Spina Bifida (SB) is a complex neural tube defect that presents multifaceted healthcare challenges requiring multidisciplinary management. While advances in foundation models (FMs), offer promising avenues for enhancing SB care through intelligent, context-aware support, existing models struggle to accurately identify and reason about SB's diverse symptoms. This study benchmarks eight widely used large language models (LLMs) through qualitative and quantitative evaluations, focusing on their ability to address the unique medical challenges of SB. We introduce an \textit{inverse prompting} technique designed to guide LLMs through a step-wise diagnostic process by incorporating a predefined symptom set relevant to SB, thereby preventing premature conclusions and improving diagnostic reasoning. Our evaluations reveal significant limitations in the LLMs' abilities to accurately diagnose SB-related conditions, underscoring the need for specialized approaches. Building on these findings, we propose a novel framework that integrates a structured, symptom-based knowledge base specific to SB, enhancing the models' contextual understanding and reasoning capabilities. This work highlights the potential of tailored AI solutions in improving access to care for individuals with SB, particularly in populations where gaps in knowledgeable providers persist. By addressing the shortcomings of general-purpose LLMs, our suggested framework aims to streamline SB care and improve patient outcomes, paving the way for more effective AI-assisted healthcare interventions in complex chronic conditions.