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 narrative 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.