Text-to-Video Generative Artificial Intelligence in Pediatrics: Potential Applications, Ethical Considerations, and...

Area of Science:

• Artificial Intelligence in Medicine
• Medical Visualization
• Pediatric Healthcare Technology

Background:

• Text-to-video (T2V) generative AI models create videos from text prompts, offering novel applications in medical visualization and communication.
• While visual aids are established in pediatrics for education and distress reduction, T2V systems lack clinical validation for routine care.

Purpose of the Study:

• To explore potential applications of T2V generative AI in pediatric education, patient communication, and simulation.
• To differentiate current evidence from hypothetical uses of T2V AI in pediatrics.

Main Methods:

• A narrative review of literature on generative AI, T2V models in healthcare, and pediatric video-based education was conducted.
• Searches included PubMed and Google Scholar up to June 2025, using keywords like "pediatric," "video-based education," "procedural preparation," "anxiety," "pain," and "simulation."

Main Results:

• Potential uses include clinician training videos, family educational animations, and anxiety-reducing videos for children.
• Identified risks include inaccurate content, bias, privacy issues, and inequitable access.
• Essential pediatric safeguards involve disclosure, clinician review, age-appropriate design, consent, and equity planning.

Conclusions:

• T2V generative AI should be considered an experimental tool, not an immediate clinical solution for pediatrics.
• Rigorous validation, governance, and equity-focused strategies are necessary before widespread pediatric implementation.