DepthPilot: Taking Colonoscopy Video Generation from Controllable to Interpretable

The Interpretability Challenge in Medical Video Generation

In recent years, controllable medical video generation has made remarkable progress, with AI now capable of producing realistic medical imaging videos based on conditional inputs. However, a critical bottleneck has consistently hindered the clinical adoption of this technology — the lack of interpretability. Can generated content remain consistent with physical priors? Can it faithfully reflect real clinical manifestations? These questions directly impact the credibility and practical value of AI-generated medical imaging.

A latest study published on arXiv (arXiv:2604.26232) introduces an innovative framework called DepthPilot, which for the first time elevates colonoscopy video generation from "controllable" to "interpretable," marking a significant step toward trustworthy medical video generation.

DepthPilot: A Depth-Guided Interpretable Generation Framework

The core idea behind DepthPilot is to use depth information as a bridge connecting generative models to the physical world, imposing interpretability constraints on the colonoscopy video generation process.

Specifically, the framework features the following key characteristics:

• Depth Information-Driven: Depth maps are used as guiding signals throughout the generation process, ensuring that the generated colonoscopy videos conform to the three-dimensional geometric properties of real intestinal structures. Depth information provides explicit physical priors, transforming the generation output from a "black box" result into one with traceable physical grounding.

• Paradigm Shift from Controllable to Interpretable: Traditional controllable generation methods focus on "what can be generated," whereas DepthPilot emphasizes "why it is generated this way." By explicitly aligning generated content with depth information and clinical manifestations, the researchers endow the model's output with interpretable semantics.

• Synergistic Design Mechanism: The research team designed two synergistic modules that enhance interpretability while maintaining generation quality, achieving a balance between output quality and trustworthiness.

Why the Colonoscopy Scenario Matters

Colonoscopy is the gold standard for colorectal cancer screening, yet acquiring high-quality colonoscopy data faces numerous challenges: stringent patient privacy requirements, high annotation costs, and imbalanced data distributions are persistent issues. AI video generation technology holds promise for alleviating these bottlenecks by synthesizing high-quality training data — but only if the generated content is medically "trustworthy."

This is precisely why interpretability holds special importance in medical image generation. Clinicians need to understand and verify the plausibility of generated content rather than blindly trusting AI outputs. DepthPilot uses depth information as an "anchor," making the generation process transparent and auditable, providing the technical foundation for building trust in clinical settings.

Technical Significance and Industry Impact

From an academic perspective, DepthPilot fills a gap in the interpretability dimension of medical video generation. Previous research has largely focused on improving generation quality and diversity while overlooking the interpretability of both the generation process and its results. This study offers a new evaluation dimension and research paradigm for future work.

From an application standpoint, the significance of this framework extends well beyond colonoscopy. Its "depth-guided + interpretability constraint" approach has the potential to transfer to other endoscopic examinations (such as gastroscopy and bronchoscopy) and even broader medical image generation tasks, driving the entire medical AI generation field toward trustworthiness.

Outlook: Trustworthy Generation Is the Inevitable Path for Medical AI

As generative AI continues to penetrate the healthcare sector, "being able to generate" is no longer the core challenge — "generating trustworthily" is the key to determining whether the technology can truly be deployed in practice. The introduction of DepthPilot signals that the research community is beginning to confront this need head-on, incorporating interpretability as a core design objective in medical video generation.

Looking ahead, how to deeply integrate interpretability with larger-scale generative models (such as video diffusion models) and how to establish clinically oriented interpretability evaluation standards are important directions worthy of sustained attention. From controllable to interpretable, medical video generation is moving toward genuine clinical trustworthiness.