Gen-AI: Enhancing Patient Education in Cardiovascular Imaging

BJR|Open Pub Date : 2024-07-17 DOI:10.1093/bjro/tzae018
A. Marey, Abdelrahman M. Saad, Benjamin D Killeen, Catalina Gomez, Mariia Tregubova, Mathias Unberath, Muhammad Umair
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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 AI, including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offer 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.
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Gen-AI:加强心血管成像中的患者教育
心血管疾病(CVD)是全球死亡的主要原因,尤其是在医疗资源有限的国家。早期检测和准确成像对控制心血管疾病至关重要,这就强调了患者教育的重要性。生成式人工智能,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前景广阔的解决方案。通过结合视觉和语言模型,生成式人工智能可通过自然语言交互生成个性化多媒体内容,从而有利于心血管成像领域的患者教育。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。尽管人工智能具有潜在的益处,但在资源有限的国家实施生成式人工智能面临着数据质量、基础设施限制和伦理考虑等挑战。解决这些问题对于成功采用人工智能至关重要。还必须克服与数据隐私和准确性相关的伦理挑战,以确保更好地理解患者、坚持治疗并改善医疗效果。生成式人工智能领域的持续研究、创新和合作有可能彻底改变患者教育。这将使患者能够对自己的心血管健康做出明智的决定,最终改善资源有限环境下的医疗效果。
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