  This study presents an innovative approach to the application of large
language models (LLMs) in clinical decision-making, focusing on OpenAI's
ChatGPT. Our approach introduces the use of contextual prompts-strategically
designed to include task description, feature description, and crucially,
integration of domain knowledge-for high-quality binary classification tasks
even in data-scarce scenarios. The novelty of our work lies in the utilization
of domain knowledge, obtained from high-performing interpretable ML models, and
its seamless incorporation into prompt design. By viewing these ML models as
medical experts, we extract key insights on feature importance to aid in
decision-making processes. This interplay of domain knowledge and AI holds
significant promise in creating a more insightful diagnostic tool.
  Additionally, our research explores the dynamics of zero-shot and few-shot
prompt learning based on LLMs. By comparing the performance of OpenAI's ChatGPT
with traditional supervised ML models in different data conditions, we aim to
provide insights into the effectiveness of prompt engineering strategies under
varied data availability. In essence, this paper bridges the gap between AI and
healthcare, proposing a novel methodology for LLMs application in clinical
decision support systems. It highlights the transformative potential of
effective prompt design, domain knowledge integration, and flexible learning
approaches in enhancing automated decision-making.
