Javan Ramin, Mostaghni Navid
Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, USA.
College of Medicine, California University of Science and Medicine, Colton, USA.
Cureus. 2024 Sep 15;16(9):e69471. doi: 10.7759/cureus.69471. eCollection 2024 Sep.
AI-powered multimedia generation technologies, particularly video synthesis through stable diffusion and transformers, offer transformative potential for radiology education, communication, and visualization. This study explores various AI-generated multimedia categories, including image and video generation, as well as voice cloning, with a focus on video synthesis and future possibilities like scan-to-video generation. Utilizing tools such as , , , and , we aimed to reincarnate deceased influential physicists in radiology, demonstrating AI's capability to generate realistic content with accessible tools, fostering creativity and innovation in the radiology community. We created 440 images through 110 prompts using image-to-image generation, 22 videos via image-to-video generation, and two videos showcasing text-to-voice and voice cloning techniques from December 1-7, 2023. Realism decreased from image-to-image to image-to-video and voiceover-to-video generations, with the latter requiring adjustments for lip, mouth, and head movements without incorporating facial expressions, eye movement, or hand motions. Potential applications in radiology include improving and speeding up medical 3D visualization, as well as enhancing educational content, information delivery, patient interactions, and teleconsultations. The paper addresses limitations and ethical considerations associated with AI-generated content, emphasizing responsible use and interdisciplinary collaboration for further development. These technologies are rapidly evolving, and future versions are expected to address current challenges. The ongoing advancements in AI-generated multimedia have the potential to revolutionize various aspects of radiology practice, education, and patient care, opening new avenues for research and clinical applications in the field.
人工智能驱动的多媒体生成技术，特别是通过稳定扩散和变压器进行的视频合成，为放射学教育、交流和可视化提供了变革性潜力。本研究探索了各种人工智能生成的多媒体类别，包括图像和视频生成以及语音克隆，重点是视频合成以及扫描到视频生成等未来可能性。利用诸如 、 、 和 等工具，我们旨在让放射学领域已故的有影响力的物理学家 “复活”，展示人工智能使用便捷工具生成逼真内容的能力，促进放射学领域的创造力和创新。我们在2023年12月1日至7日期间，通过110个提示使用图像到图像生成创建了440张图像，通过图像到视频生成创建了22个视频，并制作了两个展示文本到语音和语音克隆技术的视频。从图像到图像、图像到视频以及画外音到视频生成，逼真度逐渐降低，后者需要对嘴唇、嘴巴和头部动作进行调整，但不包括面部表情、眼球运动或手部动作。在放射学中的潜在应用包括改进和加速医学3D可视化，以及增强教育内容、信息传递、患者互动和远程会诊。本文讨论了与人工智能生成内容相关的局限性和伦理考量，强调负责任的使用和跨学科合作以实现进一步发展。这些技术正在迅速发展，预计未来版本将解决当前的挑战。人工智能生成的多媒体的持续进步有可能彻底改变放射学实践、教育和患者护理的各个方面，为该领域的研究和临床应用开辟新途径。
