Generative AI and Interdisciplinary Applications

Topic Information

Dear Colleagues,

Generative Artificial Intelligence (GenAI) now produces coherent text, photorealistic images, atom-level molecular blueprints, and working engineering layouts. These systems already accelerate protein engineering, guide chip layout optimization, draft medical reports, compose educational material, and power social science simulations. Large language models, diffusion and flow-matching networks, and structure-based generators already help researchers form hypotheses, expand data sets, speed up simulation, and support creative thinking. At the same time, pressing questions remain regarding their robustness, generalization, evaluation protocols, bias, privacy, intellectual-property rights, and governance.

This Topic welcomes original research that advances GenAI algorithms and their application in various fields. We welcome studies that integrate GenAI into interdisciplinary applications, including research at the intersection of GenAI with natural sciences, engineering, life and health sciences, social sciences, arts, or humanities. In addition, we encourage submission regarding the responsible use of GenAI regarding safety, ethics, regulation, and responsible deployment. By gathering these contributions, we aim to provide a clear snapshot of GenAI across disciplines and to outline practical directions for future work.

Topics of interest include but are not limited to the following:

• Novel generative model architectures, algorithms, objectives, and optimization strategies
• Multimodal GenAI for heterogeneous data encoding, modeling, and generation
• Social simulation, agent-based modeling, economics, and policy analysis with GenAI
• Human-centered GenAI for education, creativity, language learning, and culture studies
• GenAI for life science, health sciences, and natural science
• GenAI for information retrieval, recommendation, and content generation
• GenAI for medical imaging, report generation, and clinical decision support
• Materials generation and inverse design for energy, catalysis, and manufacturing
• GenAI for environmental and climate modeling, remote sensing, and agriculture
• GenAI for software such as text-to-code generation and automated software engineering
• Mathematical foundations, evaluation metrics, calibration, and uncertainty estimation
• Safety, bias mitigation, privacy preservation, and legal or ethical considerations in responsible GenAI

Dr. Jisheng Dang
Prof. Dr. Wenjie Wang
Dr. Yongqi Li
Prof. Dr. Juncheng Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
AgriEngineering agriengineering	3.0	4.7	2019	20.6 Days	CHF 1600	Submit
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000	Submit
Healthcare healthcare	2.7	4.7	2013	21.5 Days	CHF 2700	Submit
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
BioMedInformatics biomedinformatics	-	3.4	2021	22.9 Days	CHF 1000	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	24.5 Days	CHF 1800	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit

* Median value for all MDPI journals in the first half of 2025.

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Published Papers (3 papers)

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All Electronics AgriEngineering AI Sensors Healthcare AI BioMedInformatics BDCC Information

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13 pages, 472 KB  

Open AccessArticle

Can a Generative Artificial Intelligence Model Be Used to Create Mass Casualty Incident Simulation Scenarios? A Feasibility Study

by Sergio M. Navarro, Angie G. Atkinson, Ege Donagay, Maxwell Jabaay, Sarah Lund, Myung S. Park, Erica A. Loomis, John M. Zietlow, T. N. Diem Vu, Mariela Rivera and Daniel Stephens

Healthcare 2025, 13(24), 3184; https://doi.org/10.3390/healthcare13243184 - 5 Dec 2025

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Abstract

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a [...] Read more.

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value. Full article

(This article belongs to the Topic Generative AI and Interdisciplinary Applications)

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28 pages, 20766 KB  

Open AccessArticle

CAFE-Dance: A Culture-Aware Generative Framework for Chinese Folk and Ethnic Dance Synthesis via Self-Supervised Cultural Learning

by Bin Niu, Rui Yang, Qiuyu Zhang, Yani Zhang and Ying Fan

Big Data Cogn. Comput. 2025, 9(12), 307; https://doi.org/10.3390/bdcc9120307 - 2 Dec 2025

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Abstract

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive [...] Read more.

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive movements of Chinese ethnic folk dance, resulting in semantic distortion and cross-modal mismatch. Building on the Chinese traditional ethnic Helou Dance, this paper proposes a culture-aware Chinese ethnic folk dance generation framework, CAFE-Dance, which dispenses with manual annotation and automatically generates dance sequences that achieve high cultural fidelity, precise music synchronization, and natural, fluent motion. To address the high cost and poor scalability of cultural annotation, we introduce a Zero-Manual-Label Cultural Data Construction Module (ZDCM) that performs self-supervised cultural learning from raw dance videos, using cross-modal semantic alignment and a knowledge-base-guided automatic annotation mechanism to construct a high-quality dataset of Chinese ethnic folk dance covering 108 classes of curated cultural attributes without any frame-level manual labels. To address the difficulty of modeling cultural semantics and the weak interpretability, we propose a Culture-Aware Attention Mechanism (CAAM) that incorporates cultural gating and co-attention to adaptively enhance culturally key movements. To address the challenge of aligning the music–motion–culture tri-modalities, we propose a Tri-Modal Alignment Network (TMA-Net) that achieves dynamic coupling and temporal synchronization of tri-modal semantics under weak supervision. Experimental results show that our framework improves Beat Alignment and Cultural Accuracy by 4.0–5.0 percentage points and over 30 percentage points, respectively, compared with the strongest baseline (Music2Dance), and it reveals an intrinsic coupling between cultural embedding density and motion stability. The code and the curated Helouwu dataset are publicly available. Full article

(This article belongs to the Topic Generative AI and Interdisciplinary Applications)

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29 pages, 9355 KB  

Open AccessArticle

AI-Delphi: Emulating Personas Toward Machine–Machine Collaboration

by Lucas Nóbrega, Luiz Felipe Martinez, Luísa Marschhausen, Yuri Lima, Marcos Antonio de Almeida, Alan Lyra, Carlos Eduardo Barbosa and Jano Moreira de Souza

AI 2025, 6(11), 294; https://doi.org/10.3390/ai6110294 - 14 Nov 2025

Viewed by 705

Abstract

Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Gemini, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine–machine collaboration models to streamline and [...] Read more.

Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Gemini, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine–machine collaboration models to streamline and accelerate Delphi execution time by leveraging the extensive knowledge of LLMs. We then applied one of these models—the Iconic Minds Delphi—to run Delphi questionnaires focused on the future of work and higher education in Brazil. Therefore, we prompted ChatGPT to assume the role of well-known public figures from various knowledge areas. To validate the effectiveness of this approach, we asked one of the emulated experts to evaluate his responses. Although this individual validation was not sufficient to generalize the approach’s effectiveness, it revealed an 85% agreement rate, suggesting a promising alignment between the emulated persona and the real expert’s opinions. Our work contributes to leveraging Artificial Intelligence (AI) in Futures Research, emphasizing LLMs’ potential as collaborators in shaping future visions while discussing their limitations. In conclusion, our research demonstrates the synergy between Delphi and LLMs, providing a glimpse into a new method for exploring central themes, such as the future of work and higher education. Full article

(This article belongs to the Topic Generative AI and Interdisciplinary Applications)

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