Marey Ahmed, Saad Abdelrahman M, Killeen Benjamin D, Gomez Catalina, Tregubova Mariia, Unberath Mathias, Umair Muhammad
Alexandria University Faculty of Medicine, Alexandria, 21521, Egypt.
Department of Computer Science, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, United States.
BJR Open. 2024 Jul 17;6(1):tzae018. doi: 10.1093/bjro/tzae018. eCollection 2024 Jan.
Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.
心血管疾病（CVD）是全球主要的死亡原因，在医疗资源有限的资源匮乏国家尤其如此。早期检测和准确成像对于心血管疾病的管理至关重要，这凸显了患者教育的重要性。生成式人工智能（AI），包括在特定场景或提示下合成文本、语音、图像及其组合的算法，为加强患者教育提供了有前景的解决方案。通过结合视觉和语言模型，生成式人工智能能够通过自然语言交互实现个性化多媒体内容生成，有益于心血管成像方面的患者教育。模拟、基于聊天的交互和基于语音的界面可以提高可及性，尤其是在资源有限的环境中。尽管有潜在益处，但在资源匮乏国家实施生成式人工智能面临数据质量、基础设施限制和伦理考量等挑战。解决这些问题对于成功采用至关重要。还必须克服与数据隐私和准确性相关的伦理挑战，以确保患者有更好的理解、治疗依从性并改善医疗结果。在生成式人工智能方面持续开展研究、创新和合作有可能彻底改变患者教育。这可以使患者能够就其心血管健康做出明智决策，最终改善资源匮乏环境中的医疗结果。
