{
  "task": {
    "title": "AI Diagnostic Systems in Clinical Healthcare: Adoption and Impact 2023-2025",
    "objective": "Analyze current AI diagnostic tool adoption rates, clinical outcomes, and implementation barriers in hospital settings to inform technology investment decisions",
    "type": "analytical"
  },
  "context": {
    "background": "Healthcare AI market projected to reach $188B by 2030. Hospital systems evaluating AI diagnostic tools face challenges in ROI measurement, regulatory compliance, and clinical workflow integration. FDA has approved 500+ AI medical devices.",
    "audience": "executive",
    "use_case": "Technology investment roadmap for regional hospital network",
    "prior_knowledge": [
      "FDA has approved 500+ AI medical devices",
      "Major EMR vendors integrating AI features",
      "Radiology appears to lead in AI adoption"
    ]
  },
  "questions": {
    "primary": "What is the current state of AI diagnostic tool adoption in US hospitals and what factors determine successful implementation?",
    "secondary": [
      "What are the top 10 FDA-approved AI diagnostic tools by adoption rate?",
      "What clinical outcomes improvements are documented in peer-reviewed studies?",
      "What are the primary barriers to adoption reported by hospital administrators?",
      "How do implementation costs compare to documented ROI?",
      "What regulatory considerations affect deployment decisions?"
    ],
    "hypotheses": [
      "Larger hospital systems have higher AI adoption rates due to IT resources",
      "Radiology AI tools show highest adoption among specialties",
      "Integration with existing EMR systems is the primary technical barrier"
    ],
    "exclusions": [
      "Consumer health apps and wearables",
      "Administrative AI (billing, scheduling)",
      "Drug discovery and development AI",
      "Mental health chatbots"
    ]
  },
  "constraints": {
    "timeframe": {
      "start": "2023-01-01",
      "end": "present",
      "focus_period": "2024-2025"
    },
    "geography": {
      "scope": "national",
      "regions": ["United States"],
      "exclude_regions": []
    },
    "sources": {
      "required_types": ["peer_reviewed", "official_docs", "industry_reports"],
      "preferred_domains": ["pubmed.gov", "fda.gov", "healthaffairs.org", "jamanetwork.com"],
      "excluded_domains": ["medium.com", "wikipedia.org"],
      "min_quality": "B",
      "language": ["en"]
    },
    "data_requirements": {
      "quantitative": true,
      "qualitative": true,
      "specific_metrics": [
        "adoption rate by hospital size",
        "sensitivity/specificity metrics",
        "ROI timeline",
        "implementation cost range",
        "clinical outcome improvements"
      ]
    }
  },
  "output": {
    "format": "comprehensive_report",
    "length": {
      "min_words": 5000,
      "max_words": 12000,
      "executive_summary_words": 500
    },
    "structure": {
      "include_executive_summary": true,
      "include_methodology": true,
      "include_visualizations": true,
      "include_raw_data": false,
      "include_bibliography": true,
      "include_appendices": true,
      "generate_website": false
    },
    "citation_style": "APA",
    "tone": "professional"
  },
  "keywords": [
    "AI diagnostics",
    "clinical AI adoption",
    "FDA approved AI",
    "radiology AI",
    "healthcare AI ROI",
    "hospital AI implementation",
    "medical imaging AI",
    "clinical decision support"
  ],
  "special_instructions": [
    "Include comparison table of top AI diagnostic tools with approval dates and clinical evidence",
    "Highlight regulatory considerations (FDA, HIPAA, state regulations)",
    "Note any conflicts of interest in cited studies",
    "Separate findings by hospital size (small, medium, large systems)"
  ]
}