Accurate diagnosis in medical imaging depends heavily on image quality, often degraded by illumination artifacts such as overexposure, underexposure, and specular reflections. This paper presents a novel prompt-assisted enhancement system for attenuating such artifacts in endoscopic imagery. Leveraging a BERT-based model’s semantic capabilities, our system interprets user prompts to dynamically select and apply targeted enhancement techniques. Unlike general-purpose prompt-based editors like InstructIR or InstructPix2Pix, our method is tailored to the spatially varying, clinically critical distortions specific to endoscopy. By enabling localized correction of under- and over-exposed regions, our system improves downstream tasks such as 3D colon surface reconstruction. We show that this reprocessing enhances deep learning-based SLAM performance, yielding clearer visualizations and improved diagnostic accuracy. Furthermore, by integrating natural language prompts into the imaging pipeline, our system enables interactive, clinician-driven enhancements—potentially via voice commands—during live procedures. This introduces a new paradigm in human-AI collaboration for surgery and establishes a foundation for real-time, user-centered AI in clinical endoscopy.