22 papers · ranked by Valyu relevance
Derouiche, Hana, Brahmi, Zaki + 2 more
—The emergence of Large Language Models (LLMs) has ushered in a transformative paradigm in artificial intelligence, Agentic AI, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper provides a systematic review and comparative analysis of leading…
Peter Schüller, João Paulo Costeira, James L. Crowley, Jasmin Grosinger + 4 more
'Jasmin Grosinger' 'Félix Ingrand' 'Uwe Köckemann' 'Alessandro Saffiotti' 'Martin Welss'] Abstract. Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS…
Gunther Eysenbach, Stefan Nilsson, Marta Fernandes, Hyeyoung Hah + 6 more
'Fábio Gama' 'Daniel Tyskbo' 'Jens Nygren' 'James Barlow' 'Julie Reed' 'Petra Svedberg'] Background Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.…
Jay Lee, Hanqi Su, Dai-Yan Ji, Takanobu Minami
Direction Authors: ['Jay Lee' 'Hanqi Su' 'Dai-Yan Ji' 'Takanobu Minami'] Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity…
Shreyansh Agrawal, Harsh B. Anadkat, Kiran K. Athimoolam, Harsh Bhardwaj + 8 more
Recent advances in artificial intelligence (AI) have prompted claims about autonomous “AI scientists,” yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight…
Anton H van der Vegt, Ian A Scott, Krishna Dermawan, Rudolf J Schnetler + 2 more
The scoping review consisted of a comprehensive systematic search for existing AI implementation frameworks, with analysis limited to identification of themes and stages reported in the identified frameworks.44 It was reported according to the PRISMA Extension for Scoping Review (PRISMA-ScR) guidelines.45 No formal…
Mohammed O. Ahmed, Sahil A. Amale, Rhythm D. Bhavsar, Pratham Chopra + 18 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their capacity to routinely reproduce results from multiple real-life published studies remains largely untested. We evaluated five advanced AI research frameworks (Kosmos, K-Dense, ToolUniverse…
Tiffany Leung, Pearl Aovare, Maryati Yusof, Dillon Chrimes + 14 more
'Kathrin Cresswell' 'Nicolette de Keizer' 'Farah Magrabi' 'Robin Williams' 'Michael Rigby' 'Mirela Prgomet' 'Polina Kukhareva' 'Zoie Shui-Yee Wong' 'Philip Scott' 'Catherine K Craven' 'Andrew Georgiou' 'Stephanie Medlock' 'Jytte Brender McNair' 'Elske Ammenwerth'] Given the requirement to minimize the risks and…
Raghda Altamimi, Anas Abu Al-Haija’a
The rise of artificial intelligence (AI) and its rapidly expanding tools is revolutionizing various sectors, with education standing out as a profoundly affected domain, creating an urgent need to incorporate AI competencies into education. This study proposes an AI-enriched digital Educational Framework (AIEDF) based…
Demian Kong, Shaoqi Bei, Yueyue Wu, Bixia Tang + 1 more
AI-driven data search and integration represent an emerging research direction. Although several LLM-based backend frameworks and agentic frameworks have emerged, significant gap remains in developing a one-stop, configurable agent framework that supports various data sources and provides a web interface for efficient…
Hassane Alami, Renata Pozelli Sabio, Elsury Johanna Pérez, Marie-Pierre Gagnon + 8 more
Background Several artificial intelligence (AI) governance frameworks have emerged to help health systems (HS) address AI-related risks. However, most fail to capture the multidimensional and evolving nature of real-world governance. Objective This systematic review aimed to synthesize existing AI governance frameworks…
Boming Xia, Qinghua Lu, Harsha Perera, Liming Zhu + 3 more
'Yue Liu' 'Jon Whittle'] Abstract—The rapid development of artificial intelligence (AI) has led to increasing concerns about the capability of AI systems to make decisions and behave responsibly. Responsible AI (RAI) refers to the development and use of AI systems that benefit humans, society, and the environment while…
Priyanka Bhutada, Nitin Goyal, Tatsam K. Lakhankiya, Sai D. Narahari + 8 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI…
Naveen Krishnan
Artificial Intelligence (AI) has evolved dramatically over the past decade, transitioning from specialized systems designed for narrow tasks to increasingly sophisticated architectures capable of autonomous operation across diverse domains. Among these advancements, AI agents represent a particularly significant…
Ivan Kondratyev, Weinan Sun
AI coding assistants excel at software tasks but lack structured access to laboratory hardware, the physical instruments that define experimental science. We present Ataraxis, an open-source framework that provides hardware control capabilities spanning camera acquisition, microcontroller communication, precision…
Authors not listed
As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, presenting significant implications for STEM disciplines, it is bringing opportunities to enhance how chemistry and chemical engineering are taught and learned. This perspective critically explores the…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Soham Ghosh, Gaurav Mittal
Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate…
Authors not listed
Artificial intelligence (AI) is poised to transform heterogeneous catalysis, ushering in a new paradigm for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental, and chemical…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan Jones + 5 more
Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research…