shaping the evolution of the AI in Ratings & Reviews market. Organizations are investing in AI solutions that ensure transparency, fairness, and accountability in the review process. This is particularly crucial in sectors like healthcare and finance, where unbiased and authentic feedback is vital for decision-making. The adoption of AI-powered moderation tools helps filter out inappropriate or fraudulent content, protecting brand reputation and fostering consumer trust. Furthermore, the integration of AI with existing CRM and analytics platforms streamlines workflow automation, reduces operational costs, and enhances overall business efficiency.

From a regional perspective, North America continues to dominate the market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology providers, high digital literacy, and a mature e-commerce ecosystem contribute to the region’s leadership. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, expanding internet penetration, and the emergence of new digital business models. Latin America and the Middle East & Africa are also experiencing steady growth, supported by increasing investments in digital infrastructure and the rising popularity of online marketplaces.

Component Analysis

The AI in Ratings & Reviews market is segmented by component into software and services. The software segment currently holds the largest market share, primarily due to the widespread adoption of AI-powered analytics, sentiment analysis engines, and automated moderation platforms. AI software solutions are designed to process large volumes of unstructured data from multiple sources, providing businesses with real-time insights into customer feedback and sentiment. These solutions leverage advanced algorithms and machine learning models to identify patterns, detect anomalies, and generate actionable recommendations

In practice, process automation, machine learning, predictive analytics, generative AI, and AI-powered front-end tool chatbot are pioneer technologies of Artificial Intelligence (AI) used in recruitment, selection, and retention. However, concerns have been raised about its ability to support the process accurately without compromise/potential biases with manipulative datasets. This study aims to systematically review the existing empirical literature on the impact of Artificial Intelligence (AI) on recruitment, selection, and retention processes within Human Resource Management (HRM), specifically in the US banking sector. Through a systematic literature review, three research questions and hypotheses were formulated, and the study was guided by the PRISMA model, the study identified and analyzed empirical studies published between 2019 and 2024 that focused on AI-driven HR processes. The analysis revealed that AI is increasingly adopted in recruitment to automate candidate screening, improve efficiency, and reduce bias. However, the integration of AI into selection and retention processes is less advanced, with studies highlighting the need for human oversight to complement AI tools. The findings suggested that while AI enhances objectivity and efficiency in HRM, over-reliance on technology may overlook the nuanced judgments human recruiters provide. The study concludes with recommendations for further exploratory research and collaboration between banks and academic institutions to bridge existing knowledge gaps and optimize AI adoption in HR practices.

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Output Speed (Output Tokens per Second) by Model

The global journal review service market is experiencing robust growth, driven by the increasing volume of research publications and the rising demand for high-quality peer review to ensure scientific rigor. The market's expansion is fueled by several key trends, including the growing adoption of online submission and review platforms, the increasing specialization within scientific disciplines demanding expert review, and the rising pressure on researchers to publish in high-impact journals. While the precise market size in 2025 requires further specification, a reasonable estimate, considering typical growth rates in related sectors, places it at around $2 billion. Considering a conservative Compound Annual Growth Rate (CAGR) of 8% over the forecast period (2025-2033), the market is projected to reach approximately $4 billion by 2033. This growth, however, is not without its challenges. Constraints include the rising cost of peer review, concerns about bias and conflicts of interest within the review process, and the ongoing debate about the efficiency and effectiveness of the current peer-review system. The market is segmented by service type (e.g., editorial services, language editing, statistical analysis, manuscript formatting), target audience (researchers, publishers), and geographic region. Key players in the market, including Editorpages, Genex Services, and Research Square, are constantly innovating to address these challenges and improve the efficiency and transparency of the journal review process. The competitive landscape is characterized by a mix of large, established companies and smaller, specialized service providers. Larger companies leverage their established networks and diverse service offerings, while smaller players often focus on niche expertise or specific regions. Future market success will depend on the ability to offer innovative solutions that streamline the review process, enhance transparency, and address the growing concerns around fairness and efficiency. Furthermore, investment in advanced technologies, such as artificial intelligence (AI) for manuscript screening and plagiarism detection, is likely to play a significant role in shaping the future of the journal review service market. Continued growth will also hinge on addressing concerns regarding reviewer compensation and workload to maintain a sustainable and high-quality peer review ecosystem.

Peer Review System Market Outlook

The global peer review system market size was valued at USD 1.2 billion in 2023 and is expected to reach USD 2.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.1% from 2024 to 2032. The primary growth factor driving this market is the increasing emphasis on quality control and validation across various industries, particularly in education, healthcare, and research institutions.

One of the major growth factors contributing to the expansion of the peer review system market is the heightened focus on academic and research integrity. As academic and scientific communities strive for higher standards and legitimacy, the demand for robust peer review systems to validate and scrutinize research findings has surged. Additionally, funding agencies and governmental bodies are increasingly mandating the use of rigorous peer review processes to ensure the credibility of funded research, further propelling market growth.

The technological advancements in artificial intelligence and machine learning are also major catalysts in the growth of the peer review system market. Modern AI algorithms can streamline the peer review process by assisting in the identification of suitable reviewers, detecting potential plagiarism, and providing insights into the overall quality of submissions. These advancements make the review process more efficient and reliable, thereby increasing the adoption rate of peer review systems among various end-users.

Moreover, the growing importance of peer review systems in corporate settings cannot be overlooked. Companies are increasingly using these systems to assess the quality of internal reports, project proposals, and even employee performance reviews. The ability to maintain high standards and foster a culture of continuous improvement is appealing to corporate entities, further contributing to the market's robust growth. Additionally, the increasing requirement for transparency and accountability in various sectors is likely to accelerate the adoption of peer review systems.

In the realm of academic research and publication, Journal Software plays a pivotal role in streamlining the peer review process. These software solutions are designed to manage the entire workflow of manuscript submissions, from initial submission to final publication. By automating tasks such as reviewer selection, feedback collection, and revision tracking, Journal Software significantly reduces the administrative burden on editors and reviewers. This, in turn, allows them to focus more on the quality and integrity of the research being evaluated. As the demand for efficient and transparent peer review processes grows, the adoption of Journal Software is expected to rise, further supporting the expansion of the peer review system market.

Regionally, North America is expected to lead the peer review system market, followed by Europe and the Asia Pacific. The presence of numerous academic institutions, research organizations, and corporate entities in these regions, which prioritize high standards of quality and validation, fuels the market demand. The Asia Pacific region, with its expanding educational infrastructure and growing emphasis on research and development, is anticipated to exhibit the highest CAGR during the forecast period.

Component Analysis

The peer review system market is segmented by components into software and services. The software segment, encompassing platforms and applications that facilitate the peer review process, is anticipated to hold a significant share. This can be attributed to the rising demand for digital solutions that streamline and automate the review process. These software solutions often come with features like plagiarism detection, reviewer recommendation, and analytics, which enhance the efficiency and effectiveness of the review process.

The integration of artificial intelligence and machine learning into peer review software is a notable trend. These technologies help in automating several aspects of the review process, such as identifying suitable reviewers based on expertise and detecting any potential conflicts of interest or plagiarism. Such advanced functionalities not only improve the speed and accuracy of reviews but also make the software more appealing to end-users in various sectors, including education, healthcare, and corporate.

On the other hand, the services segme

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

Introduction: As interest in generative artificial intelligence (AI) has swept across the healthcare field, clinicians and researchers in otolaryngology-head and neck surgery (OHNS) have sought to explore its potential applications to the specialty. However, the quality of such inquiries has varied widely, with literature often not including crucial information about the AI models used or the methodology of prompting the model, to name just a few poorly reported components. Our objective is to collect the body of generative AI-focused literature in otolaryngology, and catalog the degree to which methodology is reported; this will inform the interpretation of such studies, since their outcomes are less valuable without this methodological information. Methods and Analysis: A search strategy has been devised with a medical librarian, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Reviews (PRISMA-ScR) guidelines. Databases searched will include PubMed, Embase, Web of Science, ISCA Archive, and IEEE Xplore; gray literature will also be searched via arXiv, medRxiv, and engRxiv. All studies using large language models within the field of otolaryngology will be included, regardless of patient population of interest. Time frame of publication will be limited to after November 2022, when GPT was first made available to the public. Non-primary literature such as reviews, commentaries, and editorials will be excluded, as will papers not in English. Abstract review, full text review, and data extraction will all be completed by two independent reviewers, with conflicts resolved via discussion; a third reviewer will be consulted if conflicts are unable to be resolved. Extracted information will be limited to the methodology described in included papers, such as specification of the model and prompts used. Ethics and Dissemination: As no human subjects will be involved in this review, ethics approval will not be necessary. Findings will be disseminated via an academic journal within otolaryngology. Key Words or Phrases: artificial intelligence, head and neck, large language model, otolaryngology

Search codes and databases for a systematic review on school scientific practices involving artificial intelligence. This summary details the databases used, the specific date range covered, the exact search codes applied, the total number of studies identified, and the final selection process for eligible studies within the review's scope. Additionally, it includes a list of the selected empirical studies and the appendices with the data coding.

2024

The market for systematic review tools is experiencing robust growth, driven by the increasing demand for evidence-based decision-making across academic, corporate, and public sectors. The rising volume of research publications and the need for efficient, reliable methods to synthesize this information are key factors fueling market expansion. Cloud-based solutions are gaining significant traction due to their accessibility, scalability, and collaborative features, surpassing on-premises deployments in market share. While North America currently holds a dominant position, driven by strong research infrastructure and funding, regions like Asia Pacific are exhibiting rapid growth potential, reflecting increasing research activity and adoption of digital tools. Competition is intense, with established players like Clarivate and Elsevier competing with specialized startups and open-source options. The market is segmented by application (academic, corporate, public sector) and type (cloud-based, on-premises), offering diverse solutions to meet specific needs. Future growth will be influenced by advancements in artificial intelligence (AI) and machine learning (ML) to automate aspects of systematic review processes, improving efficiency and reducing bias. Integration with other research management tools and enhanced collaboration features will further shape the market landscape. The market is projected to maintain a healthy CAGR (let's assume a conservative 8% based on typical software market growth), resulting in substantial market expansion over the forecast period (2025-2033). Challenges remain, including the need for user-friendly interfaces, robust data security, and cost considerations, particularly for smaller institutions and organizations. However, the ongoing emphasis on rigorous research methodologies and the increasing accessibility of digital tools suggest a positive outlook for the systematic review tools market. The continued development of advanced features, such as AI-powered tools for identifying relevant literature and automating data extraction, will further drive market expansion and solidify the role of these tools in research and evidence-based decision-making.

Melanoma, a lethal form of skin cancer, poses a significant health risk worldwide with rising incident rates. The usage of Artificial Intelligence (AI) tools in dermatology for melanoma detection can help curb the demand for accurate and efficient diagnosis of the disease. This review examines the current state of AI, Machine Learning (ML), and Deep Learning (DL) applications in the identification of melanomas through the analysis of various studies that have demonstrated the potential of these technologies that could outperform traditional methods and provide life-saving diagnoses. The primary usage of Convolutional Neural Networks (CNNs) has the potential to completely revolutionize the field of dermatological diagnosis.