A progressive prompting framework on 3D SAM with text, dose-box, and click prompts plus small-target loss achieves reliable multi-task segmentation of osteoradionecrosis, cerebral edema, and cerebral radiation necrosis on a new limited-data dataset and outperforms prior methods.
Visual prompt engineering for medical vision language models in radiology
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PROMPT is a pre-registered randomized protocol using component dismantling to identify active, inactive, harmful, and task-dependent effects in clinical AI prompts, shown in synthetic orientation and mammogram tasks.
Textual prompts override visual cues in a medical MLLM, reducing accuracy from 75% to 46% on a hemorrhage versus drusen task even when visual grounding is present.
citing papers explorer
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A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings
A progressive prompting framework on 3D SAM with text, dose-box, and click prompts plus small-target loss achieves reliable multi-task segmentation of osteoradionecrosis, cerebral edema, and cerebral radiation necrosis on a new limited-data dataset and outperforms prior methods.
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PROMPT: A Pre-registered Randomized Protocol for Component-Level Evaluation of Clinical AI Prompts
PROMPT is a pre-registered randomized protocol using component dismantling to identify active, inactive, harmful, and task-dependent effects in clinical AI prompts, shown in synthetic orientation and mammogram tasks.
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When Prompts Mislead: Textual Dominance and Diagnostic Bias in MLLMs
Textual prompts override visual cues in a medical MLLM, reducing accuracy from 75% to 46% on a hemorrhage versus drusen task even when visual grounding is present.