A Structured Review of Emerging Prompt Instability Indices (PIIs) to Evaluate the Reproducibility of Prompt-Driven...

Prompt-aware image segmentation has introduced new flexibility by allowing models to incorporate user-provided cues, such as points, boxes, or text prompts, to guide object delineation across diverse imaging domains. This flexibility introduces a fundamental challenge: ensuring segmentation results remain reproducible when prompts are altered. Minor variations in prompts produce substantially different outputs, raising concerns about the reliability and scientific validity of research and clinical applications. This paper conducts a structured review of emerging Prompt Instability Indices (PIIs), quantitative measures developed to assess the reproducibility of prompt-driven segmentation models. A systematic search of PubMed and arXiv (2020-2025) identified relevant studies, analyzed based on the definition, and operationalized prompt stability. This preliminary study suggests a taxonomy of PII methods, highlighting their respective advantages, limitations, and interpretive value. Integrating reproducibility metrics such as PIIs alongside traditional accuracy measures is essential for establishing consistent, transparent, and trustworthy performance assessments in next-generation interactive segmentation systems.