1.1 Background and Motivation

Women’s health remains one of the most critical yet underserved domains in global healthcare. According to global health reports, World Health Organization (WHO), cancer has become the second leading cause of death worldwide and the most prevalent diseased and concerns among women (Moghimikandelousi et al., 2024; Wild et al. 2025). At the same time, advances in liquid biopsy and biomarker science have shown that blood contains essential proteins and molecular signals that play a central role in maintaining healthy physiological function. As a result, blood-based diagnostics are increasingly used for early detection, continuous monitoring, and personalized risk stratification (Savas and Coskun, 2025).

For women, menstrual blood offers an additional, uniquely rich biological specimen. Recent studies indicate that menstrual effluent carries inflammatory markers, malignancies-associated antigens, hormonal indicators, extracellular vesicles, and microbiome-related signals that can be leveraged for non-invasive diagnostics and longitudinal health monitoring (Zaheer et al., 2024; Wang et al., 2025). Despite this potential, menstrual blood remains underutilized in mainstream clinical pathways due to regulatory uncertainty, fragmented standards, and limited ethical guidance on data use and consent (Zaheer et al., 2024; Wang et al., 2025; Dosnon et al., 2025).

Against this backdrop, FemTech-digital and device-based technologies designed to support women’s health-has emerged as one of the fastest-growing segments in digital healthcare, spanning menstrual-cycle tracking, fertility management, pregnancy monitoring, and pelvic health solutions (Brayboy and Quaas, 2022; Rajesh, 2025). However, the rapid expansion of FemTech has largely prioritized technical innovation in sensing, data analytics, and user engagement, while questions of bias, data governance, long-term sustainability, and equity continue to trail progress in technical innovation (McMillan, 2022; Suoto and Muller, 2025).

Meanwhile, other industries, particularly manufacturing, have started to implement Quality 4.0 practices that integrate advanced analytics, cyber-physical systems, and continuous improvement to ensure reliability and governance at scale (Prashar, 2023). These advancements suggest that the next phase of FemTech should focus on incorporating these principles into the design of menstrual diagnostic systems that is integration of how Responsible AI, digital quality management, and ESG–SDG principles (Saenz et al., 2024).

When aligned with Environmental, Social, and Governance (ESG) standards and the United Nations Sustainable Development Goals—especially SDG 3 (Good Health and Well-being) and SDG 5 (Gender Equality)—FemTech has the potential to transform menstrual health technologies into platforms that promote fairness, transparency, and genuine social value (Belhadi et al., 2021; Lee and Moon, 2022; Lee et al., 2025). This study is motivated by the need to reposition menstrual blood-based diagnostics within such a Responsible AI framework, and to articulate how FemTech can evolve from purely technical products into ethically grounded, sustainability-aware healthcare infrastructures.

1.2 Problem Statement

Despite the absence of integrated governance systems, the FemTech industry continues to grow rapidly. AI-enabled menstrual health technologies still lack integrated governance frameworks, which frequently leads to inconsistent health outcomes and unreliable diagnostic results. FemTech still lacks standardized ethical and quality-management frameworks, despite the fact that the manufacturing sector has already integrated Quality 4.0 practices throughout its operations. Therefore, in order to guarantee openness, justice, and long-term sustainability, governance systems are crucial (Prashar, 2023; Lee et al., 2025).

This gap is further compounded by insufficient alignment with ESG and SDG principles, which limits the ability of menstrual diagnostic technologies to promote social equity, environmental responsibility, and governance accountability (Belhadi et al., 2021). Although recent biomedical studies highlight the diagnostic value of menstrual blood and demonstrate the feasibility of AI-driven biomarker prediction (Zaheer et al., 2024; Wang et al., 2025), their integration into ethical, high-quality FemTech systems remains fragmented.

Therefore, a critical problem emerges: the absence of a unified framework that connects Responsible AI, digital quality management, and ESG–SDG governance to guide the development of trustworthy menstrual diagnostic technologies. This study responds to this gap by introducing MenstruAI, a conceptual framework designed to guide FemTech innovation with equity and transparency at its core.

1.3 Objective and Contribution

The primary objective of this study is to integrate Responsible AI, ESG–SDG governance, and Quality 4.0 principles into menstrual health innovation. To achieve this, the study develops MenstruAI, a unified conceptual framework. While recent research demonstrates the growing diagnostic potential of menstrual blood and the feasibility of AI-driven biomarker prediction (Zaheer et al., 2024; Wang et al., 2025), these technological advancements have not yet been matched by corresponding ethical, governance, and quality-assurance structures within the FemTech industry.

MenstruAI advances three core objectives:

1. Embedding Responsible AI ethics—ensuring fairness, explainability, privacy, bias mitigation, and transparent decision-making across menstrual-health technologies (Bahangulu and Owusu-Berko, 2025).

2. Integrating ESG–SDG principles—aligning FemTech development with environmental responsibility, gender equity, and global public-health goals (Belhadi et al., 2021; Lee et al., 2025).

3. Applying Quality 4.0 practices—adapting traceability, feedback loops, and digital quality-management tools validated in industrial sectors to FemTech and healthcare (Prashar, 2023).

Unlike prior work, this study aims to present a multi-layered conceptual architecture that connects biosensing technologies, edge-AI analytics, and responsible governance. This architecture enables the application of Responsible AI to gender-specific digital healthcare, expanding the discussion on equitable digital health and providing a practical model for designing trustworthy FemTech systems.

As shown in the conceptual research design in Figure 1, the development of MenstruAI is informed by the following guiding questions:

Figure 1

Conceptual Research Design

· How can Responsible AI principles be practically and consistently applied to menstrual-health diagnostic tools?

· What forms of ESG–SDG governance can ensure ethical and sustainable development within FemTech?

· How can Quality 4.0 methods strengthen transparency, reliability, and ongoing improvement within AI-supported menstrual diagnostics?

· How can biosensing, AI analytics, and governance elements be integrated into a coherent framework for trustworthy FemTech systems?

This study suggests MenstruAI as a governance-centered integration framework, in contrast to previous studies that address Responsible AI, ESG–SDG governance, Quality 4.0, or FemTech ethics as distinct or tangentially related domains. The translation of ethical, sustainable, and quality principles into operational design mechanisms integrated throughout the diagnostic lifecycle is what makes MenstruAI unique, not the collection of preexisting concepts. In particular, MenstruAI presents a mechanism-based architecture where Quality 4.0 is operationalized as a continuous monitoring and improvement loop, Quality of Use (QoU) is formalized as a user-facing assurance layer, and governance requirements are implemented as control points. With this structure, the framework offers a design-oriented approach to responsible FemTech innovation, going beyond declarative ethical guidelines.

2.1 Responsible AI and Digital Quality Management

Responsible AI emphasizes fairness, accountability, transparency, and privacy as core values embedded throughout the AI lifecycle. The importance of these principles is increasingly recognized as essential for ensuring reliability and ethical integrity within data-driven healthcare systems (Kim et al., 2022). At the same time, Quality 4.0—originating in advanced manufacturing—brings together automation, analytics, cyber- physical systems, and continuous improvement to strengthen operational performance (Prashar, 2023). When applied together, Responsible AI and Quality 4.0 reframe AI assurance as a structured governance approach rather than a purely technical task, positioning traceability, auditability, monitoring, and explainability as practical, measurable dimensions of quality. This convergence forms a foundation for digital health systems that require both algorithmic trust and ethical oversight, offering a model applicable to emerging FemTech innovation (Kiseleva et al.,2022; Moreno-Sánchez, 2025).

2.2 ESG–SDG Integration in Technological Innovation

ESG frameworks provide a basis for assessing the long-term responsibility and sustainability of organizational practices. When applied in real operations, ESG frameworks guide organizations in evaluating efficiency while also drawing attention to the wider social and environmental impacts that technological choices may create. In digital-health contexts, the application of ESG frameworks is directly connected to SDG objectives—most notably SDG 3, which emphasizes health and well-being, and SDG 5, which centers on gender equality (Belhadi et al., 2021; Lee et al., 2023).

When developing biosensors, waste reduction and the use of biodegradable materials are typically prioritized from an environmental standpoint. In order to ensure that technology breakthroughs benefit a wider population, social focus is placed on gender equity and inclusivity. Governance ties these directions together by emphasizing transparency, consent-driven data procedures, and ethical monitoring in how data are managed.

2.3 Menstrual Health as a Diagnostic Frontier

Menstrual blood contains diagnostically relevant components such as CRP, CEA, CA-125, hormones, metabolites, immune-related proteins, and extracellular vesicles, which reflect inflammatory and reproductive- health conditions (Zaheer et al., 2024; Wang et al., 2025). The adoption of menstrual-blood technologies has advanced slowly despite its potential for diagnosis due to unresolved difficulties, especially with regard to ethical governance, informed consent, dataset representation, and uniform AI workflows. These unresolved concerns still constrain real-world application, suggesting that formal governance and Responsible AI are vital if FemTech technologies are to be used safely and fairly. Responsible AI approaches—including federated learning, privacy-preserving analytics, and bias mitigation—offer routes to resolve these concerns and assist secure and equitable menstrual-health diagnostics.

2.4 Conceptual Gap

Although FemTech has advanced in biosensing, algorithm development, and mobile platform design, progress in ethics, governance, and sustainability has lagged behind (McMillan, 2022). In many systems, accuracy or usability has been prioritized over explainability, responsible data stewardship, quality controls, and long-term social and environmental impact. Because there is no coordinating framework that links technical performance to ethical and sustainable operation, biosensors, AI models, and platform features often evolve in isolation. Similar tendencies are evident across the broader AI and digital-health ecosystem, where short-term benefits frequently outweigh long-term accountability or governance.

Biomedical AI studies have demonstrated that biomarkers can be inferred reliably from large-scale public health datasets (Jung & Hwang, 2024), yet this technological progress has not translated into parallel advancements in FemTech design or governance. A unifying model that treats Responsible AI, ESG–SDG governance, and Quality 4.0 as performance requirements—alongside accuracy—is still lacking. This gap motivates the MenstruAI framework, which links biosensing innovation with responsible AI and sustainability- oriented quality management to support more ethical and accountable FemTech development. This need is further supported by comparative studies of current governance and quality paradigms, which show that although pertinent frameworks offer complementary viewpoints, they have mostly been used separately rather than operationally integrated within FemTech systems (Table 1).

Table 1

Comparative Analysis of Quality and Governance Frameworks

To clarify the conceptual distinctiveness of the proposed framework, Table 2 compares key characteristics of prior FemTech and Responsible AI–related studies with those of MenstruAI. The comparison demonstrates how MenstruAI incorporates ethical, sustainable, and quality considerations into operational design mechanisms, going beyond principle-level or declarative approaches.

Table 2

Comparative Differentiation Table (Prior Studis vs MenstruAI)

Building upon these insights, the next section outlines the methodological approach used to construct the MenstruAI framework.

3.1 Research Design

The research process developed through three iterative stages. To find common ground, a systematic review of research on subjects relating to AI governance, sustainability frameworks, menstrual-health technologies, and biomarker diagnostics was first carried out. Second, information from these domains was integrated into a multi-layer conceptual model that integrated biosensing, lightweight AI inference, and responsible governance. Finally, a conceptual evaluation of technical plausibility was conducted by looking at proxy validation evidence from biomedical AI literature. When taken as a whole, these phases highlight MenstruAI's interdisciplinary nature and place menstrual diagnostics at the nexus of ethics and technology.

3.2 Literature Mapping

The conceptual framework was developed based on findings from various literature. Search terms like "FemTech," "menstrual diagnostics," "Responsible AI," "Quality 4.0," and "ESG–SDG" were utilized. Although this process does not constitute a full PRISMA review, it did enable a targeted investigation of conceptual overlaps relevant to responsible menstrual innovation.

With complementary strengths found throughout the literature, responsible AI emphasizes the importance of justice, transparency, and privacy protection. ESG–SDG frameworks highlight long-term societal alignment and Quality 4.0 introduces mechanisms for traceability and continuous improvement. Combining these viewpoints provided a foundation for creating a diagnostic ecosystem that is more dependable and accountable.

3.3 Framework Development

Drawing from the reviewed evidence, MenstruAI was structured as a three-layer conceptual architecture (Chiarini, 2020; Prashar, 2023; Lee et al., 2025; Zaheer et al., 2024). Biosensing and biomimetic materials, utilizing non-invasive LFIA-based pads and microfluidic substrates capable of detecting clinically meaningful biomarkers such as CRP, CEA, and CA-125. The second layer incorporates edge-AI analytics and privacy-preserving computation by applying lightweight neural networks, on-device inference, and federated learning to enable diagnostic analysis while reducing data exposure and computational burden. The third layer introduces responsible governance grounded in ESG–SDG sustainability and Quality 4.0 principles, embedding fairness, explainability, traceability, and long-term sustainability while aligning innovation with SDG 3 on health and SDG 5 on gender equity. As a whole, the three-layer structure is intended to address long-standing challenges in FemTech, including opaque algorithmic behavior, limited reliability, and uneven ethical standards.

While Figure 2 presents the structural composition of the MenstruAI framework, Figure 3 illustrates how this architecture is operationalized as a governance-oriented workflow rather than a static conceptual model. The process begins with user-driven data input and consent, followed by menstrual data acquisition through non-invasive biosensing materials, preprocessing, and on-device AI inference. Diagnostic results are delivered together with interpretable explanations to support user understanding, while user interaction and feedback are collected as Quality of Use (QoU) signals and continuously monitored under a Quality 4.0 perspective.

Figure 2

MenstruAI System Architecture

Figure 3

MenstruAI Operational Workflow

Governance checkpoints—such as privacy protection, bias assessment, regulatory compliance, and quality assurance—are embedded at predefined stages of the workflow. These checkpoints inform periodic model updates and governance decisions, ensuring that ethical, quality, and sustainability considerations are enforced throughout the system lifecycle. This operational design clarifies when and how monitoring, evaluation, and continuous improvement activities occur within the MenstruAI framework.

3.4 Ethical–Quality–Sustainability Mapping

To ensure conceptual rigor, each dimension of the model was systematically mapped against established global governance frameworks. Fairness, explainability, and privacy protection were contributed by responsible AI; traceability, dependability, and continuous improvement were strengthened throughout the diagnostic workflow by Quality 4.0; and environmental responsibility, accessibility, equity, and transparent decision-making were highlighted by ESG–SDG governance. The framework provides an organized way to integrate technology, ethics, and sustainability by lining up these three domains. This alignment enables the development of a unified diagnostic ecosystem. To ascertain how this mapping can be successfully applied in practical contexts, more research is necessary (Prashar, 2023; Chiarini, 2020; Belhadi et al., 2021; Lee et al., 2025).

3.5 Conceptual Propositions

Because this study takes a conceptual rather than empirical approach, the theoretical foundations are expressed through four guiding propositions. These ideas are offered as reasonable expectations that guided the creation of the MenstruAI framework rather than as verified assertions.

First, integrating Responsible AI principles into menstrual-diagnostic systems may help improve fairness, transparency, and user trust, which involves implementing explainability methods, reducing algorithmic bias, and adopting privacy-by-design practices.

Second, aligning FemTech development with ESG–SDG governance may help strengthen long-term sustainability and ethical accountability. Especially, attention to environmental responsibility, inclusiveness, and transparent decision-making may support the development of menstrual-health technologies that reach more diverse users.

Third, applying Quality 4.0 concepts may improve traceability and operational stability across biosensing and AI-enabled diagnostic workflows. More dependable inference processes may result from continuous system improvement and data-driven quality management.

Finally, a unified framework that brings together Responsible AI, ESG–SDG governance, and Quality 4.0 may offer advantages beyond fragmented or technology-only approaches. By tying these aspects together, FemTech innovation that strives to be reliable and socially significant may have a stronger basis.

3.6 Proxy Validation Using Existing Biomedical AI Research

This study uses proxy validation from previously published biomedical AI research to investigate conceptual feasibility. Although no new empirical experiments were conducted here, prior findings offer indirect yet informative signals supporting the technical assumptions behind MenstruAI.

For example, Jung and Hwang (2024) demonstrated that hemoglobin levels can be estimated with notable accuracy using national health-screening datasets and models such as XGBoost, LightGBM, and multilayer perceptrons, indicating that large-scale clinical data contain physiologically meaningful patterns that machine- learning models can infer. If analogous relationships hold for menstrual-blood biomarkers such as CRP, CEA, or CA-125, non-invasive inference pipelines may be technically achievable. Empirical validation is still needed, but the existing evidence points toward a promising trajectory for future development.

Multimodal investigations reporting that inflammatory conditions can be inferred from ECG and wearable- sensor data add further support for the idea that physiological signals may serve as practical surrogates for biochemical markers. Complementing this, studies on smartphone-driven interpretation of lateral- flow assays — with reported AUC values frequently above 0.85 — illustrate that lightweight, on-device image quantification can enhance both the reliability and usability of colorimetric diagnostics, aligning well with the edge-AI orientation of MenstruAI. Table 3 summarizes representative proxy studies along with their relevance to the framework. Although these studies do not constitute a direct validation of MenstruAI and should not be interpreted as performance estimates for the system, they collectively indicate that core components of the proposed framework — non-invasive biosensing, image-based signal extraction, and compact AI modeling — have already demonstrated practical feasibility in adjacent domains. This offers preliminary support for the overall technical trajectory that MenstruAI sets out.

Table 3

Proxy Validation Summary for Biomedical AI Models

3.7 Quality of Use (QoU) Operationalization

Quality of Use (QoU) is positioned in this study as a foundational perspective for evaluating menstrual- health technologies developed within a Responsible AI framework. Quality of Use (QoU) should focus on the lived experience of individuals interacting with diagnostic systems rather than technical accuracy. According to Saenz et al. (2024) and Moreno-Sánchez et al. (2025), Quality of Use (QoU) is conceptualized as a multidimensional construct encompassing user autonomy, interpretability, psychological safety, and interaction quality, reflecting how diagnostic tools shape comprehension, trust, and overall engagement.

Within the MenstruAI framework, Quality of Use (QoU) is expressed through four practical assessment components. (1) The User Autonomy Index takes into account a person's capacity for informed decision- making. (2) The results are evaluated using the Interpretability and Comprehension Score. (3) Perceptions of emotional comfort, perceived risk, and general dependability are reflected in the Psychological Safety and Trust Metric. Lastly, workflow usability, stability, and the degree of user burden reduction during practical use are all examined in (4) Sustainable Interaction Efficiency. These dimensions provide a structured basis for future validation and align with established principles in Responsible AI and digital quality-management research, even though Quality of Use (QoU) is not empirically measured in this study (Prashar, 2023).

In the future, multi-item instruments modified from research on human-AI interaction and digital health could be used to operationalize Quality of Use (QoU). User autonomy may be evaluated through digital- consent frameworks, interpretability and comprehension through tools such as the System Usability Scale (SUS) and eHealth literacy assessments, psychological safety using trust-in-AI or perceived reliability measures, and interaction quality through standardized Human–Computer Interaction (HCI) usability metrics. Taken together, these mappings point to possible directions for creating Quality of Use (QoU) assessment instruments with empirical support in upcoming MenstruAI studies.

Although Quality of Use (QoU) is not empirically measured in the present study, it is introduced as a design- oriented construct intended to guide future evaluation of Responsible AI–driven menstrual health technologies. Each Quality of Use (QoU) dimension can be operationalized using a combination of short-form questionnaires and interaction-level metrics in subsequent empirical research. The purpose of this conceptualization is not to propose a finalized instrument, but to provide a structured blueprint for future validation.

Table 4

Operationalization of Quality of Use (QoU) in MenstruAI

3.8 Methodology Boundaries

Because of its conceptual design and lack of direct empirical data on diagnostic accuracy, bias-mitigation effects, or user-experience outcomes, this study recognizes methodological limitations. Nonetheless, feasibility is indirectly supported by proxy validation from current biomedical AI research. For instance, using machine-learning models, hemoglobin inference from national screening data has shown excellent predictive performance (Jung & Hwang, 2024). Research on wearable biosensing (Masuda et al., 2025) and edge-AI pipelines for smartphone-based LFIA interpretation (Colombo et al., 2023) provide more proof of viability. These results direct future empirical development of the MenstruAI framework and imply possible relevance to menstrual-blood biomarkers.

To clarify how each research question is operationally addressed across the manuscript, Table 5 summarizes the alignment between the guiding questions introduced in Figure 1 and the corresponding sections and contributions.

Table 5

Alignment between Research Questions and Manuscript Sections

4.1. Overview of the MenstruAI Framework

MenstruAI intentionally integrates ethical and technical concerns rather than treating them as separate design domains. Biosensing, edge-AI, and sustainability-driven governance were all meant to develop simultaneously rather than one layer at a time. That choice signified a turn away from developing the “best device” toward building health solutions that were accountable, transparent, and socially relevant over time.

4.2 Functional Interpretation of the Three-Layer Model

Menstrual blood's practicality was investigated in Layer 1 (Biosensing and Biomimetic Materials). The layer may provide a solid foundation for diagnostic interpretation. The previous studies had reported that biomarkers including CRP, CEA, and CA-125 can signal inflammatory or reproductive-health conditions with reasonable regularity. These results suggested that menstrual-blood diagnostics can be viewed as an extension of current biomarker research rather than as a conceptual leap into uncharted territory. These scientific underpinnings were combined with biomimetic materials and low-impact substrates in MenstruAI to improve diagnostic performance while reducing environmental load. This approach naturally matched SDG 12's goal for waste reduction and responsible resource usage.

Layer 2(Edge-AI Inference and Privacy-Preserving Analytics) considered how AI inference can be carried out in a way that people can realistically understand and trust. In this part of the framework, federated learning and lightweight neural networks were used so that the analytical workload stays closer to the device rather than being fully dependent on remote servers. The motivation behind this setup reflected long-standing worries in FemTech about uneven model performance and the handling of sensitive data. Therefore, privacy-by-design and fairness-aware modeling were incorporated from the start to avoid the computational and environmental overhead that centralized systems usually produce, as well as to foster user confidence.

Layer 3(Responsible Governance and ESG–SDG Alignment) focused on the governance dimension of the framework and how Responsible AI and ESG–SDG commitments could be translated into actual practice. The mechanisms in this layer included procedures for traceability, ongoing quality improvement, and environmental oversight, as well as clearer ways of recording how diagnostic decisions are formed across the system. When these components were considered together, MenstruAI became more than a diagnostic design. It began to operate as a governance model that aimed to support more inclusive and accountable development in women’s health technologies.

Figure 4

ESG–SDG Alignment Map for Responsible AI in FemTech

4.3. Comparative Advantage Over Existing FemTech Approaches

In earlier FemTech experiments, the limitations of MenstruAI were found, and the differences became obvious. As prior research has consistently shown, many menstrual health technologies prioritized algorithmic correctness or sensor-driven performance while giving contextual, ethical, or user-centered considerations significantly less attention (McMillan, 2022; Jacobs & Evers, 2023). To close these gaps, MenstruAI offered a more cohesive framework. Transparency, equity, and privacy-by-design were integrated into every point of the diagnostic workflow from the beginning; MenstruAI did not add ethics and governance after the technical components were finished.

The same reasoning applied to the ESG-SDG components: environmental impact, equitable access, and accountability were positioned as essential criteria rather than optional extras. In this sense, MenstruAI was a more all-encompassing and durable alternative to the technologically focused tactics that dominated the FemTech market at the time.

Viewing MenstruAI from this angle showed a different way of organising the diagnostic process. Biosensing, AI analysis, and governance interacted rather than existing in separate compartments. Because fairness, transparency, and privacy were built into the same processes where decisions were formed, these principles shaped the system’s behavior from the outset rather than being added afterwards. ESG–SDG themes reinforced this orientation by prompting designers to consider environmental cost, accessibility, and the long-term stewardship of biomaterial-based diagnostics. Together, these choices created a trajectory that diverged from many current FemTech products: while strong technical performance remained essential, it was paired with dependability, inclusivity, and broader formed of responsibility that determined whether menstrual-health systems could be trusted in real use.

4.4 QoU as a Bridge Between Responsible AI and User-Centered Design

Quality of Use (QoU) highlighted aspects of menstrual health technology that were not revealed by accuracy ratings alone. It shifted the focus to how the system felt from the viewpoint of the user, including whether the processes could be completed without undue physical or mental strain, whether the procedure was understandable, and whether the user felt safe and respected.

Within MenstruAI, these ideas were translated into practice. Because the analytical processes and decision points remained visible rather than concealed, users maintained a sense of autonomy. System recommendations were conveyed through user-friendly interface elements and explanations that did not require technical knowledge. Strong privacy protections and careful handling of reproductive data helped maintain psychological comfort, especially in settings where sharing such information could feel personally or socially risky. The stability and clarity of the diagnostic workflow also matter; when the procedure runs smoothly, users did not have to fight against the system to move through it.

MenstruAI expanded the standards for evaluating FemTech systems by incorporating these Quality of Use (QoU) components. Accuracy remained important, but it is weighed together with trust, usability, and emotional well-being — the factors that ultimately decided whether menstrual-health technologies could be used and depended on in real settings.

4.5 Implications of Proxy Validation

Although there was no clinical testing in this study, several lines of earlier biomedical research indirectly supported the framework's technical direction. Research utilizing extensive public health datasets has demonstrated that physiological biomarkers could be accurately predicted without the need for extremely intricate neural architectures. Research in non-invasive biosensing further suggested that biochemical states could sometimes be inferred from broader physiological signals rather than blood drew alone. In parallel, smartphone-based interpretation of lateral-flow assays — in many cases reporting AUC values above 0.85 — had demonstrated that point-of-care diagnostics could be enabled by lightweight computation carried out directly on the device. When combined, these separate results suggested that, at this stage of development, combining biosensing with edge-AI for menstrual blood analysis was both conceptually and technically feasible. Taken together, these independent findings implied that coupling biosensing with edge-AI for menstrual-blood analysis was not only conceptually plausible but technically achievable at the current stage of development.

4.6 Extended Discussion: Societal, Ethical, and Regulatory Implications

MenstruAI had implications that went beyond the technical blueprint. Equity was one of the first topics it raised. A system that operated outside of specialized clinical settings could mitigate some of the long-standing disparities in menstrual health diagnostics, which have historically lagged behind other areas of healthcare.

Regulatory implications also surfaced. Since tools in this area involved sensitive biological information, they sat at the intersection of evolving AI-health policies and data-protection rules — and those rules were far from uniform across regions. Techniques that kept most calculations on the user's device rather than in centralized storage could not only lessen reliance on external servers but also increase users' sense of control over their data.

When the diagnostic materials themselves were constructed of low-impact or biodegradable materials, environmental issues developed in a different way. Those design decisions first appeared to be minor matters that had no influence on anything beyond the lab. But once they were used in real restrooms, clinics, offices, and daily packs, tools ceased to be little. They joined in on the discussion.

MenstruAI was more than simply a technical process; it was a method of building FemTech that took privacy, legislation, justice, and environmental responsibility into account from the start rather than adding them afterward.

A biosensing sanitary pad combined with smartphone-based analysis is one way that MenstruAI can be used in a home-based menstrual health screening setting. In order to reduce the exposure of sensitive data, the system uses on-device inference and gives users directly interpretable diagnostic feedback. Feedback pertaining to Quality of Use (QoU) is gathered in order to evaluate user understanding, autonomy, and psychological comfort. A Quality 4.0 monitoring framework is used to aggregate these signals, and governance mechanisms in line with ESG-SDG principles are used to review them on a regular basis. This example shows how the suggested framework can facilitate ethical and user-focused FemTech applications without depending on centralized data infrastructures.

4.7 Practical and Policy-Related Limitations

MenstruAI identified a clear conceptual direction, but implementation may take longer. It was also more complicated than the framework. The wider availability of digital health tools contributed to some challenges. AI-driven diagnostics regulations were still evolving, so what was needed in one area may not be in another. Healthcare systems' adoption of new data workflows and digital tools varied. ESG-SDG priorities did not yet influence daily decisions in many places. When this happens, the framework's presumptions may predate modern practice. This reflected the field's state rather than MenstruAI's flaws. Empirical research is needed to assess the framework's performance beyond conceptualization. Creating a menstrual-blood dataset was the next step to build diagnostic models and test robustness and fairness under real user variability. On-device performance was also assessed to determine if reliability could be maintained without centralized pipelines because the system handled sensitive data. User experience studies were needed to determine how people interpreted system explanations, their autonomy, and how emotionally safe the diagnostic process was. Environmental factors were important, especially when biosensing materials and workflows were designed for sustainability. These directions outlined a path to understanding whether MenstruAI worked responsibly, equitably, and in legitimate clinical and personal settings.

The system's footprint can be determined by examining materials, workflow, and energy use. It made it possible to assess the environmental impact more accurately. Another unanswered question was how all of this fits into routine clinical practice. Any new diagnostic technique had to fit into the established routines that clinics had developed over many years, not interfere with them. Cultural norms and people's attitudes toward using digital tools to share menstrual or reproductive information would also influence acceptance. It could vary greatly between groups, depending on the circumstances and individuals involved. If the framework was to be developed beyond a conceptual proposal, it was crucial to comprehend these responses.

If work in these areas progressed, the framework could gradually take on a shape that reflected not only its technical aims but also the practical and social realities of menstrual-health care. That process would probably happen piece by piece rather than all at once, which is typical for technologies entering sensitive areas of health.