Results 271 to 280 of about 221,929 (334)

Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work

Journal of Organizational Behavior, EarlyView.
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster, Christopher Flathmann, Jennifer Hsu, Nathan J. McNeese, Thomas A. O'Neill, Eduardo Salas   +5 more
wiley   +1 more source

Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks. [PDF]

J Med Imaging (Bellingham)
Li D, Li K, Zhou W, Anastasio MA.
europepmc   +1 more source

Artificial Intelligence in Multimedia Content Generation: A Review of Audio and Video Synthesis Techniques

Journal of the Society for Information Display, EarlyView.
Modern AI systems can now synthesize coherent multimedia experiences, generating video and audio directly from text prompts. These unified frameworks represent a rapid shift toward controllable and synchronized content creation. From early neural architectures to transformer and diffusion paradigms, this paper contextualizes the ongoing evolution of ...
Charles Ding, Rohan Bhowmik
wiley   +1 more source

Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks. [PDF]

Sensors (Basel)
Liu Y, Guo L, Hu X, Zhou M.
europepmc   +1 more source

Is orthopaedics entering the age of generative AI?—A narrative review of current applications, challenges, and future directions

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Artificial intelligence (AI) in medicine is undergoing a pivotal transformation, evolving from discriminative models that classify data to generative AI systems capable of creating novel content. Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music,
Felix C. Oettl, James A. Pruneski, Balint Zsidai, Yinan Yu, Ting Cong, Thomas Tischer, Michael T. Hirschmann, Kristian Samuelsson   +7 more
wiley   +1 more source

Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation. [PDF]

PLoS One
Lesmes-Leon DN, Dengel A, Ahmed S.
europepmc   +1 more source

Optical Coherence Tomography for High‐Precision Industrial Inspection in Industry 4.0: Advances, Challenges, and Future Trends

Laser &Photonics Reviews, EarlyView.
This review examines how optical coherence tomography transforms industrial inspection by delivering real‐time, micrometer‐resolution, depth‐resolved imaging. It surveys applications across display manufacturing, thin films, microelectronics, laser processing, and coatings, evaluates performance against conventional techniques, and highlights emerging ...
Nipun Shantha Kahatapitiya, Sm Abu Saleah, Daewoon Seong, Naresh Kumar Ravichandran, Sangyeob Han, Ruchire Eranga Wijesinghe, Mansik Jeon, Jeehyun Kim   +7 more
wiley   +1 more source

High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks. [PDF]

Sci Rep
Religa P, Mickael ME, Kubick N, Horbańczuk JO, Floretes N, Sacharczuk M, Atanasov AG.   +6 more
europepmc   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Artificial Intelligence in Ovarian Cancer: Current Advances and Perspectives

Medicine Advances, EarlyView.
This article provides a comprehensive summary of the applications of artificial intelligence, including radiomics and deep learning, in ovarian cancer. It also discusses the current challenges and future directions, which mainly involve certain aspects of model generalizability, interpretability, and ethical and regulatory considerations.
Zijing Lin, Jinwei Qiang, Yajia Gu, Haiming Li   +3 more
wiley   +1 more source