  Large Language Models (LLMs) have demonstrated immense potential in
artificial intelligence across various domains, including healthcare. However,
their efficacy is hindered by the need for high-quality labeled data, which is
often expensive and time-consuming to create, particularly in low-resource
domains like healthcare. To address these challenges, we propose a
crowdsourcing (CS) framework enriched with quality control measures at the
pre-, real-time-, and post-data gathering stages. Our study evaluated the
effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT)
for predicting autism-related symptoms. The results show that real-time quality
control improves data quality by 19 percent compared to pre-quality control.
Fine-tuning Bio-BERT using crowdsourced data generally increased recall
compared to the Bio-BERT baseline but lowered precision. Our findings
highlighted the potential of crowdsourcing and quality control in
resource-constrained environments and offered insights into optimizing
healthcare LLMs for informed decision-making and improved patient care.
