Evidence map›Paper›PMID 39663218›Full record

ReviewAesthetic plastic surgery2025

Text-to-Video Models and Sora in Plastic Surgery: Pearls, Pitfalls, and Prospectives.

Yuanbo Kang, Sifan Wang, Lin Zhu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Aesthetic plastic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. The first birthday of OpenAI's Sora: A promising but cautious future in medicine.International journal of surgery (London, England) · 2025
    Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yuanbo KangDepartment of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1#, Dongcheng District, Beijing, 100730, P. R. China.
Sifan WangPeking Union Medical College, Chinese Academy of Medical Sciences, Dongdan Santiao 9#, Dongcheng District, Beijing, 100730, P. R. China.
Lin ZhuDepartment of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1#, Dongcheng District, Beijing, 100730, P. R. China. pumchzhulin@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After the groundbreaking release of the highly acclaimed chatbot ChatGPT, which revolutionized the field of artificial intelligence (AI) last year, OpenAI has once again astounded the world with the unveiling of their latest generative AI model, Sora, on February 16, 2024. This cutting-edge model has the remarkable ability to generate videos up to a duration of 60 seconds solely through text instructions. With a series of AI-generated contents, such as AI chat, AI drawing, and AI music, emerging one after another, the era of "AI revolution" that had a disruptive impact on modern life has arrived. Meanwhile, AI has made significant achievements in the medical field, especially in diagnosing based on medical imaging. This article briefly describes the development history of text-to-video models and provides a detailed introduction to the Sora model, including its portrayal of the human face and contour, inspiring its potential applications in plastic surgery. It also provides a prospect for other AI-generated content technologies, such as text-to-holography and text-to-material objects.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Indexed as

Artificial IntelligenceFacePlastic Surgery ProceduresSurgery, PlasticVideo RecordingHumansPlastic surgerySoraText-to-video models

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.