Reimagining Fashion: A Critical Review of AI and 3D Modeling for Sustainable Development and Human Health
Reimagining Fashion: A Critical Review of AI and 3D Modeling for Sustainable Development and Human Health
I. Executive Summary
Revisiting my old article about “A New Era in Fashion: Leveraging AI and 3D Modeling for Sustainable Development and Human Health,” this report provides an updated assessment of the fashion industry’s ongoing challenges and advancements. The fashion industry, a significant global economic force, continues to grapple with profound environmental and health challenges, including extensive material waste, high storage costs, and substantial environmental emissions. A particularly alarming concern, as highlighted in the original article, is the pervasive release of microplastics from synthetic textiles, which not only contaminates ecosystems but also poses direct threats to human health, with recent discoveries affirming their presence in human brain tissue and links to various systemic diseases. Furthermore, the impact of synthetic fibers on the delicate balance of the skin’s microbiome remains a critical area of concern.
The vision of leveraging Artificial Intelligence (AI) and 3D modeling emerged as a transformative pathway towards a more sustainable and cost-effective production paradigm. This report provides an updated assessment, integrating developments from 2023-2025, revealing significant strides in AI-driven design, hyper-personalization, virtual try-on technologies, and on-demand manufacturing. These advancements demonstrate a clear potential to drastically reduce waste, optimize resource allocation, and enhance consumer experience.
However, the journey from theoretical promise to widespread practical implementation is marked by substantial gaps. Key challenges include the high financial investment required for technological integration, complexities in data management, and a notable deficit in specialized AI and fashion expertise. Ethical considerations, such as potential job displacement, algorithmic bias, and critical data privacy concerns associated with biometric data, necessitate careful navigation. Moreover, the environmental footprint of AI itself, coupled with the persistent performance advantages of synthetic fibers, presents paradoxes that must be addressed for truly holistic sustainability.
The viability of this transformative approach hinges on a concerted, collaborative effort across the industry, focusing on accessible technology solutions, robust ethical governance, and continuous innovation in material science. While the “new era” in fashion promises a more digital, personalized, and sustainable future, its ultimate success will be determined by the industry’s capacity to realistically confront and bridge these divides, moving beyond awareness to decisive, responsible action.
II. Introduction: The Fashion Industrys Evolving Trajectory
The fashion industry stands as one of the world’s largest and most polluting sectors, characterized by deeply entrenched traditional manufacturing methods that lead to immense material waste, exorbitant storage costs, and significant environmental emissions. A central tenet of the original article, and a concern that has only grown in urgency, is the widespread release of microplastics from synthetic garments, such as polyester and nylon. These microscopic particles infiltrate aquatic ecosystems and, more alarmingly, have been found to permeate human biology, including brain tissue, raising serious questions about their long-term health implications. Additionally, the interaction of synthetic fibers with the skin’s microbiome has been identified as a factor potentially compromising skin health. The assertion that “It is time to change this trajectory” by harnessing the power of AI and 3D modeling for a production model that is both sustainable and economically viable remains profoundly pertinent.
The proposed technological intervention, centered on Artificial Intelligence and 3D modeling, envisions a radical overhaul of the fashion value chain. This includes the development of customized clothing patterns derived from precise body scans, the optimization of fabric utilization through Computer-Aided Manufacturing (CAM) systems, and the adoption of Just-In-Time (JIT) production methodologies. These integrations are projected to yield multiple benefits: a substantial reduction in material waste, a significant improvement in garment fit leading to decreased product returns, and a strategic pivot away from environmentally detrimental synthetic fibers. The overarching aim is to foster sustainable development within the industry while simultaneously promoting healthier outcomes for consumers.
This report undertakes a critical re-evaluation of the ambitious vision put forth in the original article. It incorporates the most recent research and industry advancements spanning 2023-2025, providing an updated perspective on the capabilities and applications of AI and 3D modeling within the fashion sector. A core objective is to rigorously examine the persistent discrepancies between the theoretical potential of these technologies and the practical realities of their implementation. Furthermore, the report will delve into the multifaceted factors that could either propel this innovative approach to success or present formidable challenges, thereby offering a balanced and informed perspective for all industry stakeholders, including innovators and investors.
III. Advancements in AI and 3D Modeling for Sustainable Fashion
Between 2023 and 2025, the fashion industry has seen a rapid acceleration in the integration of Artificial Intelligence (AI) and 3D modeling. These technologies have moved beyond theoretical concepts to tangible applications that enhance sustainability and personalization.
A. AI-Driven Design, Personalization, and Production
Artificial Intelligence has become an indispensable tool in the fashion industry, reshaping design, personalization, and production processes. AI algorithms now analyze extensive customer data, including style preferences, size specifications, and geographical location, to generate highly personalized outfit recommendations. This capability has significantly increased repurchase rates, demonstrating AI’s direct impact on customer engagement and loyalty (SGAnalytics, 2024).
Generative AI (Gen-AI) has emerged as a powerful catalyst for creativity and innovation. Designers are increasingly adopting Gen-AI as a collaborative partner, leveraging its capacity to create intricate styles, unique prints, and novel fabric patterns (Plug and Play Tech Center, n.d.; SGAnalytics, 2024). This technology dramatically accelerates the design and prototyping phases, enabling the creation of dozens of design iterations within minutes, thereby fostering a more experimental and agile approach to fashion creation (NewArc.ai, n.d.). For instance, leading brands like Collina Strada have publicly acknowledged their use of tools such as Midjourney for ideation processes in their Spring/Summer 2024 collections, highlighting the practical adoption of these advanced AI capabilities (Plug and Play Tech Center, n.d.).
AI can transform rudimentary sketches into photorealistic visualizations, streamlining internal approval processes and facilitating real-time collaboration among design teams, which significantly reduces the time from concept to visualization (NewArc.ai, n.d.). Crucially, AI’s ability to analyze individual customer preferences and precise body measurements allows for the creation of unique, tailored designs. This not only elevates personalization to an unprecedented level but also directly contributes to a reduction in product return rates, as garments are designed to fit perfectly (NewArc.ai, n.d.; SGAnalytics, 2024; A3Logics, n.d.).
The impact of AI on waste reduction through optimized design and accurate demand prediction is substantial. AI contributes to sustainability by optimizing garment patterns to minimize fabric waste during cutting and by recommending more environmentally sound materials (SGAnalytics, 2024; NewArc.ai, n.d.; Refabric, n.d.; Source Fashion, n.d.). This capability directly addresses the issue of material waste inherent in traditional production. Moreover, AI-powered inventory management and advanced demand forecasting systems can accurately predict consumer behavior and emerging trends. This predictive power prevents overproduction and the accumulation of unsold inventory, a major source of waste in the industry (NewArc.ai, n.d.; SGAnalytics, 2024; A3Logics, n.d.; Refabric, n.d.; Sustainability Directory, n.d.; DigitalDefynd, 2025).
A broader implication of these advancements is the shift from AI as merely an efficiency tool to an innovation catalyst. While initial discussions focused on AI optimizing existing processes, the current landscape demonstrates AI’s proactive role in generating new designs and accelerating material science. This suggests that AI is not just a reactive problem-solver but a fundamental driver for creative and material innovation, redefining what clothes are made of and how they look (SGAnalytics, 2024; Plug and Play Tech Center, n.d.; NewArc.ai, n.d.; Source Fashion, n.d.).
Furthermore, AI is playing a pivotal role in advancing circular economy principles within fashion, particularly in upcycling and material innovation. AI systems can analyze discarded fabrics and garments to determine the most effective methods for repurposing them, transforming waste into new, high-quality fashion pieces (Refabric, n.d.; Source Fashion, n.d.). This includes leveraging AI algorithms to categorize textiles based on composition and wearability, making upcycling processes more efficient and cost-effective (Refabric, n.d.). In material science, AI is accelerating the discovery and simulation of viable options for new sustainable materials. Examples such as Materiom AI and TNO’s polyScout demonstrate AI’s ability to speed up the development of biobased materials and biodegradable polymers by identifying optimal chemical structures and properties (Source Fashion, n.d.). This interconnectedness of AI applications, from design and demand forecasting to material innovation and recycling, points towards a holistic, AI-powered ecosystem for circular fashion. The capacity of AI to track garments throughout their entire lifecycle—from inception through production, use, and ultimate disposal—and to identify opportunities for repair, resale, or recycling, signifies a systemic shift towards a truly circular economy (SGAnalytics, 2024; Refabric, n.d.). This suggests that the long-term success and scalability of AI in sustainable fashion are contingent upon its ability to seamlessly connect and manage these disparate stages of the product lifecycle, necessitating comprehensive “digital twins” for products and enhanced data sharing across the supply chain.
B. Evolution of 3D Body Scanning and Virtual Try-On
The global market for Virtual Try-On (VTO) is experiencing exponential growth, reflecting a significant shift in consumer behavior and technological capabilities. Valued at $12.5 billion in 2024, the market is projected to reach $48.8 billion by 2030, driven largely by the increasing preference for contactless shopping solutions, a trend accelerated by the recent global pandemic (ResearchAndMarkets.com, 2025). VTO effectively addresses common pain points in online shopping, such as uncertainty regarding size, fit, and compatibility, thereby enhancing the overall customer experience (ResearchAndMarkets.com, 2025).
The effectiveness of VTO solutions is deeply rooted in their sophisticated integration of cutting-edge innovations, including Augmented Reality (AR), Artificial Intelligence, and high-resolution imaging (ResearchAndMarkets.com, 2025). AI, in particular, refines the VTO experience by analyzing user preferences and precise body measurements to recommend products that are optimally suited to individual needs (ResearchAndMarkets.com, 2025).
The emergence and refinement of 3D body scanning technologies are further expanding the applications of VTO. Companies like Size Stream are at the forefront of this innovation, capable of capturing over 200 body measurements in mere seconds with what is described as “incredible accuracy” (Size Stream, n.d.; Good Design Awards, 2023). This precision is achieved through proprietary deep learning algorithms, rigorously trained on gold-standard DXA scans, which are typically used in clinical body composition assessment (Size Stream, n.d.; Good Design Awards, 2023). Such meticulous detail is critical, as it profoundly impacts the fit of garments by accounting for the minute contours and unique shapes of an individual’s body (Mirrorsize, n.d.).
A significant development in accessibility has been the advent of smartphone-based solutions, such as Formcut, which are democratizing 3D body scanning. These systems leverage computer vision and AI to transform simple scans performed with a smart device into actionable data for custom clothing production (Size Stream, n.d.). This innovation effectively lowers the barrier to entry for personalized fashion for businesses, facilitating seamless body scanning either in-store or at home, enabling real-time garment previews, and supporting on-demand manufacturing tailored to an individual’s exact body shape (Size Stream, n.d.).
However, a deeper examination reveals a nuanced trade-off between accuracy and accessibility in 3D body scanning. While mobile applications are undeniably expanding the reach of this technology (ResearchAndMarkets.com, 2025; Size Stream, n.d.), specific numerical accuracy details for smartphone-based scanning, particularly concerning apparel fit, are not consistently detailed across all available information (ResearchAndMarkets.com, 2025; Size Stream, n.d.). Some earlier assessments even suggested that smartphone scans might offer only a “fair guide” and might not be “entirely accurate” if the user is fully dressed during the scan (Engadget, 2018). In stark contrast, dedicated 3D scanners consistently demonstrate high reliability and reproducibility when compared to manual measurements, achieving point accuracy of less than 1mm (PMC, n.d.). This implies that while smartphones make scanning broadly accessible, achieving the precision required for a truly perfect, bespoke garment fit, especially for complex tailoring, might still necessitate more advanced, less universally accessible hardware or highly controlled scanning environments. This accuracy-accessibility dynamic directly influences consumer trust and the widespread adoption of these technologies. If smartphone scans do not consistently deliver on the promise of a “perfect fit,” it could lead to continued product returns, thereby undermining the very sustainability benefits (such as reduced waste and returns) that the technology aims to achieve. Bridging this gap, either through significant advancements in smartphone scanning algorithms or clearer communication of limitations, is crucial for the long-term success of this approach and consumer acceptance.
The role of VTO in reducing product returns and enhancing the overall customer experience is a significant driver for its adoption. Virtual try-on actively reduces the need for physical samples and minimizes product returns, directly contributing to a lower environmental footprint for fashion brands (Refabric, n.d.; ResearchAndMarkets.com, 2025). AI-driven virtual assistants are currently undergoing experimentation to further refine fit accuracy, with the explicit goal of reducing return rates (Reynolds Center for Business Journalism, 2025). Personalized shopping experiences are substantially enhanced by tailored recommendations derived from individual body data, fostering deeper customer connections and cultivating brand loyalty (A3Logics, n.d.; Mirrorsize, n.d.; SGAnalytics, 2024). Online retailers are now able to integrate body scanning technology directly into their platforms, empowering customers to receive accurate size recommendations without the traditional necessity of physical try-ons (Mirrorsize, n.d.). A notable recent development in this space is Google’s Doppl app, launched in June 2025. This application allows users to visualize outfits on their own bodies by uploading photos, converting static images into dynamic, AI-generated videos for a highly personalized VTO experience, with the clear potential to reduce dissatisfaction-driven returns (OpenTools.ai, 2025).
Beyond these immediate benefits, VTO and 3D scanning generate immense quantities of precise body data. This aggregate data can then be leveraged by AI to inform future collections, allowing brands to anticipate broader trends in body shapes and sizes across their customer base (Mirrorsize, n.d.). Furthermore, AI can utilize this collective body data to optimize sizing runs and production planning at a macro level (Size Stream, n.d.). This establishes a powerful, continuous feedback loop where real-world consumer body data directly influences and refines design and production strategies, moving beyond mere individual customization to systemic improvements in fit and sizing across entire product lines. This transforms VTO from a simple consumer-facing sales tool into a critical data intelligence component that informs the entire product development and supply chain optimization process, positioning it as central to a truly data-driven fashion industry.
C. On-Demand Manufacturing and Supply Chain Optimization
On-demand manufacturing has emerged as a pivotal solution to address the systemic challenges of overproduction, excessive waste, and financial strain that have long plagued both nascent and established brands within the fashion industry (Fashinnovation, n.d.). In contrast to traditional bulk production models, this approach facilitates the creation of products only when they are explicitly needed—typically after a confirmed sale. This methodology inherently minimizes waste, substantially reduces storage costs, and significantly improves cash flow by preventing capital from being tied up in unsold inventory (Fashinnovation, n.d.).
The technological underpinnings of this transformative shift include advanced software platforms and innovative manufacturing processes, notably 3D knitting. Companies such as Tailored Industry, for example, leverage sophisticated Shima Seiki knitting machines to produce seamless garments in a single piece. This process virtually eliminates excess fabric waste, a common byproduct of traditional cut-and-sew methods, while simultaneously enhancing both garment durability and comfort (Fashinnovation, n.d.). Similarly, Unspun is at the forefront of pioneering 3D weaving technologies, enabling fast, automated, and low-waste garment manufacturing that inherently supports on-demand and even localized production models (Unspun, n.d.). The integration of AI further optimizes this production paradigm by refining garment patterns to minimize fabric waste and by enabling highly precise demand forecasting. This allows brands to produce volumes that align closely with actual consumer demand, thereby preventing the costly and environmentally detrimental practice of overproduction (NewArc.ai, n.d.; SGAnalytics, 2024; A3Logics, n.d.; Refabric, n.d.).
The benefits of on-demand production, particularly concerning waste reduction, inventory management, and market responsiveness, are profound. This model directly eradicates the problem of overproduction, ensuring that every garment manufactured has a confirmed buyer. This is a critical intervention, especially when considering that the fashion industry is estimated to produce over 92 million tons of textile waste annually (Fashinnovation, n.d.). From a financial perspective, the elimination of excess inventory means brands avoid tying up valuable capital, which is particularly beneficial for emerging brands seeking to scale sustainably without the financial burden of unsold products (Fashinnovation, n.d.). Moreover, market response times are dramatically shortened; new designs can transition from concept to consumer in a matter of weeks, a stark contrast to the six months or more typically required by traditional supply chains (Fashinnovation, n.d.). This inherent agility empowers brands to adapt swiftly to evolving trends, as demonstrated by companies like Zara, which leverages AI for rapid trend adaptation and efficient inventory management (DigitalDefynd, 2025). Furthermore, on-demand production inherently supports customization and personalization, as products are made to order based on individual measurements, a capability augmented by AI and 3D scanning (Fashinnovation, n.d.; Unspun, n.d.). AI also streamlines broader retail operations, improving customer service efficiency and responsiveness, and enhancing overall customer satisfaction through accessible and timely support (A3Logics, n.d.; DigitalDefynd, 2025).
A significant implication of these advancements is the evolution from a “Just-In-Time” (JIT) production model, primarily focused on efficiency and waste reduction, to a “Just-for-You” approach. The integration of AI and 3D modeling elevates JIT beyond mere inventory and process efficiency to enable mass customization and hyper-personalization at scale. When combined with individual 3D body scans and AI-driven pattern generation, on-demand manufacturing means that production is not merely initiated when needed, but also tailored exactly to what is needed for whom. This signifies a fundamental shift from a production-centric model, where clothes are made efficiently for a generic market, to a consumer-centric, bespoke manufacturing approach, where unique garments are created for individual consumers. This evolution fundamentally alters the relationship between the consumer and the brand, moving towards a service-oriented model where clothing is either co-created with the consumer or highly personalized to their specific needs and preferences. This shift has the potential to foster much stronger brand loyalty, reduce the impulse for fast fashion overconsumption (as items are unique and perfectly fitting), and create a more meaningful connection between the garment and its wearer.
The combined capabilities of AI for demand forecasting and design optimization (NewArc.ai, n.d.; SGAnalytics, 2024; A3Logics, n.d.), 3D modeling for virtual prototyping (Source Fashion, n.d.), and advanced CAM/3D knitting for on-demand production (Fashinnovation, n.d.; Unspun, n.d.) suggest a future where the entire fashion supply chain operates as a highly responsive, digitally managed ecosystem. AI’s ability to track garments from production to disposal (SGAnalytics, 2024; Refabric, n.d.), predict granular demand, and optimize cutting (NewArc.ai, n.d.) implies the creation and continuous refinement of a “digital twin” of the supply chain. In this scenario, real-time data informs every decision, from initial design concept and material sourcing to manufacturing, distribution, and even end-of-life management. This level of digital integration could lead to unprecedented levels of transparency and traceability throughout the fashion supply chain. This is critical for verifying sustainability claims, ensuring ethical labor practices, and identifying bottlenecks or inefficiencies. A fully digitized and AI-managed supply chain would allow for proactive problem-solving, dynamic resource allocation, and a truly closed-loop system, making the fashion industry far more resilient, efficient, and genuinely sustainable.
IV. Updated Health and Environmental Perspectives
The initial article correctly identified critical environmental and human health concerns associated with traditional fashion manufacturing. Recent research from 2023-2025 has not only corroborated these concerns but has also deepened the understanding of their pervasive and escalating nature.
A. Microplastics: A Deepening Health Concern
The presence of microplastics in human brain tissue, a concern raised in the original article, has been further substantiated by recent scientific findings. A study published in Nature Medicine confirmed the detection of microplastics in the frontal cortex of human brain tissue, with polyethylene (PE) identified as the most abundant polymer type. Alarmingly, concentrations of microplastics in human brain tissue demonstrated an approximate 50% increase between 2016 and 2024. These concentrations were found to be significantly higher—between 7 and 30 times—in brain tissue compared to liver and kidney tissue. Furthermore, analysis revealed that microplastic concentrations were three to five times higher in the brains of deceased patients diagnosed with dementia when compared to cognitively normal brains, suggesting a potential, though not yet definitively causal, link between plastic exposure and neurological conditions. One hypothesis for this accumulation is the affinity of plastics for fats, or lipids, which are abundant in brain tissue. This rapid escalation and systemic presence of microplastics underscore the profound and widespread nature of this environmental pollutant, indicating that merely reducing new synthetic fiber production may not be sufficient; addressing the continuous release from existing textiles is also critical.
Beyond neurological implications, new research from 2023-2025 has broadened the understanding of microplastics’ systemic health threats. A study, indicates that residing near ocean waters heavily polluted with microplastics may elevate the risk of cardiometabolic diseases, including Type 2 diabetes, coronary artery disease (plaque-clogged blood vessels), and stroke. This adds a significant dimension to the known health concerns. Both micro and nanoplastics are byproducts of the chemical breakdown of larger plastic waste, including synthetic fabrics. These ubiquitous particles have been detected in various human exposure pathways, including drinking water, seafood, and the air. A March 2024 study further reinforced these concerns, finding that patients with higher concentrations of microplastics in their arteries faced an increased risk of heart attacks, stroke, and mortality. Despite these alarming discoveries, a critical gap persists in the scientific understanding of microplastics. Researchers consistently highlight the need for further investigation into how microplastics enter the human body, the specific levels at which they become harmful, their precise accumulation sites, and their long-term health effects. This gap between the observed presence of microplastics in the body and a full understanding of their pathological mechanisms and dose-response relationships presents a challenge for clear regulatory action and public health guidance.
The issue of airborne microplastics, particularly from textiles, and their impact on indoor air quality, is also gaining increased attention. The textile industry is recognized as a major contributor to microplastic release throughout a garment’s entire lifecycle, encompassing production, use (especially washing), and eventual disposal. These microscopic plastic particles can readily become airborne and be inhaled, posing a silent but significant threat to respiratory health. A systematic review published in May 2024 confirmed that polyester and polyethylene terephthalate are the most dominant polymer types found in both indoor and outdoor environments, with synthetic textiles identified as a primary indoor source. Inhaled or ingested airborne microplastics have been linked to potential adverse effects such as inflammation, lung injury, and oxidative stress. Factors such as fabric construction, maintenance practices (e.g., washing frequency and methods), and the general wear and aging of garments all accelerate the breakdown of fibers and the subsequent release of microplastics. In response to this growing concern, ISO 4484-2:2023 has been established as the first standardized method for quantitatively and qualitatively measuring microplastics from the textile sector in solid, liquid, or gas streams, representing a crucial first step towards addressing this pervasive form of pollution.
B. Synthetic Fibers and the Skin Microbiome
The original article touched upon the disruption of the skin’s microbiome by synthetic fibers. Recent research provides a more detailed understanding of how synthetic materials influence the skin environment, particularly concerning moisture, heat, and odor. Synthetic fibers, such as polyester, exhibit different moisture and heat-trapping properties compared to natural materials. Their non-porous structure is conducive to trapping sweat and odor-causing bacteria against the skin, leading to the development and retention of unpleasant smells. In contrast, natural fibers facilitate better airflow and possess inherent properties that aid in moisture management and resist bacterial proliferation. This interaction is particularly relevant given the dramatic shift in the global textile market, where natural fibers declined from approximately 53.5% to 26.6% between 1995 and 2023, while synthetic fibers surged from 46.5% to a dominant 73.4%, with polyester alone experiencing a staggering 383.5% growth. This trend means that individuals are now in increased daily contact with synthetic materials.
The skin microbiome is a complex and vital ecosystem of microorganisms that plays a crucial role in maintaining overall skin health. It contributes to defending the body against harmful pathogens, training host immunity, and supporting epithelial turnover. A dysbiotic, or imbalanced, state of the skin microbiome is strongly associated with various skin diseases, particularly inflammatory conditions such as atopic dermatitis and psoriasis. Disruptions to this delicate balance can stem from a multitude of factors, including the application of topical products and broader environmental changes.
While the original article and some contemporary sources broadly link synthetic fibers to skin irritation and odor due to bacterial trapping, direct scientific studies from 2023-2025 explicitly detailing the precise mechanisms or specific microbial shifts (dysbiosis) caused by synthetic fibers themselves are less comprehensively presented in the available research. The current scientific focus appears to be more on developing robust in vitro and in vivo models of the human skin microbiome. These advanced models are designed to rigorously study microbe-compound, microbe-host, and microbe-microbe interactions, as well as the impact of various chemicals or disease states on microbiome stability. Some investigations explore how antimicrobial finishes in textiles can inadvertently disrupt the skin microbiome, and there is a holistic approach to understanding the complex interplay between the skin and textile microbiomes concerning pH, moisture content, and odor generation. New research into the dynamics of facial bacteria, such as Cutibacterium acnes and Staphylococcus epidermidis, is also opening avenues for probiotic therapies, indicating a deepening understanding of specific skin bacterial populations. This suggests a potential research lag in directly substantiating the specific dysbiotic effects of synthetic fibers on the skin microbiome, moving beyond general observations of odor and irritation. For the “new era” of fashion to truly deliver on its promise of improved skin health, more targeted scientific investigation is needed to establish definitive causal links and mechanisms between specific synthetic fiber types and skin microbiome dysbiosis.
Impact Area 1: Microplastics (Brain)
Key Findings (2023-2025):
Microplastics found in human brain tissue, particularly in the frontal cortex
Polyethylene (PE) identified as the most abundant polymer type
Microplastic concentrations rose by approximately 50% between 2016 and 2024
Concentrations were found to be 7-30 times higher in brain tissue compared to liver and kidney tissue
3-5 times higher concentrations in brains of deceased patients diagnosed with dementia compared to cognitively normal brains
Implications for Fashion Industry:
Urgent need to develop and adopt non-synthetic materials
Critical importance of low-shedding textile design
Increased pressure for industry accountability regarding material choices
Potential long-term liability concerns for fashion brands
Impact Area 2: Microplastics (Cardiometabolic)
Key Findings (2023-2025):
Living near ocean waters heavily polluted with microplastics linked to increased risk of Type 2 diabetes, coronary artery disease, and stroke
Higher concentrations of microplastics in arteries linked to increased risk of heart attacks, stroke, and death
Implications for Fashion Industry:
Heightened regulatory scrutiny on textile microplastic shedding expected
Demand for comprehensive lifecycle assessment of materials will increase
Industry will need to shift from awareness to action in plastic pollution policy
Impact Area 3: Microplastics (Airborne/Respiratory)
Key Findings (2023-2025):
Textile industry identified as a major contributor to microplastic release
Microplastics become airborne and inhaled, posing respiratory threats
Polyester and polyethylene terephthalate (PET) identified as dominant polymer types found in both indoor and outdoor environments
Synthetic textiles identified as a primary indoor source
Inhaled or ingested airborne microplastics linked to inflammation, lung injury, and oxidative stress
Implications for Fashion Industry:
Imperative to design textiles with reduced shedding characteristics
Development of standardized measurement methods (e.g., ISO 4484-2:2023) for microplastic footprint assessment
Need to focus on indoor air quality impact of clothing materials
Impact Area 4: Skin Microbiome (Synthetic Fibers)
Key Findings (2023-2025):
Synthetic fibers trap moisture and heat, creating ideal environments for odor-causing bacteria
Increased daily contact with synthetic materials (73.4% of global textile market in 2023)
Skin microbiome plays a crucial role in maintaining skin health and overall well-being
Dysbiotic (imbalanced) skin microbiome strongly associated with inflammatory skin diseases
Implications for Fashion Industry:
Need for more targeted scientific research on direct mechanisms of microbiome disruption by synthetic fibers
Drive for development of more breathable, natural fabrics
Potential for development of microbiome-friendly textile treatments
This section summarizes the updated health and environmental impacts of fashion materials, particularly focusing on microplastics and synthetic fibers, from 2023-2025. It highlights the escalating urgency of these issues and their implications for the fashion industry. By presenting these critical findings, the information underscores the imperative for the industry to pivot towards more sustainable and health-conscious material choices and production methods, while also identifying areas where further scientific investigation is needed to fully substantiate certain health claims and inform future innovations.
V. Gaps Between Theory and Practice: Challenges and Limitations
While the theoretical promise of leveraging AI and 3D modeling for a sustainable and healthy fashion industry is compelling, the practical implementation faces a complex array of challenges. These gaps between aspiration and reality significantly influence whether this innovative approach will achieve its full potential.
A. Technical and Implementation Hurdles
The integration of advanced AI technologies and 3D modeling into existing fashion industry workflows is fraught with significant technical and implementation hurdles. A primary deterrent is the high cost associated with AI integration. This encompasses not only the substantial initial investment in specialized software and hardware but also the ongoing expenses for maintenance, system updates, and the recruitment or training of skilled personnel. For many fashion companies, particularly smaller and medium-sized enterprises, these costs can be prohibitive, creating a significant barrier to entry and fostering a divide between industry giants and nascent innovators.
Furthermore, the efficacy of AI systems is intrinsically linked to the quality and accessibility of data. Acquiring and organizing relevant, high-quality data from diverse sources across the fashion supply chain is a complex and time-consuming undertaking. Issues such as data accuracy, consistency, and the integration of disparate data systems pose considerable challenges. Critically, if AI models are trained on biased or incomplete data, the resulting outputs can be flawed, leading to suboptimal or even counterproductive outcomes. Ensuring the privacy and security of this sensitive data, especially customer information, is paramount and adds another layer of complexity to data management protocols.
The fashion sector also faces a pronounced lack of specialized AI expertise. While numerous technology companies offer AI solutions, they frequently lack a deep understanding of the unique intricacies and nuances of the fashion industry. This often results in a mismatch between available technologies and the specific operational needs of fashion businesses. To bridge this gap effectively, companies must invest significantly in training existing staff or recruit professionals possessing dual expertise in both AI and fashion. The rapid pace of technological change in AI further compounds this challenge, necessitating continuous learning and adaptation to remain current with the latest advancements. Additionally, the absence of standardized protocols for AI implementation within the fashion industry complicates the selection of appropriate solutions and hinders interoperability between different systems.
Despite AI’s impressive capabilities, there are inherent limitations in its creative autonomy and comprehensive design capabilities. While generative AI excels at rapid ideation, trend forecasting, and pattern optimization, it often struggles to translate these concepts into fully finished products or comprehensive designs without substantial human oversight and manual adjustments. Current AI tools, for instance, may not fully grasp the complexities of clothing structure or the nuanced drape and feel of various fabrics, meaning human intervention remains vital for guiding and optimizing the final output. This implies that the promise of fully automated, AI-driven design-to-production is still largely a theoretical ideal. Practical implementation requires a hybrid human-AI workflow, which adds complexity, cost, and potentially limits the speed and efficiency gains initially envisioned. This suggests that the “new era” is more about human-AI collaboration than full AI replacement.
Finally, the accuracy and user adoption challenges for smartphone-based 3D scanning present a notable hurdle. While smartphone scanning offers unprecedented accessibility, concerns persist regarding its precision compared to dedicated, professional 3D scanners, particularly when users are fully dressed. Factors such as the scanning environment, the specific technology employed, and even the posture of the individual being scanned can introduce variability and affect data accuracy. Proper calibration and controlled environmental conditions, such as consistent lighting and neutral backgrounds, are essential to minimize discrepancies in data collection. Furthermore, widespread user adoption hinges on consumers’ trust and comfort with these new scanning technologies. While perceived usefulness and ease of use are crucial, consumer perceptions, including enjoyment, perceived value, and risk, significantly influence adoption attitudes. The interdependency of technical and human factors for adoption means that even if 3D scanning is technically accurate, low user adoption due to privacy concerns or discomfort will hinder its practical impact. The success of implementation requires a holistic strategy addressing technological maturity, workforce development, and consumer psychology simultaneously.
B. Ethical, Social, and Regulatory Considerations
The transformative potential of AI and 3D modeling in fashion also introduces a complex web of ethical, social, and regulatory challenges that must be carefully navigated.
A significant social concern revolves around job displacement and the impact on human creativity. As AI automates various tasks traditionally performed by human labor within the fashion industry, there is a legitimate concern that certain roles may become obsolete. This necessitates proactive strategies from companies, including investing in retraining and upskilling programs to enable their workforce to adapt to the evolving landscape. Moreover, the increasing integration of AI could inadvertently diminish the human element in fashion, potentially leading to a perceived loss of creativity and individuality if not implemented thoughtfully. While AI undeniably enhances creative exploration, the ongoing debate about AI’s potential to replace or merely augment human creativity remains pertinent.
Another critical ethical consideration is the potential for algorithmic bias and the imperative for transparency and accountability. AI models are trained on vast datasets, and if these datasets reflect historical biases, the AI will inevitably replicate and even amplify those biases. This can lead to discriminatory outcomes in various areas, including product design, marketing strategies, and even hiring practices within the fashion industry. The “black box” nature of many AI algorithms, which makes it challenging to understand how they arrive at their decisions, further exacerbates this issue, undermining trust and making it difficult to identify and rectify biases effectively. This suggests that the success of AI and 3D modeling in fashion isn’t solely about technological capability or economic viability, but also about building public trust and establishing robust ethical governance.
Data privacy risks associated with 3D body scans and biometric data represent a substantial barrier to widespread adoption. The collection of sensitive biometric data from 3D body scans necessitates stringent handling and storage protocols. Concerns about body information privacy negatively influence consumers’ willingness to adopt 3D body scanning technology. Consumers’ perceived inability to control the collection and use of personal body information is a significant impediment for retailers. However, evidence suggests that enhanced consumer experiences, particularly through personalization and responsiveness features of virtual try-on services, can mitigate these privacy concerns, highlighting a “privacy paradox” where convenience and benefit can outweigh apprehension.
The evolving landscape of AI regulation in the fashion industry adds another layer of complexity. The current lack of clear regulatory frameworks for AI creates uncertainty for businesses and can inadvertently hinder responsible innovation. There is a growing call for greater transparency in AI development and usage, along with the establishment of comprehensive ethical guidelines and regulatory standards to ensure responsible growth. Furthermore, intellectual property concerns are emerging, as some AI-generated designs could potentially be based on copyrighted work, with legal precedents and guidelines in this area still evolving. This regulatory lag in fast-paced innovation highlights a critical tension: the desire for rapid technological transformation versus the need for responsible, ethical, and legally sound implementation. The “future of AI in the fashion industry is still unknown” partly because these foundational frameworks are still nascent.
C. Economic Viability and Scalability Challenges
While the integration of AI and 3D modeling promises long-term efficiencies and sustainability benefits, the economic viability and scalability of this approach present significant hurdles, particularly in the short to medium term.
The investment required for retooling traditional manufacturing processes is substantial. Transitioning from established bulk production models to on-demand, personalized manufacturing necessitates significant capital expenditure in new machinery, software, and infrastructure (Size Stream, n.d.). The high cost of AI implementation, encompassing software licenses, hardware upgrades, and the recruitment or training of skilled personnel, acts as a major financial barrier, especially for smaller businesses that may lack the deep pockets of larger corporations (Sustainability Directory, n.d.). This indicates that while the long-term vision might be cost-effective due to waste reduction and efficiency, the transition is capital-intensive. This suggests a potential widening gap between large, well-funded fashion companies that can afford these transformations and smaller, independent brands. Without financial incentives or accessible, lower-cost solutions, the widespread adoption of this “new era” could be limited to industry giants.
Consumer willingness and trust in adopting new purchasing behaviors are also crucial for scalability. While technologies like 3D body scanning and virtual try-on are advancing rapidly, consumer comfort and readiness to embrace these new methods are still developing (Size Stream, n.d.; Frontiers, 2025). The adoption of such systems is heavily influenced by perceived usefulness, ease of use, and overall positive consumer perceptions, including enjoyment and value (Frontiers, 2025). Despite the clear potential, the widespread impact of 2D/3D measurement technology on consumer purchasing habits is still in its early stages (Frontiers, 2025). This behavioral lag means that technological capability alone is insufficient; consumer psychology and trust are equally critical. The success of this approach hinges not just on technological innovation but on effective marketing, user experience design, and trust-building strategies to overcome inherent consumer hesitations and shift long-standing purchasing habits. If consumers do not fully embrace these new methods, the economic viability of personalized, on-demand fashion will be compromised.
Finally, balancing the profitability of sustainable practices with market demands remains a complex challenge. While AI-powered sustainable practices can be both “practical and profitable” (Refabric, n.d.) through waste reduction and optimized inventory, the initial investment and the need to retool supply chains can be substantial (Size Stream, n.d.). The fast-paced nature of the fashion industry requires brands to adapt quickly to changing trends (DigitalDefynd, 2025). Balancing the benefits of on-demand production, which may involve a slightly slower production cycle for customization, with the rapid demands of fast fashion can be difficult. While AI can undoubtedly increase sales and profitability through optimized inventory and enhanced customer satisfaction (DigitalDefynd, 2025), there is also a concern that the pressure to produce quickly, even with AI monitoring supply chains, could inadvertently lead to exploitative practices (East Carolina University Libraries, n.d.). This creates a tension between business growth driven by AI and the overarching goal of sustainability.
D. The Environmental Footprint of AI Itself
A significant, yet often overlooked, challenge to the sustainability promise of AI in fashion is the environmental footprint of AI technology itself. This creates a latent environmental cost of digital sustainability, a crucial contradiction that must be addressed.
The energy consumption and carbon emissions from AI model training and data centers are substantial. Training and running complex generative AI models, particularly those with billions of parameters, demand enormous amounts of electricity, frequently derived from fossil fuels. This directly contributes to greenhouse gas emissions and exacerbates climate change (East Carolina University Libraries, n.d.; MIT News, 2025). The electricity demands of data centers, which host these AI operations, are skyrocketing; North American data center power requirements, for instance, doubled between 2022 and 2023, partly driven by generative AI. Globally, data center electricity consumption is projected to approach 1,050 terawatts by 2026, which would position them as one of the world’s largest electricity consumers (MIT News, 2025). To illustrate the scale, training a single large AI model like GPT-3 has been estimated to consume 1,287 megawatt-hours of electricity, generating 552 tons of carbon dioxide equivalent—comparable to the annual emissions of 123 gasoline-powered passenger vehicles (East Carolina University Libraries, n.d.; MIT News, 2025). The environmental impact is further compounded by the energy and resource intensity involved in the manufacture and transport of high-performance computing hardware, such as Graphics Processing Units (GPUs), which often involves environmentally damaging mining procedures and the use of toxic chemicals (MIT News, 2025).
Beyond energy, water usage for cooling AI hardware and the generation of e-waste are critical concerns. A considerable volume of water is required to cool data centers, placing significant strain on municipal water supplies (MIT News, 2025). Estimates suggest that approximately two liters of water are needed for cooling per kilowatt-hour of energy consumed by a data center, meaning the training of GPT-3 alone may have consumed 700,000 liters of freshwater (East Carolina University Libraries, n.d.; MIT News, 2025). Furthermore, the production and improper disposal of AI hardware contribute to electronic waste (e-waste), which contains harmful chemicals that can contaminate the environment (East Carolina University Libraries, n.d.). This means that the “new era” must not only focus on sustainable fashion practices but also on sustainable AI, requiring the development of more energy-efficient AI models, reliance on renewable energy for data centers, and addressing the lifecycle of AI hardware.
Finally, there is a paradox of AI-driven consumption versus sustainability goals. While AI is championed for its role in optimizing production and reducing waste, its capabilities in hyper-personalization of marketing campaigns and precise trend prediction can inadvertently increase consumption and waste by encouraging impulse buying and fueling the fast fashion cycle (East Carolina University Libraries, n.d.). This creates a tension where a technology aimed at sustainability could simultaneously drive unsustainable consumption patterns. This suggests that the “might not work” scenario could arise if AI’s commercial applications (driving consumption) outweigh its sustainability applications (reducing waste). Ethical AI development and responsible marketing practices are essential to ensure AI genuinely contributes to a sustainable future, rather than just shifting the environmental burden or accelerating consumption.
E. Performance Limitations of Alternative Materials
The original article’s call to shift away from environmentally harmful synthetic fibers faces a significant practical challenge: the performance limitations of alternative materials. Synthetic fibers currently offer distinct functional advantages that are difficult to replicate with natural or biodegradable alternatives.
Synthetic fibers are widely favored for their functional advantages, including superior dimensional stability, color retention, and ease of care (Fulgar S.p.A., n.d.). Their engineered polymer structure enables them to retain their original elasticity and fit over extended periods, resisting deformation from washing or wear (Fulgar S.p.A., n.d.). These materials are dominant in specialized sectors like sportswear and technical textiles due to their high mechanical strength, adaptability to various technical requirements, and specific performance properties such as breathability, water resistance, and stretch (Fulgar S.p.A., n.d.; Permanent Style, 2019). Some synthetic fibers can also be engineered to be stainproof (Permanent Style, 2019). While earlier generations of synthetics had drawbacks in breathability and softness, continuous innovation in yarn development has led to contemporary synthetic fabrics that offer superior fit and a “second-skin feel” (Fulgar S.p.A., n.d.).
The core challenge lies in developing natural or biodegradable alternatives that can consistently match synthetic performance across all applications. Natural fibers, while environmentally preferable, often require more meticulous care (Permanent Style, 2019). Artificial fibers, such as viscose, may be biodegradable but can be more energy-intensive to produce than natural fibers (Permanent Style, 2019). While natural fibers like wool possess inherent benefits—being hypoallergenic, flame-resistant, odor-resistant, and capable of absorbing significant moisture without feeling damp—they may not offer the same specific combinations of performance (e.g., extreme water resistance or high stretch) as synthetics without additional chemical treatments that can compromise the fabric’s natural “hand” or feel (Permanent Style, 2019). The process of researching and developing new sustainable textile fibers is inherently slow, although AI is beginning to accelerate this process by identifying and simulating viable options (Source Fashion, n.d.). This performance gap means that a complete and rapid shift away from synthetics is difficult without significant breakthroughs in material science for natural or bio-based alternatives that can match or exceed synthetic performance. If these alternatives do not meet consumer and industry performance expectations, the “new era” might struggle to gain widespread market acceptance, particularly in segments where functionality is paramount.
This situation highlights a complex interplay between material innovation, consumer expectation, and sustainability goals. The challenge is not merely to find new materials, but to discover and scale those that are simultaneously sustainable, high-performing, and cost-effective enough to meet evolving consumer demands (Source Fashion, n.d.; Fulgar S.p.A., n.d.; Permanent Style, 2019). If sustainable alternatives prove to be significantly more expensive or compromise on desired features like stretch or easy care, consumer adoption will likely be slow, even with the benefits of AI-driven personalization. This necessitates a multi-pronged approach for the “new era”: not just AI and 3D modeling for production, but also aggressive research and development in material science to close the performance gap of sustainable alternatives. The success of the overall approach is highly dependent on the co-evolution of these different technological fronts and consumer willingness to adapt their expectations and habits.
VI. Bridging the Divide: Strategies for a Sustainable and Healthy Future
Bridging the identified gaps between the theoretical promise and practical implementation of AI and 3D modeling in fashion requires a multi-faceted and strategic approach. The following recommendations outline pathways to overcome technical, economic, ethical, and material challenges, fostering a genuinely sustainable and healthy future for the industry.
Recommendations for overcoming technical and economic barriers:
To address the high costs and technical complexities of AI and 3D modeling integration, a strategic focus on investment in accessible AI/3D solutions is crucial. This involves prioritizing the development of more affordable and user-friendly AI and 3D modeling software and hardware. Leveraging cloud-based services and fostering open-source initiatives can significantly lower the barrier to entry for small and medium-sized enterprises (SMEs), democratizing access to these transformative technologies (Sustainability Directory, n.d.). Alongside this, promoting standardization and interoperability across the industry is paramount. Establishing common data formats for 3D body scans, material properties, and AI integration protocols will ensure seamless workflows across the entire supply chain, reducing complexity and facilitating broader adoption (Size Stream, n.d.; Sustainability Directory, n.d.). Recognizing the current limitations of AI’s creative autonomy, the industry must embrace hybrid human-AI workflows. AI should be viewed as a powerful tool to augment human creativity and efficiency, rather than a complete replacement (Plug and Play Tech Center, n.d.; A3Logics, n.d.; ResearchGate, n.d.; Wilson College of Textiles, 2024). This necessitates significant investment in training and upskilling programs for designers and production teams, enabling them to effectively collaborate with AI systems and leverage their capabilities (Reynolds Center for Business Journalism, 2025; Sustainability Directory, n.d.). Finally, implementing robust strategic data management frameworks is essential. This includes developing clear protocols for data collection, cleaning, integration, and security to ensure that AI models are trained on high-quality, unbiased data, thereby improving the accuracy and reliability of AI-driven processes (Sustainability Directory, n.d.).
Strategies for building consumer trust and driving adoption:
Consumer willingness and trust are pivotal for the widespread adoption of personalized, AI-driven fashion. To cultivate this, transparency in data usage is non-negotiable. Fashion brands must clearly communicate how 3D body scan data and other personal information are collected, stored, and utilized, emphasizing stringent security measures and providing users with clear control over their data to mitigate privacy concerns (Size Stream, n.d.; ResearchGate, n.d.). Concurrently, continuous efforts must be directed towards enhanced user experience for VTO and scanning technologies. Improving the accuracy, ease of use, and realism of smartphone-based 3D scanning and virtual try-on experiences is vital (Size Stream, n.d.; OpenTools.ai, 2025; Frontiers, 2025). Focusing on features that enhance perceived usefulness and enjoyment will directly drive consumer adoption (Frontiers, 2025). Furthermore, comprehensive education and awareness campaigns are necessary to inform consumers about the tangible environmental and health benefits of personalized, on-demand, and sustainable fashion. This can help shift consumer preferences and generate demand for these innovative approaches.
Approaches to mitigate the environmental impact of AI technologies:
Addressing the inherent environmental footprint of AI itself is crucial to ensure that digital sustainability efforts are genuinely net positive. This requires a commitment to sustainable AI development, prioritizing research and development into more energy-efficient AI algorithms and models to reduce the computational power required for training and inference (East Carolina University Libraries, n.d.; MIT News, 2025). Simultaneously, advocating for and investing in green data centers is imperative. This means supporting facilities powered by renewable energy sources and employing advanced, water-efficient cooling technologies to minimize their ecological impact (East Carolina University Libraries, n.d.; MIT News, 2025). Implementing robust lifecycle management of AI hardware is also vital, encompassing strategies for responsible manufacturing, reuse, and recycling of GPUs and servers to minimize e-waste and resource depletion (East Carolina University Libraries, n.d.). Lastly, a critical ethical dimension involves developing ethical AI for consumption. AI marketing and recommendation systems should be designed with clear ethical guidelines that prioritize conscious consumption over hyper-personalization that could inadvertently lead to impulse buying and overproduction (East Carolina University Libraries, n.d.).
Innovations in material science to address performance gaps:
To overcome the performance limitations of alternative materials and facilitate a genuine shift away from problematic synthetics, aggressive accelerated sustainable material R&D is essential. This involves leveraging AI and machine learning to rapidly identify, simulate, and develop new bio-based, biodegradable, and low-release materials that can match or exceed the performance characteristics (e.g., durability, stretch, ease of care) of traditional synthetics (Source Fashion, n.d.; Fulgar S.p.A., n.d.; Permanent Style, 2019). A core focus should be on designing materials that inherently minimize microplastic shedding throughout their entire lifecycle, from production and use to end-of-life (Aquafil, n.d.). Finally, investing in circular material streams is critical. This means developing and scaling technologies for efficient textile recycling and upcycling, enabled by AI, to create truly closed-loop material systems that reduce reliance on virgin resources and minimize waste (Refabric, n.d.).
VII. Conclusion: A Realistic Outlook for Fashion’s New Era
The vision articulated in the original article, “A New Era in Fashion: Leveraging AI and 3D Modeling for Sustainable Development and Human Health,” remains a profoundly compelling and necessary trajectory for an industry grappling with significant environmental and health crises. The advancements observed between 2023 and 2025 demonstrate tangible progress: AI-driven design, hyper-personalization, and on-demand manufacturing offer clear pathways to drastically reduce material waste, optimize garment fit, and enhance the consumer experience. The escalating health concerns associated with microplastics, including their increasing presence in human brain tissue and newly identified links to cardiometabolic diseases, further underscore the critical urgency of this transformation.
However, the journey from theoretical promise to widespread practical implementation is fraught with substantial challenges and inherent complexities. The high costs associated with AI integration, the intricacies of data quality and management, and a critical shortage of specialized expertise represent significant technical and economic hurdles. Furthermore, the inherent limitations of AI in achieving fully autonomous creative design, requiring continued human oversight, temper the initial vision of complete automation. Beyond technicalities, ethical considerations loom large: concerns about job displacement, the potential for algorithmic bias, and the paramount importance of data privacy surrounding sensitive biometric information demand careful navigation and robust regulatory frameworks. Perhaps most paradoxically, the environmental footprint of AI itself—its considerable energy and water consumption, and the generation of e-waste—presents a hidden cost that could potentially offset some of the sustainability gains in fashion production. Moreover, the persistent performance advantages of synthetic fibers continue to pose a challenge for a complete shift towards natural or biodegradable alternatives, highlighting a critical performance gap that requires ongoing material innovation.
The ultimate success of this “new era” in fashion hinges not on a singular technological breakthrough, but on a holistic, collaborative, and realistically managed approach. This necessitates continuous innovation in AI and 3D modeling, coupled with an unwavering commitment to ethical development, transparent data practices, and the proactive establishment of clear regulatory guidelines. Strategic investment in upskilling the existing workforce and fostering genuine consumer trust are equally critical components for widespread adoption.
The future of fashion is undeniably digital, sustainable, and personalized. AI and 3D modeling are not merely supplementary tools but foundational technologies poised to redefine how clothes are conceived, produced, and consumed. While the path forward involves navigating complex technical, economic, and ethical landscapes, the potential for a fashion industry that is both environmentally responsible and genuinely beneficial for human health is within reach. It demands sustained investment, robust cross-sector collaboration, and a realistic understanding of both its immense promise and its inherent limitations. The conversation initiated by the original article is more relevant than ever, urging all industry stakeholders to move decisively from mere awareness to impactful, responsible action.
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