Hallucinated medical advice threatens trust in AI-assisted breastfeeding support

AI-assisted breastfeeding services are stepping in to fill critical support gaps across rural communities. With fewer specialists available and limited postpartum follow-up, many mothers now depend on AI chatbots for guidance during some of the most vulnerable weeks after childbirth. The shift represents a major transformation in maternal care delivery.

In a study titled Building Safe AI Chatbots for Rural Mothers Seeking Breastfeeding Support: Understanding Hallucinations and How to Mitigate Them, published in Social Sciences, researchers state that the safety of AI-assisted breastfeeding depends on robust governance, domain-specific training, and layered technical safeguards to prevent harmful hallucinations

Hallucination risks in high-stakes maternal health

Large language models (LLMs) generate responses by predicting words based on statistical patterns rather than verifying factual accuracy. This probabilistic architecture creates the risk of hallucination, where systems produce inaccurate, fabricated, or overconfident statements. In the context of breastfeeding support, such errors are not merely informational inaccuracies. They may directly influence infant feeding practices and maternal health outcomes.

The study identifies four major categories of AI failure that pose distinct risks in breastfeeding support chatbots.

The first involves false citations and fabricated evidence. Chatbots may confidently attribute advice to authoritative bodies, including pediatric or obstetric organizations, without any corresponding documentation. Such artificial legitimacy can mislead mothers into adopting feeding practices that lack clinical support. The authors argue that hallucinated references create a particularly dangerous illusion of credibility in resource-constrained settings where users may not have easy access to independent verification.

The second category concerns transcription errors in telehealth environments. Rural mothers increasingly rely on virtual consultations, and automated speech recognition tools are often used to generate summaries of medical discussions. These systems can introduce words that were never spoken, especially when audio quality is compromised or accents differ from training data. Even minor transcription distortions can alter the interpretation of breastfeeding practices, potentially affecting clinical decisions or follow-up care.

The third risk arises from prompt injection and jailbreaking. Even chatbots intentionally designed to support breastfeeding can be manipulated into bypassing safety restrictions. Users may inadvertently or deliberately introduce hidden instructions that override system safeguards. In high-risk health domains, such vulnerabilities can expose families to unsafe medical advice and erode trust in digital tools.

The fourth category involves incorrect generalization and false personalization. Generative models often default to dominant patterns in their training data, leading to overgeneralized recommendations. For example, nipple pain may be attributed solely to poor latch technique while overlooking less common causes such as infection or vascular issues. Similarly, chatbots may recommend formula supplementation prematurely, even when mothers have not expressed such intentions. These subtle shifts in guidance can influence breastfeeding duration, maternal confidence, and infant nutrition.

Governance and design as safety imperatives

The authors point out that hallucination should not be treated as a singular coding flaw but as a systems-level risk shaped by design choices, data governance, and deployment context. Preventing harm requires layered safeguards embedded throughout the AI lifecycle.

One proposed mitigation strategy is retrieval-augmented generation. In this approach, chatbot responses are grounded in pre-approved, authoritative breastfeeding resources rather than relying solely on general training data. By constraining the information pool to vetted clinical sources, the risk of fabricated citations and unsupported claims can be reduced. However, the effectiveness of this method depends on rigorous data structuring and validation practices.

The study also highlights the importance of treating data as a product. Cleaned, validated, and machine-readable datasets improve reliability and reduce noise in model outputs. In rural health settings, where community-based organizations may lack extensive technical infrastructure, structured data governance becomes a crucial foundation for safe AI deployment.

To address transcription risks, the authors recommend stress-testing speech recognition systems with diverse accents and audio conditions. Secondary models can be deployed to flag uncertain segments for review, and high-stakes interactions should involve human oversight. Encouraging mothers to review visit summaries and confirm key details verbally adds an additional safeguard.

Guardrails represent another central mitigation layer. Retrieval guardrails restrict the chatbot's access to trusted repositories, while generation guardrails constrain outputs to remain aligned with breastfeeding safety standards. Off-topic or speculative medical advice can be rejected or redirected toward professional care. Continuous monitoring and adversarial testing are recommended to identify vulnerabilities before widespread use.

Fine-tuning models on domain-specific breastfeeding content is also critical. Training systems to recognize decision thresholds and diverse clinical presentations can reduce overgeneralization. Uncertainty-aware messaging further strengthens safety by avoiding categorical language and explicitly encouraging professional evaluation when symptoms warrant clinical attention.

Rural context and digital literacy gaps

The paper also sheds light on the broader social determinants that shape AI use in rural communities. Geographic isolation, limited broadband infrastructure, and reduced access to postpartum follow-up care compound the stakes of AI misinformation. When in-person support is distant, digital tools may become the primary source of guidance during urgent breastfeeding challenges.

Digital health literacy emerges as a key variable. Mothers with limited experience evaluating online health information may interpret fluent, confident chatbot responses as authoritative. The human-like tone of generative systems can amplify anthropomorphic tendencies, leading users to attribute expertise or intent to the AI. In high-stress postpartum contexts, such perceptions may increase reliance on flawed guidance.

The authors argue that AI literacy should be integrated into broader maternal health initiatives. Mothers need tools to critically evaluate chatbot outputs, understand uncertainty signals, and recognize when professional intervention is necessary. Without user-centered education, even technically robust systems may fail to mitigate risk.

The study also calls for structured evaluation before deployment. Expert lactation consultants and maternal-child health specialists should assess chatbot outputs using standardized rubrics. Dedicated test datasets of realistic breastfeeding scenarios can measure whether responses are clinically appropriate rather than merely fluent. Red teaming exercises, in which experts attempt to break the system or bypass safeguards, are recommended to uncover vulnerabilities.

Automated evaluation tools may assist in scaling oversight, but the authors caution that expert review remains essential for high-stakes maternal health decisions. In addition, user perception research is needed to understand how tone, certainty, and perceived personality influence reliance on AI systems.