Lightstar/backend/data/cache/research_1744959673.json

{
  "research_intent": "人工智能在医疗诊断中的应用",
  "timestamp": 1744959560.8536503,
  "language": "zh",
  "english_keywords": [
    "artificial intelligence",
    "medical diagnosis",
    "machine learning",
    "healthcare",
    "deep learning",
    "clinical decision support",
    "diagnostic imaging",
    "predictive analytics"
  ],
  "original_keywords": [
    "人工智能，医学诊断，机器学习，医疗保健，深度学习，临床决策支持，诊断影像，预测分析"
  ],
  "english_directions": [
    "How can deep learning models improve the accuracy and interpretability of diagnostic imaging (e.g., MRI, CT scans) for early detection of rare diseases?",
    "What role can multimodal machine learning (combining imaging, genomics, and EHR data) play in personalized clinical decision support for chronic disease management?",
    "How can federated learning frameworks address data privacy concerns while enabling robust predictive analytics for hospital readmission risk across diverse healthcare systems?",
    "Can lightweight AI models deployed on edge devices achieve comparable performance to cloud-based systems in real-time point-of-care medical diagnosis for resource-limited settings?",
    "What ethical and regulatory challenges arise when integrating explainable AI systems into high-stakes diagnostic workflows, and how can they be mitigated?",
    "How can reinforcement learning optimize dynamic treatment planning in oncology by integrating real-time patient monitoring data with clinical guidelines?"
  ],
  "original_directions": [
    "深度学习模型如何提高诊断影像（如MRI、CT扫描）在罕见疾病早期检测中的准确性和可解释性？",
    "多模态机器学习（结合影像、基因组学和电子健康记录数据）在慢性病管理的个性化临床决策支持中可以发挥什么作用？",
    "联邦学习框架如何通过解决数据隐私问题，在不同医疗系统中实现针对再入院风险的稳健预测分析？",
    "在资源有限的环境中，部署于边缘设备的轻量级AI模型能否在实时护理点医疗诊断中达到与云端系统相当的性能？",
    "将可解释AI系统整合到高风险诊断工作流程时会引发哪些伦理和监管挑战？应如何缓解？",
    "强化学习如何通过整合实时患者监测数据与临床指南，优化肿瘤学中的动态治疗计划？"
  ],
  "papers_by_direction": [
    {
      "direction": "How can deep learning models improve the accuracy and interpretability of diagnostic imaging (e.g., MRI, CT scans) for early detection of rare diseases?",
      "original_direction": "深度学习模型如何提高诊断影像（如MRI、CT扫描）在罕见疾病早期检测中的准确性和可解释性？",
      "papers": []
    },
    {
      "direction": "What role can multimodal machine learning (combining imaging, genomics, and EHR data) play in personalized clinical decision support for chronic disease management?",
      "original_direction": "多模态机器学习（结合影像、基因组学和电子健康记录数据）在慢性病管理的个性化临床决策支持中可以发挥什么作用？",
      "papers": []
    },
    {
      "direction": "How can federated learning frameworks address data privacy concerns while enabling robust predictive analytics for hospital readmission risk across diverse healthcare systems?",
      "original_direction": "联邦学习框架如何通过解决数据隐私问题，在不同医疗系统中实现针对再入院风险的稳健预测分析？",
      "papers": []
    },
    {
      "direction": "Can lightweight AI models deployed on edge devices achieve comparable performance to cloud-based systems in real-time point-of-care medical diagnosis for resource-limited settings?",
      "original_direction": "在资源有限的环境中，部署于边缘设备的轻量级AI模型能否在实时护理点医疗诊断中达到与云端系统相当的性能？",
      "papers": []
    },
    {
      "direction": "What ethical and regulatory challenges arise when integrating explainable AI systems into high-stakes diagnostic workflows, and how can they be mitigated?",
      "original_direction": "将可解释AI系统整合到高风险诊断工作流程时会引发哪些伦理和监管挑战？应如何缓解？",
      "papers": []
    },
    {
      "direction": "How can reinforcement learning optimize dynamic treatment planning in oncology by integrating real-time patient monitoring data with clinical guidelines?",
      "original_direction": "强化学习如何通过整合实时患者监测数据与临床指南，优化肿瘤学中的动态治疗计划？",
      "papers": []
    }
  ],
  "clusters": [],
  "status": "completed",
  "direction_reports": [],
  "processing_time": 112.84043836593628
}