Minimizing Hallucinations & Maximizing Value with AI Tools
Fertility networks and clinics are increasingly integrating artificial intelligence into their operations, but industry leaders are raising concerns over the reliability of many available solutions. A key issue is AI “hallucination” – a phenomenon where models generate inaccurate or entirely false information.
The Data Gap: 97% of Healthcare Information Remains Unstructured
Healthcare providers generate up to 50 petabytes of data per year, with fertility-specific data forming a critical subset. Yet, 97% of healthcare data remains unstructured and largely untapped. Many AI solutions rely on large language models (LLMs) trained on publicly available data, which is often outdated, unverified, or irrelevant to reproductive endocrinology. Without access to real-world data (RWD) specific to fertility, these models frequently produce misleading outputs, raising concerns about their clinical utility and financial impact.
AI hallucination in any healthcare setting could be the difference between life and death. While AI presents opportunities for improving predictive analytics, enhancing clinical decision-making, and reducing inefficiencies, the risk of error remains a significant barrier to adoption.
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Leverage your own data for your AI applications
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Build agents & tools on top of your data
Automate tedious data & compliance tasks
Personalize medicine
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AI Hallucinations Undermine Reliability in Fertility Medicine
A study from Mendel and UMass Amherst highlights the prevalence of AI hallucinations in summarizing medical records, emphasizing the need for robust detection mechanisms. Similarly, OpenAI’s Whisper, an AI-driven transcription tool, has been flagged by software engineers and researchers for fabricating entire sentences. These examples reinforce the challenge fertility clinics face in ensuring AI-generated insights are both accurate and actionable.
The fertility sector has unique operational challenges, from optimizing laboratory workflows to managing patient conversions. Network executives, including CFOs and COOs, are evaluating AI tools for their potential to enhance both clinical and commercial performance. However, the widespread issue of hallucinations—driven by models that prioritize pattern recognition over verified retrieval—makes most AI offerings unsuitable for high-stakes medical environments. This is beginning to change, as AI companies like Cercle utilize an innovative method that draws on and retrieves from a vast repository of real-world-data (RWD)."
The Costs of Building AI In-House Isn’t the Only Barrier
Fertility practices seeking to implement AI must address three critical challenges:
Data Reliability – Most AI models lack direct integration with provider data, limiting their ability to produce precise and context-specific insights.
Hallucination Risk – Models trained on generic datasets are prone to inaccuracies, undermining trust in AI-generated recommendations.
Customization for Fertility Care – Many AI solutions are developed for general healthcare applications, rather than tailored for the complexities of reproductive medicine.
While some networks have attempted to build AI applications internally, these efforts often come with high capital expenditure, limited data access, and significant opportunity costs. In contrast, a growing number of fertility-focused AI providers are leveraging real-world data and advanced database architectures to mitigate these issues.
Methods That Allow AI To Shine, Minimizing Hallucinations
One such advanced database architecture approach is the use of graph databases, which allow AI systems to recognize complex relationships between diverse data points, such as hormonal levels, embryonic development, and patient demographics. Unlike traditional relational databases, graph-based models can more accurately reflect the interconnected nature of fertility treatment factors, reducing the risk of hallucination and improving predictive capabilities.
As AI adoption accelerates, industry leaders are urging providers to select solutions that prioritize retrieval-augmented generation (RAG) - a method that supplements AI responses with verified, real-time data sources. Fertility clinics that fail to scrutinize their AI technology risk implementing systems that produce unreliable insights, ultimately compromising physician decisions on care and financial performance.
Cercle AI is among the few companies addressing these concerns by integrating RWD-driven AI into fertility networks. Having assembled the world's largest fertility data repository and leveraging graph database architecture, Cercle is able to minimize hallucinations while delivering predictive analytics that personalize care. As the fertility sector navigates AI adoption, solutions that combine information retrieval with graph databases will be best positioned to meet the industry's evolving needs.
Protect Clinic Finances, Patient Outcomes
See Why US Fertility Uses Cercle. Book Demo.
Leverage your own data for your AI applications
Consolidate your AI vendors into one
Build agents & tools on top of your data
Automate tedious data & compliance tasks
Personalize medicine
See how US Fertility and others utilize Cercle’s AI platform to revolutionize their business insights.