PDF(Pubmed)
Abstract：
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.
摘要：
心血管疾病（CVD）是全球范围内死亡的主要原因，特别是在资源有限的国家，获得医疗资源的机会有限。早期检测和准确成像对于管理CVD至关重要，强调患者教育的重要性。生成人工智能(AI)包括合成文本的算法，演讲,images,以及在特定场景或提示下的组合，为加强患者教育提供了有希望的解决方案。通过结合视觉和语言模型，生成AI通过自然语言交互实现个性化多媒体内容生成，有益于心血管影像学的患者教育。模拟,基于聊天的互动，基于语音的界面可以增强可访问性，尤其是在资源有限的环境中。尽管有潜在的好处,在资源有限的国家实施生成式人工智能面临数据质量等挑战，基础设施限制，和道德考虑。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道德挑战，以确保更好的患者理解。治疗依从性，改善医疗保健结果。继续研究,创新,在生成AI中的合作有可能彻底改变患者的教育。这可以使患者对心血管健康做出明智的决定，最终在资源有限的环境中改善医疗保健结果。
