2022-12-03-Capgemini-健康与人工智能的现在和未来（英）_108页_5mb

Healthcare AI Industry Analysis: Key Findings

1. Executive Engagement & Strategy

• AI is deemed a key strategic priority (avg. 3.1/4) but C-suite acculturation lags.
• Dedicated Data & AI teams exist (~63%), often under C-level, but lack direct board-level influence.
• Research labs lead in AI adoption, while Hospitals struggle with C-level tech literacy.

2. Data & AI Use Cases & Industrialization

• Top Use Cases: Hospitals focus on care delivery/efficiency; Pharma/MedTechs prioritize drug discovery.
• Data accessibility/q
• Startups industrialize fewer use cases (avg. 5) but show higher scaling ambition.

3. Organizational Maturity in Data Journey

• Maturity States:
  • Learners: Small orgs with low scaling capacity.
  • Transitional: Larger orgs progressing use cases.
  • Experienced: Few large players with full ecosystems.
• Data interoperability remains the #1 barrier to collaboration.

4. Technology Trends

• Cloud migration accelerates (AWS, Azure preferred).
• AI techniques: Data viz & Machine Learning dominant, Quantum at nascent stages.
• Sustainability is a growing criterion, especially for Hospitals & Startups.

5. Ethics & Privacy Focus

• Privacy is mastered regulatory-wise, but explainability (esp. for Research Labs) is a rising concern.
• Data sovereignty increasingly debated, driven by geopolitical instability.

6. Key Cross-Sector Themes

• Patient Centricity: Tools improving engagement, but personalization lagging.
• Future of R&D: In silico drug discovery & decentralized trials gain traction.
• Connected Health: Slow value demonstration in patient/disease management.

Summary Caveats

• Ecosystem fragmentation hinders progress; collaboration with Startups/external actors is key.
• Talent shortages persist; Startups lag in traditional profiles but excel in integration capability.
• Regulation (EU AI Act) looms large, demanding co-construction to avoid over-compliance.