โก Quick Summary
The emergence of text-to-video (T2V) generation models like OpenAI’s Sora and Google’s Veo 2 is set to transform healthcare by enhancing patient education, medical training, and telemedicine. This narrative review highlights both the potential benefits and the critical challenges associated with these technologies.
๐ Key Details
- ๐ Studies Reviewed: 41 relevant studies from 2020 to 2024
- โ๏ธ Technologies: Sora Turbo (OpenAI) and Veo 2 (Google)
- ๐ Key Applications: Patient education, medical training, telemedicine
- โ ๏ธ Challenges: Misinformation, privacy breaches, ethical concerns
๐ Key Takeaways
- ๐น T2V models can create tailored videos for patient education.
- ๐ฉโโ๏ธ Medical training could be standardized and enhanced through video content.
- ๐ Telemedicine may be optimized with high-fidelity video consultations.
- โ ๏ธ Risks include misinformation and deepfake technology.
- ๐ Detection mechanisms for deepfakes are currently underdeveloped.
- ๐ Regulatory frameworks need to be established to address ethical concerns.
- ๐ Future advancements could lead to real-time healthcare visualizations.
- โ๏ธ Accessibility challenges must be addressed to ensure equitable implementation.
๐ Background
The rapid advancement of generative artificial intelligence (AI) has opened new avenues in various fields, particularly in healthcare. The introduction of T2V generation models represents a significant leap forward, offering innovative solutions for patient engagement and medical training. However, as with any emerging technology, it is essential to consider the implications and challenges that accompany these advancements.
๐๏ธ Study
This narrative review conducted a comprehensive literature search across databases such as PubMed and Google Scholar, focusing on the applications of T2V AI generation models in healthcare. The review identified 41 studies published between 2020 and 2024, providing a broad overview of the current landscape and future directions for these technologies.
๐ Results
The findings indicate that T2V models like Sora and Veo 2 could significantly enhance patient education by delivering customized video content. Additionally, these models have the potential to standardize medical training and improve the quality of remote consultations. However, the review also highlighted critical challenges, including the risks of misinformation and the need for robust detection mechanisms for deepfakes.
๐ Impact and Implications
The integration of T2V AI generation models into healthcare could lead to a more effective and innovative system. By improving patient education and training, these technologies have the potential to enhance overall healthcare delivery. However, addressing the associated challenges is crucial to ensure that these advancements do not exacerbate existing disparities in healthcare access and quality.
๐ฎ Conclusion
The review underscores the transformative potential of T2V AI generation models in healthcare. While the benefits are promising, it is essential to navigate the challenges carefully to harness these technologies effectively. Continued interdisciplinary research and collaboration among stakeholders will be vital in shaping a future where AI-driven video generation enhances healthcare delivery while safeguarding against misuse.
๐ฌ Your comments
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OpenAI’s Sora and Google’s Veo 2 in Action: A Narrative Review of Artificial Intelligence-driven Video Generation Models Transforming Healthcare.
Abstract
The rapid evolution of generative artificial intelligence (AI) has introduced transformative technologies across various domains, with text-to-video (T2V) generation models emerging as transformative innovations in the field. This narrative review explores the potential of T2V AI generation models used in healthcare, focusing on their applications, challenges, and future directions. Advanced T2V platforms, such as Sora Turbo (OpenAI, Inc., San Francisco, California, United States) and Veo 2 (Google LLC, Mountain View, California, United States), both announced in December 2024, offer the capability to generate high-fidelity video contents. Such models could revolutionize healthcare by providing tailored videos for patient education, enhancing medical training, and possibly optimizing telemedicine. We conducted a comprehensive narrativeย literature search of databases including PubMed and Google Scholar, and identified 41 relevant studies published between 2020 and 2024. Our findings reveal significant possible benefits in improving patient education, standardizing customized medical training, and enhancing remote medical consultations. However, critical challenges persist, including risks of misinformation (or deepfake), privacy breaches, ethical concerns, and limitations in video authenticity. Detection mechanisms for deepfakes and regulatory frameworks remain underdeveloped, necessitating further interdisciplinary research and vigilant policy development. Future advancements in T2V AI generation models could enable real-time healthcare visualizations and augmented reality training. However, achieving these benefits will require addressing accessibility challenges to ensure equitable implementation and prevent potential disparities. By addressing these challenges and fostering collaboration among stakeholders, healthcare systems and AI technologists, T2V AI generation models could transform global healthcare into a more effective, universal, and innovative system while safeguarding against its potential misuse.
Author: [‘Temsah MH’, ‘Nazer R’, ‘Altamimi I’, ‘Aldekhyyel R’, ‘Jamal A’, ‘Almansour M’, ‘Aljamaan F’, ‘Alhasan K’, ‘Temsah AA’, ‘Al-Eyadhy A’, ‘Aljafen BN’, ‘Malki KH’]
Journal: Cureus
Citation: Temsah MH, et al. OpenAI’s Sora and Google’s Veo 2 in Action: A Narrative Review of Artificial Intelligence-driven Video Generation Models Transforming Healthcare. OpenAI’s Sora and Google’s Veo 2 in Action: A Narrative Review of Artificial Intelligence-driven Video Generation Models Transforming Healthcare. 2025; 17:e77593. doi: 10.7759/cureus.77593