The role of AI in FemTech

Key takeaways

AI is transforming FemTech, but with innovation comes responsibility. Medical device regulations are evolving; data protection and cybersecurity obligations are intensifying, and ethically, companies must address bias, invest in explainability, preserve user autonomy, and ensure equitable access to ensure growth and to protect their revenue and reputation. Regulatory divergence is a significant practical challenge, but the organisations that navigate this challenge successfully will be those that treat compliance and ethics not as constraints, but as foundations for building trust with the women their products serve.

 

Introduction

The FemTech market is evolving rapidly, driven by increasing investment, rising consumer demand, and growing recognition that advanced technology can significantly improve the quality and accessibility of women's healthcare.

AI is a major catalyst behind this acceleration. AI-enabled products are moving beyond basic tracking into predictive and personalised health insights tools that are more accurate, more responsive, and increasingly capable of assisting both users and clinicians.

What began with simple digital tools has expanded into a sophisticated ecosystem with embedded AI, spanning diagnostics, therapeutics, precision health, and integrated care pathways.

Powerful examples already exist.

Machine learning models can forecast ovulation with precision surpassing traditional calendar‑based methods. AI‑enabled platforms assess contraction patterns and distinguish between false and established labour. Pattern recognition algorithms applied to imaging data offer earlier detection of endometriosis. AI‑driven genomic platforms help identify mutations linked to breast and ovarian cancers.

But as AI becomes embedded in FemTech, it introduces legal, regulatory, and ethical questions that companies must navigate carefully. This article explores these challenges and what they mean for organisations operating in the FemTech market from a UK and, where applicable, EU perspective.

 

Legal and regulatory landscape

FemTech companies must navigate a complex web of laws, regulations and standards governing product design, development and deployment. Some are mandatory; others are best practice. Together, they shape requirements around safety, performance, data handling and algorithmic integrity.

A. Medical device regulation

In Great Britain, the Medical Devices Regulations 2002 establishes baseline requirements for placing medical devices on the market.

The regime is undergoing reform to address emerging technologies, including AI. The MHRA’s Software and AI as a Medical Device Change Programme will introduce strengthened rules on qualification, classification and clinical evidence for AI‑enabled software, with greater emphasis on managing adaptive algorithms, improving transparency, and ensuring human interpretability.

For FemTech innovators, this means clearer regulatory expectations but increased obligations around data quality, bias mitigation, and post‑market surveillance.

Industry codes also matter. The ABHI Code emphasises that transparency and accurate communication is critical for conveying the capabilities and limitations of complex algorithmic systems.

Its focus on ethical interactions with healthcare professionals and maintaining patient trust is particularly relevant for AI tools that directly influence clinical decision-making.

• Action point: Monitor MHRA guidance on Software and AI as a Medical Device closely. The guidance clarifies how AI-driven software qualifies as a medical device and sets expectations around intended purpose, risk classification, and lifecycle oversight.

 B. Data protection and cybersecurity

The UK has no single AI specific statute. Instead, AI systems are regulated through existing legislation (see AI Laws of the World). From a data protection and cybersecurity perspective, this is primarily:

• the UK GDPR, Data Protection Act 2018
• the Data (Use and Access) Act 2025
• the Product Security and Telecommunications Infrastructure Act 2022
• the anticipated Cyber Security and Resilience Bill

These frameworks are not AI-specific, but they reinforce the expectation that organisations deploying innovative technologies in sensitive sectors, including health, take proportionate and well governed steps to manage cybersecurity risk throughout the lifecycle of their products.

The ICO's January 2026 Tech Futures report flagged key considerations for FemTech:

1. Controllers deploying AI agents retain full legal responsibility for data processing, regardless of system autonomy. Autonomous decision-making capabilities is a distinct risk domain.
2. AI errors in sensitive health contexts can cascade into harm. A misinterpretation of cycle data could lead to incorrect diagnoses and unreliable advice. Controllers must ensure stricter Article 9 UK GDPR compliance where agents may infer special category data, noting the intimate nature of FemTech fertility patterns, pregnancy histories, menstrual cycles etc.
3. Purpose creep is a real risk if agent goals are set too broadly. Conduct data protection impact assessments early to embed privacy by design.

• Action Point: Following the US Supreme Court's Dobbs decision in 2022, reproductive health data has been sought in law enforcement investigations in US states restricting abortion access. While UK and EU data protection law provides significant protections, FemTech companies that transfer data to US-based processors should consider the implications and ensure compliance with existing data transfer requirements. Apply data minimisation rigorously – collect only what is necessary and be transparent with users about all circumstances in which data might be disclosed. For more details, see Privacy and Data Security in FemTech.

The integration of agentic AI into FemTech presents two particularly acute security considerations.

1. There is a shift in how these technologies operate. Rather than simply recording, analysing, and presenting data, agentic systems may anticipate user needs and initiate actions autonomously.
2. These systems increasingly operate within a wider ecosystem, interfacing with clinical infrastructure, cloud services, application programming interfaces, and third-party services, extending their operational footprint beyond a single application or dataset.

This combination of greater autonomy and expanded connectivity creates an elevated threat landscape. Femtech systems often process deeply personal and sensitive health data, including fertility information, pregnancy histories, hormonal profiles, and behavioural insight, making them particularly attractive targets for malicious actors.

The potential attack surface is correspondingly broader. For example, an attacker may seek to manipulate inputs into an AI-enabled health assistant to influence outputs, trigger unintended actions, or extract sensitive information at scale.

Over time, malicious data introduced into an AI systems context or memory may also affect the reliability and safety of its outputs in ways that are difficult to detect immediately.

• Action point: Apply state-of-the-art security principles. Addressing these challenges requires security architectures that are appropriate for AI-enabled and, in some cases, autonomous systems. Principles such as least privilege, robust authentication, and comprehensive logging take on heightened importance where systems are capable of initiating actions or interacting with other services without direct human intervention.
  
  Maintaining clear audit trails of system behaviour can also be critical for incident investigation, regulatory engagement, and maintaining trust with users and partners. The NCSC's voluntary guidance on secure AI development is increasingly treated by regulators as a benchmark for reasonable practice in healthcare. Treat it as essential reading, not optional. We also recommend that FemTech companies monitor DSIT publications and emerging voluntary standards, because the Department for Science, Innovation and Technology leads the UK's AI policy agenda and its guidance is increasingly relevant to health sector AI.

 C. The EU AI Act

The EU AI Act is the world's first comprehensive AI-specific legislation. It came into force in August 2024, with most obligations scheduled to be fully applicable from August 2026. The EU AI Act takes a risk-based approach, imposing different obligations depending on the risk level an AI system presents.

The EU AI Act categorises AI systems into four risk tiers:

1. unacceptable (prohibited)
2. high risk
3. limited risk
4. minimal risk

For FemTech developers, high-risk classification has critical consequences. AI systems intended as medical devices, or as safety components of such devices, are listed as high-risk, triggering obligations including on: risk management systems; data governance for training datasets; detailed technical documentation; human oversight mechanisms; accuracy and cybersecurity requirements; and conformity assessments before market placement.

The EU AI Act prohibits AI systems that exploit vulnerabilities of specific groups due to age, disability, or social or economic situation. FemTech products serving individuals navigating fertility challenges, pregnancy complications or oncological diagnoses should assess whether any system element could be characterised as exploiting user vulnerability.

If your FemTech product incorporates a general-purpose AI model (such as a large language model for personalised health insights), additional obligations apply (see European Union publishes its General-Purpose AI Code of Practice).

Providers must maintain technical documentation, comply with EU copyright law, and publish training data summaries. For models with systemic risk, adversarial testing and incident reporting are required. FemTech companies integrating third-party models should ensure contracts clearly allocate compliance obligations.

The EU AI Act has broad territorial reach. UK/US based FemTech companies whose products are used by consumers or clinicians in EU Member States will be subject to its requirements – even without any EU establishment. If output reaches EU users, you must comply.

By contrast, the UK has adopted a sector-led framework for AI regulation.

Rather than enacting a single AI-specific statute, the UK government has set out a principles-based framework that assigns responsibility for AI governance to existing sector regulators.

For FemTech companies, this means the MHRA and the ICO remain the primary regulatory bodies with oversight of AI in the health context.

Perhaps the most significant practical challenge for FemTech companies operating across both the UK and EU markets is regulatory divergence. Companies should build compliance programmes sufficiently flexible to accommodate both regimes.

Action point: FemTech providers should map AI systems against the EU AI Act risk categories now. If any qualify as high-risk (eg AI intended as a medical device), begin building compliance infrastructure. For products incorporating general-purpose AI models, ensure contractual arrangements with third-party providers clearly allocate compliance responsibilities. Investors should ensure they are asking what preparedness steps FemTech providers have taken if their product is marketed to, or used in, the EU.

 

Ethical considerations

Compliance alone is not enough. As AI capabilities expand, so do the ethical questions they raise – particularly in a sector that has historically faced gaps in research and representation. Below is our view on the top five issues:

1. Machine bias

Bias is not abstract. The NHS's experience with pulse oximeters, where racial bias produced inaccurate readings for patients with darker skin, shows how technology can perpetuate inequities if bias is not actively addressed.

FemTech faces similar risks. Women, particularly those from minority ethnic backgrounds, have historically been underrepresented in clinical research and health datasets. AI models trained on incomplete data will produce predictions that work well for some groups, but fail for others.

Models interpreting reproductive hormone patterns, analysing pelvic pain, or detecting breast imaging anomalies are only as accurate as the diversity of their underlying datasets.

Bias can also arise from device design. Wearables collecting physiological signals may not perform consistently across different skin tones, ages, or body types, leading to incorrect predictions or inaccurate health alerts.

FemTech companies should address machine bias deliberately through representative datasets, inclusive design and testing, transparent development processes, and continuous post‑market monitoring.

2. Explainability

Many AI systems operate as black boxes, producing outputs without explanation. This raises significant concerns in health contexts, where women and clinicians need to understand the basis for predictions about fertility, pregnancy or cancer risk.

The challenge is acute with deep learning models: their ability to identify subtle patterns is precisely what makes their reasoning difficult to interpret.

FemTech developers should invest in explainability– through interpretable architectures, plain language explanations, and clear communication of confidence levels and limitations. This is not merely regulatory box-ticking; it is fundamental to trust.

3. Informed consent and autonomy

Where AI influences health recommendations, are users giving meaningful informed consent? Standard privacy notices rarely convey how intimate menstrual patterns, hormonal profiles, reproductive history is used and what inferences are drawn.

Developers should design consent mechanisms that are genuinely informative rather than perfunctory, and that preserve user autonomy: AI tools should support decision-making, not nudge users toward particular conclusions.

4. Accountability and human oversight

When AI produces incorrect outputs – a missed diagnostic signal, misleading fertility prediction, or inappropriate advice – responsibility can be difficult to attribute between developer, deploying organisation, and the clinician or user who acted on it.

Beyond legal liability, organisations have an ethical obligation to ensure humans remain genuinely in the loop, particularly where AI influences clinical decisions or behaviour relating to serious health conditions. This means designing for meaningful oversight – not nominal oversight where clinicians defer automatically to AI outputs.

5. Health equity and access

AI-powered FemTech could democratise access to high-quality personalised health insights – but only if tools are genuinely accessible. There is a real risk that sophisticated AI health tools become the preserve of wealthier, more digitally literate users, entrenching health inequalities. Accessibility and affordability should be ethical imperatives from the outset of design, not afterthoughts.

 

This article is part of the FemTech Now series. Access the hub here.