Advancing pediatric care through OpenAI’s Sora
Bibliographic record
Abstract
For children, therapeutic play-structured activities tailored to age, cognitive abilities, and health-alleviates health-related anxiety and improves outcomes. This study introduces Sora, a text-to-video artificial intelligence (AI) software, as a novel tool to enhance therapeutic play. Using Sora, we generated videos tailored to children's interests. The personalized videos were designed to foster emotional well-being and provide education for pediatric patients. We demonstrate Sora's versatility, as its videos can be tweaked to match a child's interests, preferences, and medical conditions. Videos for emotional support depict a teddy bear in playful outfits comfortably using an inhaler, a nebulizer, or an EpiPen. We also demonstrate Sora's educational potential with a video that uses a seesaw to illustrate diabetes management. Overall, this study represents the first application of text-to-video AI software in pediatrics. Through personalized child-like videos, Sora can foster a sense of comfort and engagement for children in hospital.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".