World and Human Action Models towards gameplay ideation

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student’s specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI’s knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Generative artificial intelligence (AI) models will increasingly replace humans in producing output for a variety of important tasks. While much prior work has mostly focused on the improvement in the average performance of generative AI models relative to humans’ performance, much less attention has been paid to the significant reduction of variance in output produced by generative AI models. In this article, we demonstrate that generative AI models are inherently prone to the phenomenon of “regression toward the mean”, whereby variance in output tends to shrink relative to that in real-world distributions. We discuss potential social implications of this phenomenon across three levels—societal, group, and individual—and two dimensions—material and non-material. Finally, we discuss interventions to mitigate negative effects, considering the roles of both service providers and users. Overall, this article aims to raise awareness of the importance of output variance in generative AI and to foster collaborative efforts to meet the challenges posed by the reduction of variance in output generated by AI models.

This review aims to offer a foundational overview of Generative Artificial Intelligence (AI) for healthcare professionals without an engineering background. It seeks to aid their understanding of Generative AI's current capabilities, applications, and limitations within the medical field. Generative AI models, distinct from discriminative models, are designed to create novel synthetic data. Key model families discussed include diffusion models for generating images and videos, Large Language Models (LLMs) for text, and Large Multimodal Models (LMMs) capable of processing multiple data types. Recent applications in healthcare are diverse, encompassing general uses like generating synthetic medical images, automating clinical documentation, and creating synthetic audio/video for training. More specialized applications include leveraging Generative AI models as backbones for diagnostic aids, enhancing information retrieval through Retrieval-Augmented Generation (RAG) pipelines, and coordinating multiple AI agents in complex workflows. Generative AI holds significant transformative potential in medicine, enhancing capabilities across imaging, documentation, education, and decision support. However, its integration faces substantial challenges, including models' knowledge limitations, the risk of generating incorrect or uncertain "hallucinated" outputs, inherent biases from training data, difficulty in interpreting model reasoning ("black box" nature), and navigating complex regulatory and ethical issues. This review offers a balanced perspective, acknowledging both the promise and the hurdles. While Generative AI is unlikely to fully replace physicians, understanding and leveraging these technologies will be crucial for medical professionals navigating the evolving healthcare landscape.

BackgroundGenerative artificial intelligence (AI) models have shown potential for addressing text-based dental enquiries and answering exam questions. However, their role in diagnosis and treatment planning has not been thoroughly investigated. This study aimed to investigate the reliability of different generative AI models in classifying the severity of dental crowding.MethodsTwo experienced orthodontists categorized the severity of dental crowding in 120 intraoral occlusal images as mild, moderate, or severe (40 images per category). These images were then uploaded to three generative AI models (ChatGPT-4o mini, Microsoft Copilot, and Claude 3.5 Sonnet) and prompted to identify the dental arch and to assess the severity of dental crowding. Response times were recorded, and outputs were compared to orthodontists’ assessments. A random image subset was re-analyzed after one week to evaluate model consistency.ResultsClaude 3.5 Sonnet successfully classified the severity of dental crowding in 50% of the images, followed by ChatGPT-4o mini (44%), and Copilot (34%). Visual recognition of the dental arches was higher with Claude and ChatGPT-4o mini (99%) compared to Copilot (72%). Response generation was significantly longer for ChatGPT-4o mini than for Claude and Copilot (p < .0001). However, the response times were comparable for both Claude and Copilot (p = .98). Repeated analyses showed improvement in image classification for both ChatGPT-4o mini and Copilot, while Claude 3.5 Sonnet misclassified a significant portion of the images.ConclusionsThe performance of ChatGPT-4o mini-, Microsoft Copilot, and Claude 3.5 Sonnet in analyzing the severity of dental crowding often did not match the evaluations made by orthodontists. Further developments in image processing algorithms of commercially available generative AI models are required prior to reliable use for dental crowding classification.

The rapid evolution of generative and diffusion-based method in artificial intelligence (AI) has transformed de novo peptide and protein design, enabling the creation of sequence–structure–function relationships beyond conventional heuristic exploration. In this work, we developed an AI-guided molecular design pipeline that integrates diffusion-based generative modeling, inverse folding algorithms, and atomistic simulation to design and evaluate novel peptide inhibitors of the LasR quorum-sensing receptor of Pseudomonas aeruginosa. Peptides were generated using multiple complementary AI frameworks, including DiffPepBuilder (a diffusion-based denoising generator), ProteinMPNN (an inverse folding network), RFdiffusion2, Evobind2, and BindCraft (a hybrid sequence–structure generative model). These peptides were then subjected to physicochemical profiling and molecular docking to estimate binding affinities (Kd) and interface complementarity. The top candidates were subjected to 400 ns all-atom molecular dynamics (MD) simulations, performed in triplicate, to assess stability, flexibility, and interaction persistence, and were compared against the experimentally validated peptides benchmark. Post-simulation metrics, including RMSD, RMSF, and radius of gyration (Rg), revealed reproducible but architecture-specific dynamic behaviors, with BindCraft 4 maintaining the most compact and stable configuration, exhibiting minimal fluctuations and consistent binding-site retention. MMGBSA, performed at different time intervals, revealed strong van der Waals and electrostatic stabilization, correlating with sub-micromolar dissociation constants predicted computationally. In contrast, DiffPepBuilder 1–2 exhibited balanced flexibility and moderate stabilization, while ProteinMPNN 2 and the control (Aqs1C) displayed comparatively weaker energetic convergence. Principal Component Analysis (PCA) and Free Energy Landscape (FEL) projections confirmed that BindCraft 4 sampled a single deep energy basin, signifying a thermodynamically optimized conformational state. Collectively, this integrative AI-simulation hybrid computational framework demonstrates the capability of generative diffusion and inverse folding models to design peptides with superior structural stability and binding thermodynamics, offering a rational framework for AI-driven peptide therapeutics targeting Pseudomonas aeruginosa communication networks.

&lt;p&gt;&lt;strong&gt;Because of its wide range of applications, generative artificial intelligence (Generative AI) has received a lot of attention in academia and industry. Despite significant advances in computer vision and natural language processing, the use of Generative AI in tabular data is still underexplored. This gap is especially significant given the prevalence of tabular data as the primary data modality. This thesis seeks to close this gap by focusing on the efficient generation, evaluation, refinement, and application of tabular data, while addressing the challenges inherent in its heterogeneous nature.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The primary goal of this thesis is to develop efficient algorithms for tabular data synthesis, advancing the field of Generative AI in the tabular domain. To achieve this goal, four specific research objectives have been outlined.&lt;/p&gt;&lt;p&gt;First, this thesis addresses gaps in evaluation metrics by unifying a framework for the comprehensive and consistent assessment of synthetic data. The proposed framework is designed to meet diverse business requirements across various downstream tasks by incorporating a wide range of advanced metrics. These metrics cover various types of evaluations, including univariate, bivariate, multivariate, cluster, and record-level evaluations. Additionally, standardized visualizations are provided to facilitate qualitative assessments. The results indicate that the proposed evaluation framework not only enables consistent comparisons, rankings, and the selection of different data synthesis approaches but also acts as a valuable tool to promptly assess the reliability of results obtained from synthetic data.&lt;/p&gt;&lt;p&gt;Second, this thesis presents innovative algorithms for the generation of tabular data, aiming to significantly enhance the quality of data synthesis. One of the contributions is the development of a reversible feature engineering pipeline designed to automatically represent tabular data in an efficient format while also ensuring that the transformed data can be easily converted back to its original format. Additionally, the thesis proposes novel deep learning-based tabular data generation models that are capable of learning the joint distribution of multivariate datasets without relying on predefined distribution assumptions. Experimental results highlight the effectiveness of the proposed algorithms in handling heterogeneous data, demonstrating superior performance in most scenarios within the same training duration when compared to similar alternatives.&lt;/p&gt;&lt;p&gt;Third, this thesis introduces innovative synthetic data prototype selection algorithms with the goal of refining the generated samples. This approach aims to leverage the advantages of Deep Generative Models, which, once trained, can generate unlimited and diverse synthetic data. By carefully identifying and selecting high-quality samples or removing unrealistic ones, the quality of the synthetic data can be enhanced from a post-processing perspective. Building on this hypothesis, we recognize the iterative nature inherent in the data synthesis procedure, where the processes of data generation, evaluation, and refinement should operate repeatedly in a cyclical flow. During the data generation phase, the evaluation framework assists in identifying potential issues and risks based on use cases, while the refinement (post-processing) step iteratively improves synthetic data in alignment with the evaluation outcomes.&lt;/p&gt;&lt;p&gt;Last, this thesis applies and validates the proposed models across various domains, including business, healthcare, and government, each requiring distinct downstream tasks. Specifically, we assess our algorithms from the standpoint of data balancing using churn data, evaluate our models focusing on data augmentation with health data, and test our algorithms from the perspective of data representation considering data privacy concerns within sensitive citizen data. These applications serve as practical illustrations, demonstrating the effectiveness and utility of the proposed Generative AI models in real-world scenarios.&lt;/p&gt;&lt;p&gt;In summary, this thesis makes a substantial contribution to the advancement of Generative AI within the domain of tabular data. It not only presents innovative algorithms and evaluation methods but also introduces practical frameworks applicable to real-world scenarios. The findings demonstrate the considerable capacity of Generative AI to fundamentally transform tabular data in various fields, ultimately leading to improved data availability, quality, and quantity.&lt;/p&gt;

Contemporary creativity support tools (CSTs) primarily support divergent thinking, while often neglecting convergent thinking. Currently, research is limited concerning how these complementary aspects of creative thought could be integrated into a unified approach. This article theoretically conceptualizes the integration of artificial intelligence (AI) as a facilitator within CSTs to enhance design students' divergent and convergent thinking during ideation processes in higher education. The research employed the concept-driven design research methodology and conducted qualitative interviews with ten design students to evaluate and refine a theoretically underpinned design concept. The study identified four key themes that informed the crafting of a revised design concept: clarity and comprehension requirements, visualization of user journeys, the role of AI, and the balance between predictable and unpredictable interactions. Through iterative refinement, the research established a theoretically and empirically underpinned design concept that demonstrates how AI facilitation can support both divergent and convergent thinking while preserving student agency in creative processes. This research contributes to the theoretical grounding of AI-enhanced creativity support and provides practical insights for integrating AI facilitation in CTSs for design education.

As generative artificial intelligence (AI) reshapes the landscape of media and communication, its integration with social media opens new possibilities for human-centered content creation. In the field of fashion journalism, which relies heavily on style, nuance, and visual culture, we present FITMag, a framework that combines multimodal large language models (LLMs) with real-time social media influence and graph data to generate fashion articles approaching professional quality.FITMag builds on the FITNet and FITViz datasets, which identify fashion influencer subgraphs on Twitter. It uses multimodal inputs including influencer metadata, retweets, mentions, hashtag trends (such as #NYFW and #sustainability), and image content to create structured prompts for both text and image generation. Leading LLMs such as ChatGPT, Claude, DeepSeek, and LLaMA are paired with Stable Diffusion to generate content in three primary formats: event-driven articles, niche community pieces, and trend-based narratives. Graph Retrieval-Augmented Generation (Graph RAG) is used to enhance contextual alignment by connecting influencer activity with fashion discourse.To assess FITMag’s effectiveness, we conducted a human-centered study with 15 fashion professionals including editors, stylists, bloggers, and researchers. Participants evaluated 52 fashion articles using 5-point Likert scales across three dimensions: authenticity, coherence, and style. They also completed a blind identification task to determine whether each article was human-written or generated by AI. Quantitative results show that GPT-4o with FITNet data achieved the highest overall performance among AI models, closely matching human-written content in stylistic quality. Participants frequently misclassified AI-generated text as human-written, especially when produced by GPT-4o and Claude, suggesting strong perceived realism. However, vision and language alignment remained a limitation. Participants observed that AI-generated images sometimes lacked contextual relevance or omitted recognizable influencers due to licensing restrictions.These findings highlight both the capabilities and current limitations of multimodal systems. While AI-generated articles can reach professional-level quality in text, challenges in image and text coherence persist. FITMag contributes to ongoing conversations about AI-assisted journalism by integrating social influence data, generative models, and user-centered evaluation. The research provides insight into how AI can support rather than replace human creativity in fashion media.Ultimately, FITMag serves as a testbed for studying AI and human collaboration in social media contexts. It offers practical tools and theoretical foundations for designing future systems that balance generative power, editorial integrity, and cultural sensitivity across digital platforms.

An increasingly popular subcategory of Artificial Intelligence (AI) is Generative AI (GAI), which encompasses technologies capable of creating new content, such as images, text, and music, often resembling outputs made by humans. The potential impact by GAI on sustainability is multifaceted. On the positive side, generative AI can aid in optimizing processes, developing innovative solutions, and identifying patterns in large datasets related to sustainability. This can lead to more efficient resource management, reduced energy consumption, and the creation of more sustainable products. However, there are also potential negative impacts, such as increased energy consumption associated with training and running generative AI models, as well as the potential for unintended consequences or biases in the generated content. Additionally, overreliance on generative AI may lead to reduced human oversight, which could undermine holistic, interdisciplinary, and collaborative approaches to sustainability. The aim of this paper is to explore the potential impacts on sustainability by generative artificial intelligence through a review of prior research on the topic. The study was conducted with a scoping literature review approach to identify potential impacts by generative AI on sustainability. Data were collected through a search in the database Scopus during the spring semester of 2024. Keywords, relevant for the study, were combined with Boolean operators. Papers identified through the search underwent a manual screening process by the authors, in which papers were selected for inclusion or exclusion in the study based on a set of criteria. Included paper were then analyzed with thematic analysis, according to the guidelines by Braun and Clarke. A categorization matrix, based in prior research on sustainability, supported the analysis and deductive coding of collected data. Results of the study highlight generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability. These different aspects of sustainability impact make this research an important contribution for deepening the understanding of generative AI and its potential consequences for society. Findings of the study provide theoretical contribution, implications for practice, and recommendations for future research on generative AI and sustainability.

- Marathi is one of the popular oldest languages of India and possesses the greater syntactic complexity. Nouns, verbs and compound words of this language have very clear and simple rules that make the learning of the language an easy task for anybody. However, a more effective tool is required to consolidate the particularity of the higher-level grammar, especially the regional dialects. In the last few years there has been a lot of work done in the era of Artificial Intelligence (AI) and Natural Language Processing (NLP) where various languages related complicated tasks have been made easy. Pre-trained generative AI models like BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pre-trained Transformer), T5 (Text-to-Text Transfer Transformer) and many others hold much promise in different language uses like text generation, translation, and grammar checks. These models can formulate, interpreting and modifying any linguistic behaviour that might be useful in handling other challenges of Marathi language such as noun declensions and verb conjugations. AI model built from the transformer architecture can be adapted to handle several of linguistic problems in Marathi language that involves syntax analysis and error detection. This paper presents a generative AI model for learning the Marathi grammar which is further categorized into two parts. The first part of the study is devoted to the elementary grammar training, and the second part is dedicated to the intermediate and advanced levels. Using generative AI, this current model gives higher accuracy than rule-based system and presents effective ideas towards modern grammar. Also, this model became improving tools in computational linguistics for regional languages and for promoting language education. Keywords: Marathi language, generative AI, BERT, GPT-3, T5, computational linguistics

Artificial Intelligence (AI) is emerging as a transformative force in both drug development and orthodontics. This paper provides an overview of key AI techniques employed in these fields. In drug development, AI is accelerating discovery, development, and delivery through generative models, predictive modeling, image analysis, and natural language processing. Orthodontics benefits from AI through image analysis, treatment planning, and patient experience enhancement. Common AI techniques in both domains include machine learning, deep learning, natural language processing, and computer vision. By leveraging these technologies, researchers and clinicians can achieve significant advancements in drug discovery, treatment efficacy, and patient care.

A Taxonomy of Generative Artificial Intelligence in Health Economics and Outcomes Research: An ISPOR Working Group Report.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G’) and loss (G”) moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G’ and G”. We employed a multilayer perceptron (MLP) and successfully predicted G’ and G” from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student’s t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input–output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.