28 papers · ranked by Valyu relevance
Jauhiainen, Jussi S., Toppari, Aurora
This study explores how Generative Artificial Intelligence (GenAI), Large Language Models (LLMs) and AI Agents are changing research and education. It explains, in simple terms, how these technologies have developed from early artificial intelligence (AI) to machine learning (ML), deep learning (DL), and finally to…
Sandeep Reddy
Background Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient…
Ran He, Jie Cao, Tieniu Tan
Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and…
Leda Tortora
The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing…
Petar Radanliev, Omar Santos, Uchenna Daniel Ani
Generative Artificial Intelligence marks a critical inflection point in the evolution of machine learning systems, enabling the autonomous synthesis of content across text, image, audio, and biomedical domains. While these capabilities are advancing at pace, their deployment raises profound ethical, security, and…
Martin Neil Baily, D. Byrne, Aidan Kane, Soto + 1 more
With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain with the effect of the technology on the innovation process a crucial open question. Some labor-saving innovations, such as the light…
Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar, Fiona Carroll
In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world…
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Md. Aminul Islam
'Md. Aminul Islam'] Dear Editor, Previously, we published a correspondence article in International Journal of Surgery about next-generation drug discovery and development using ChatGPT or Large Language Model (LLM)1. The article was very timely. However, we found that generative artificial intelligence (AI) is…
Barclay Burns, Bo Nemelka, Anmol Arora
Generative artificial intelligence (AI) represents a subset of AI that focuses on generating new content after being trained on existing data, in contrast to deductive AI systems, which seek to analyse data to derive conclusions or predictions. The new content produced by generative AI technologies can include text…
Jingbo Jiang, Aiqun Shao
In the field of education, generative artificial intelligence has a profound impact, mainly reflected in areas such as personalized learning, automated assignment evaluation, content generation, and open educational resources. However, some scholars have begun to discuss whether this technology may lead to new…
Jingyu Xu, Binbin Wu, Jiaxin Huang, Yulu Gong + 2 more
Systems in Medical Image Analysis Authors: ['Jingyu Xu' 'Binbin Wu' 'Jiaxin Huang' 'Yulu Gong' 'Yifan Zhang' 'Bo Liu'] The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous…
Abdenour Hadid, Tanujit Chakraborty, D. Busby
Foundations, Trends, and Future Challenges Authors: ['Abdenour Hadid' 'Tanujit Chakraborty' 'D. Busby'] Abstract—Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a…
Sultan A. Alharthi
Generative AI tools are increasingly being integrated into game design and development workflows, offering new possibilities for creativity, efficiency, and innovation. This paper explores the evolving role of these tools from the perspective of game designers and developers, focusing on the benefits and challenges…
Qi Wang, Yanghe Feng, Jincai Huang, Yiqin Lv + 2 more
Nowadays, big data, deep learning models, optimization methods, and computational power are essential in promoting the development of artificial intelligence. Recent advances are focused on generative artificial intelligence (GenAI), which paves unprecedented paths to exploring the mechanisms behind the creation of new…
Jens Knappe
Shift in Generative AI with a Focus of Text-To-Image Authors: ['Jens Knappe'] The year 2022 marks a watershed in technology, and arguably in human history, with the release of powerful generative AIs capable of convincingly performing creative tasks. With the help of these systems, anyone can create something that…
Antoine Szatkownik, Cyril Furtlehner, Guillaume Charpiat, Burak Yelmen + 1 more
Synthetic data generation via generative modeling has recently become a prominent research field in genomics, with applications ranging from functional sequence design to high-quality, privacy-preserving artificial in silico genomes. Following a body of work on Artificial Genomes (AGs) created via various generative…
Authors not listed
The vastness of chemical space presents a long-standing challenge for the exploration of new compounds with pre-determined properties. In materials science, crystal structure prediction has become a mature tool for mapping from composition to structure based on global optimisation techniques. Generative artificial…
Burak Yelmen, Aurélien Decelle, Linda Ongaro, Davide Marnetto + 5 more
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent…
Authors not listed
The value of generative artificial intelligence (AI) for teaching and learning is currently hotly debated. Concerns regarding the accuracy of information produced by generative AI as well as student over-reliance on this tool coexist with excitement about tailored opportunities that AI may provide for educational…
Youhan Lee, Jaehoon Kim
With the fact that protein functionality is tied to its structure and shape, a protein design paradigm of generating proteins tailored to specific shape contexts has been utilized for various biological applications. Recently, researchers have shown that top-down strategies are possible with the aid of deep learning…
Authors not listed
The discovery of radiation-resistant polymers is vital for aerospace, medical, and energy applications, where ionizing radiation rapidly degrades conventional materials. Inspired by the impact of Google DeepMind’s AlphaFold in structural biology, this study presents a closed-loop generative AI framework for polymer…
Hiroaki Iwata, Taichi Nakai, Takuto Koyama, Shigeyuki Mtsumoto + 2 more
Molecular generation is crucial for advancing drug discovery, material design, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques, such as molecular…
Burak Yelmen, Aurélien Decelle, Leila Lea Boulos, Antoine Szatkownik + 3 more
Applications of generative models for genomic data have gained significant momentum in the past few years, with scopes ranging from data characterization to generation of genomic segments and functional sequences. In our previous study, we demonstrated that generative adversarial networks (GANs) and restricted…
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…
Shaked Ahronoviz, Ilan Gronau
In recent years, there have been increasing attempts to develop computational methods for generating synthetic genomic data that aim to mimic real genomic datasets. Artificial genomes (AGs) generated by these methods have emerged as a promising potential solution for privacy concerns raised by public genomic datasets…
Giovanni Bolcato, Esther Heid, Jonas Boström
Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the…
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter + 1 more
There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new…