Text-to-video generative artificial intelligence: sora in neurosurgery

Text-to-video generative artificial intelligence: sora in neurosurgery

Received: 16 May 2024 / Revised: 1 June 2024 / Accepted: 9 June 2024 / Published online: 13 June 2024 | Ali A. Mohamed, Brandon Lucke-Wold
The article discusses the potential of Sora, a text-to-video generative AI tool, in neurosurgery. Sora, developed by the creators of ChatGPT, leverages natural language processing, deep learning, and computer vision to generate high-quality videos from text prompts. The tool has several applications in neurosurgery, including patient education, public health, surgical training and planning, and research dissemination. However, it also faces limitations such as physically implausible motion, unnatural object morphing, and inaccurate physical interactions. Concerns about patient privacy, bias, and ethics are also highlighted. Despite these challenges, Sora and similar tools have the potential to significantly benefit the neurosurgical community. The article emphasizes the need for further investigation to assess the effectiveness of generative videos compared to non-generated counterparts.The article discusses the potential of Sora, a text-to-video generative AI tool, in neurosurgery. Sora, developed by the creators of ChatGPT, leverages natural language processing, deep learning, and computer vision to generate high-quality videos from text prompts. The tool has several applications in neurosurgery, including patient education, public health, surgical training and planning, and research dissemination. However, it also faces limitations such as physically implausible motion, unnatural object morphing, and inaccurate physical interactions. Concerns about patient privacy, bias, and ethics are also highlighted. Despite these challenges, Sora and similar tools have the potential to significantly benefit the neurosurgical community. The article emphasizes the need for further investigation to assess the effectiveness of generative videos compared to non-generated counterparts.
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