Lightstar/backend/data/cache/research_1744960762.json

{
  "research_intent": "人工智能在医疗诊断中的应用",
  "timestamp": 1744960534.033066,
  "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 be optimized for early detection of rare diseases in diagnostic imaging while minimizing false positives?",
    "What role can multimodal machine learning (combining imaging, EHR, and genomic data) play in improving clinical decision support for personalized cancer diagnosis?",
    "How can federated learning frameworks be designed to enhance predictive analytics in healthcare while preserving patient privacy across institutions?",
    "Can explainable AI techniques improve clinician trust and adoption of artificial intelligence tools for medical diagnosis in high-stakes specialties like cardiology or neurology?",
    "What are the most effective strategies for mitigating bias in machine learning-based diagnostic systems when applied to diverse patient populations?",
    "How can reinforcement learning be adapted to optimize dynamic treatment planning in chronic disease management using real-time clinical data streams?"
  ],
  "original_directions": [
    "如何优化深度学习模型以在诊断影像中早期发现罕见疾病，同时最小化误报？",
    "多模态机器学习（结合影像、电子健康记录和基因组数据）在改善个性化癌症诊断的临床决策支持中可以发挥什么作用？",
    "如何设计联邦学习框架以增强医疗领域的预测分析，同时保护跨机构患者隐私？",
    "可解释人工智能技术能否提高临床医生对心脏病学或神经学等高风险专科医疗诊断人工智能工具的信任和采用？",
    "在应用于多样化患者群体时，减轻基于机器学习的诊断系统偏见的最有效策略是什么？",
    "如何调整强化学习以利用实时临床数据流优化慢性病管理的动态治疗计划？"
  ],
  "papers_by_direction": [
    {
      "direction": "How can deep learning models be optimized for early detection of rare diseases in diagnostic imaging while minimizing false positives?",
      "original_direction": "如何优化深度学习模型以在诊断影像中早期发现罕见疾病，同时最小化误报？",
      "papers": []
    },
    {
      "direction": "What role can multimodal machine learning (combining imaging, EHR, and genomic data) play in improving clinical decision support for personalized cancer diagnosis?",
      "original_direction": "多模态机器学习（结合影像、电子健康记录和基因组数据）在改善个性化癌症诊断的临床决策支持中可以发挥什么作用？",
      "papers": []
    },
    {
      "direction": "How can federated learning frameworks be designed to enhance predictive analytics in healthcare while preserving patient privacy across institutions?",
      "original_direction": "如何设计联邦学习框架以增强医疗领域的预测分析，同时保护跨机构患者隐私？",
      "papers": []
    },
    {
      "direction": "Can explainable AI techniques improve clinician trust and adoption of artificial intelligence tools for medical diagnosis in high-stakes specialties like cardiology or neurology?",
      "original_direction": "可解释人工智能技术能否提高临床医生对心脏病学或神经学等高风险专科医疗诊断人工智能工具的信任和采用？",
      "papers": []
    },
    {
      "direction": "What are the most effective strategies for mitigating bias in machine learning-based diagnostic systems when applied to diverse patient populations?",
      "original_direction": "在应用于多样化患者群体时，减轻基于机器学习的诊断系统偏见的最有效策略是什么？",
      "papers": []
    },
    {
      "direction": "How can reinforcement learning be adapted to optimize dynamic treatment planning in chronic disease management using real-time clinical data streams?",
      "original_direction": "如何调整强化学习以利用实时临床数据流优化慢性病管理的动态治疗计划？",
      "papers": []
    }
  ],
  "clusters": [],
  "status": "completed",
  "direction_reports": [],
  "processing_time": 228.2858166694641
}