Ramírez López Leonardo J, Mora Ana María Campos
TIGUM Research Group, Universidad Militar Nueva Granada, Bogota, Colombia.
Healthc Inform Res. 2025 Jul;31(3):245-252. doi: 10.4258/hir.2025.31.3.245. Epub 2025 Jul 31.
This study proposes the integration of chatbots into personalized medicine by demonstrating how these tools can support the personalized medicine model. Chatbots can deliver tailored health recommendations, facilitate patient-doctor communication, and provide decision support in clinical settings. The goal is to establish a reference framework aligned with national and international standards for personalized healthcare solutions.
The chatbot model was developed by reviewing 30 scientific and academic articles focused on artificial intelligence and natural language processing in healthcare. The study analyzed the capabilities of existing healthcare chatbots, particularly their capacity to support personalized medicine through accurate data collection and processing of individual health information.
Key parameters identified for effective chatbot deployment in personalized medicine include user engagement, data accuracy, adaptability, and regulatory compliance. The study established a compliance benchmark of 25% based on current industry standards and application performance. The results indicate that the proposed chatbot model significantly increased the precision and efficacy of personalized medical recommendations, surpassing baseline requirements set by standardization organizations.
This model provides healthcare professionals and patients with a robust framework for utilizing chatbots in personalized medicine, focusing on improved patient outcomes and engagement. The research identifies a gap in the application of artificial intelligence-driven tools in personalized healthcare and suggests strategic directions for future innovations. Implementing this model aims to bridge this gap, offering a standardized approach to developing chatbots that support personalized medicine.
本研究通过展示聊天机器人如何支持个性化医疗模式，提出将其整合到个性化医疗中。聊天机器人可以提供量身定制的健康建议，促进医患沟通，并在临床环境中提供决策支持。目标是建立一个符合国家和国际个性化医疗保健解决方案标准的参考框架。
通过回顾30篇专注于医疗保健领域人工智能和自然语言处理的科学与学术文章，开发了聊天机器人模型。该研究分析了现有医疗保健聊天机器人的功能，特别是它们通过准确收集和处理个人健康信息来支持个性化医疗的能力。
在个性化医疗中有效部署聊天机器人所确定的关键参数包括用户参与度、数据准确性、适应性和法规遵从性。该研究根据当前行业标准和应用性能建立了25%的遵从基准。结果表明，所提出的聊天机器人模型显著提高了个性化医疗建议的精度和效果，超过了标准化组织设定的基线要求。
该模型为医疗保健专业人员和患者提供了一个在个性化医疗中利用聊天机器人的强大框架，重点是改善患者的治疗效果和参与度。该研究确定了人工智能驱动工具在个性化医疗保健应用中的差距，并为未来的创新提出了战略方向。实施该模型旨在弥合这一差距，提供一种开发支持个性化医疗的聊天机器人的标准化方法。
