Applied Artificial Intelligence

Multidisciplinary Frontiers of Generative AI: State of the Art and Open Challenges

Generative AI has emerged as a transformative technology capable of producing synthetic data that closely resembles original content across various modalities, including text, audio, images, and video. Unlike traditional AI systems that focus on prediction or classification, generative models learn complex patterns from data and generate novel content. Generative AI is not only revolutionizing creative industries but is also reshaping problem-solving methodologies in domains such as healthcare, law, engineering, business, marketing, agriculture. The integration of generative capabilities with task-specific applications has opened new avenues for research and innovation. One particularly impactful application is the generation of synthetic data, which allows researchers and developers to augment limited datasets, improve model generalization, and enable experimentation in domains where real data is scarce, sensitive, or expensive to obtain. As this technology evolves and integrates into real-world systems, there is a growing need to examine its impact, effectiveness, limitations, and ethical dimensions across disciplines.

The importance of Generative AI lies in its versatility and potential to drive innovation across various domains, including but not limited to business analytics, healthcare, creative industries, education, law, engineering, and environmental science. Understanding the multidisciplinary applications of generative AI is critical for guiding its responsible adoption and fostering cross-domain collaboration. Moreover, each domain brings unique challenges, such as data privacy in healthcare or explainable in legal contexts. Furthermore, the excessive use and adoption of generative tools raise questions about misinformation, intellectual property, and regulatory boundaries. As research and development in this field continue to accelerate, there is a growing need to explore how generative AI can be effectively applied in multidisciplinary contexts and to understand the implications it holds for various industries and scientific domains. By highlighting diverse implementations and lessons learned, this collection will contribute to the growing body of knowledge on how generative AI is shaping the future of applied artificial intelligence.

This Article Collection invites submissions that explore the development, deployment, and impact of generative AI across multiple disciplines, emphasizing cross-disciplinary innovations and challenges. The goal is to provide a platform that connects research and applications in diverse fields, including but not limited to:

• Healthcare: AI-generated medical imaging, diagnostics, and personalized treatment simulations.
• Finance: Synthetic data for financial forecasting, risk modeling, and fraud detection.
• Education: Automated content creation, personalized learning resources, and intelligent tutoring systems.
• Art, Design, and Media: Creative applications including visual arts, music, and cross-modal storytelling.
• Law and Policy: AI-assisted document drafting, contract analysis, and regulatory compliance tools.
• Engineering and Manufacturing: Generative design, optimization, and simulation of complex systems.

Submissions may address:

• Cross-disciplinary applications that combine methods or insights from multiple domains (e.g., healthcare and robotics, finance and natural language processing).
• Human-AI collaboration and user interaction in domain-specific contexts.
• Technical innovations such as novel architectures, evaluation frameworks, stability, controllability, and interpretability of generative models.
• Ethical, societal, and regulatory considerations of deploying generative AI in specialized domains.
• Open challenges and future directions for multidisciplinary integration of generative AI technologies.

This list of topics is not exhaustive. Contributions from additional application domains are strongly encouraged. We welcome original research, review articles, and case studies that demonstrate the impact of generative AI in diverse disciplines, aiming to provide actionable insights for both researchers and practitioners.

Keywords: Generative Artificial Intelligence, Multimodal Generative Models, Synthetic Data Generation, Conversational AI, Responsible Generative AI

Manuscript Submissions:

Manuscript submission is open until 31st July 2026.

All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Manuscripts which do not fall within the scope of the journal will be rejected.

To submit your papers to this Article Collection, please:

1. Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the relevant Article Collection from the drop-down menu under the question, "Multidisciplinary Frontiers of Generative AI: State of the Art and Open Challenges"

We are able to offer a 10% Discount to all authors, and have a limited number of 20% Discount codes only available for early submissions. It should be noted that discount codes must be entered in at the point of submission as they cannot be applied retroactively, nor can these be combined as only the higher valued discount would be applicable.

Please contact Christopher Montgomery, Commissioning Editor regarding details on obtaining your discount codes, and with any other queries for this Article Collection.

Article Collection Guest Advisors

Prof. Muhammad Bilal is currently working as an Assistant Professor in the Department of Software Engineering at FAST National University of Computer and Emerging Sciences (NUCES), Islamabad Campus, Pakistan. He has postdoctoral experience from the University of Florida, USA and with prior academic roles in Malaysia and Pakistan. He is a Senior Member of IEEE and his research interest includes natural language processing, data mining, social computing, machine learning and generative AI.