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A Structured Review of Emerging Prompt Instability Indices (PIIs) to Evaluate the Reproducibility of Prompt-Driven
Adib Shafipour1, Deevakar Rogith1, Zulfiia Ditto1
1The University of Texas Health Science Center at Houston, Houston, Texas, United States.
Summary
Prompt-aware image segmentation offers flexibility but faces reproducibility challenges. This study reviews Prompt Instability Indices (PIIs) to measure and ensure the reliability of these interactive segmentation models.
Area of Science:
- Computer Vision
- Medical Imaging
- Artificial Intelligence
Background:
- Prompt-aware image segmentation models allow user cues (points, boxes, text) for object delineation.
- Altering prompts can lead to significantly different segmentation outputs, impacting reliability.
- This variability raises concerns for scientific validity in research and clinical settings.
Purpose of the Study:
- To conduct a structured review of emerging Prompt Instability Indices (PIIs).
- To assess the reproducibility and reliability of prompt-driven image segmentation models.
- To propose a taxonomy for PII methods and their application.
Main Methods:
- Systematic literature search on PubMed and arXiv (2020-2025).
- Analysis of identified studies focusing on PII definitions and operationalization of prompt stability.
- Categorization of PII methods based on their characteristics.
Main Results:
- Identified and reviewed various Prompt Instability Indices (PIIs).
- Highlighted the advantages, limitations, and interpretive value of different PII approaches.
- Suggested a preliminary taxonomy for PII methods.
Conclusions:
- Prompt Instability Indices (PIIs) are crucial for evaluating segmentation model reproducibility.
- Integrating PIIs with accuracy metrics enhances performance assessment transparency.
- Consistent and trustworthy evaluation is essential for next-generation interactive segmentation systems.
