2024-09-04-KPMG-Realizing_the_value_of_AI_in_MedTech_within_Asia_Pacific_80页_5mb

Summary of "Realizing the Value of AI in MedTech within Asia Pacific"

Core Content

This whitepaper explores the opportunities and challenges of integrating artificial intelligence (AI) into the MedTech industry within the Asia Pacific region. It emphasizes the need for stakeholders to work collaboratively to ensure equitable, financially viable, and trusted access to AI-driven healthcare solutions.

Main Viewpoints

• Asia Pacific's Healthcare Landscape:

  • Over 60% of the world's population resides in the Asia Pacific.
  • The region faces a rapidly aging population, limited healthcare access, a growing burden of disease, and low healthcare expenditure relative to the OECD average.
  • These challenges create a strong case for AI to enhance healthcare delivery and outcomes.

• AI's Role in Intelligent Healthcare:

  • AI in MedTech is seen as a key enabler of the next digital evolution in healthcare.
  • It enhances the power of health data, supports adaptive and intelligent devices, and introduces new use cases along the care continuum.

• Three Key Enablers for AI Adoption in MedTech:

  • Access: Ensuring equitable and financially sustainable access to AI in MedTech across the care continuum.
  • Capability: Building the skills and infrastructure needed to deliver, adopt, and sustain AI in MedTech.
  • Trust: Ensuring confidence in the ethical, safe, and effective use of AI in healthcare settings.

Key Information

Access Considerations

• Financial Access: Reimbursement mechanisms are crucial for AI adoption. However, there is a lack of clarity and coherence in these frameworks across Asia Pacific.
• Equitable Access: Disparities in digital infrastructure, AI literacy, and investment across the region may widen healthcare gaps.
• Strategies for Improvement:
  • Expand and align government-led reimbursement frameworks for AI in MedTech.
  • Explore alternative funding mechanisms such as grants, subsidies, crowdfunding, and private insurance to lower costs for AI development and deployment.

Capability Considerations

• Skills and Infrastructure: Stakeholders need the right skills and infrastructure to support AI integration.
• Talent and Readiness: Limited availability of AI capability and talent in the region poses a challenge.
• Strategies for Improvement:
  • Invest in regional AI readiness and education.
  • Develop guidelines for digital sustainability in AI for MedTech.

Trust Considerations

• Ethical and Safe Use: Trust in AI is essential for its adoption in clinical and patient care.
• Governance and Oversight: Rigorous education and oversight are necessary to ensure responsible deployment and governance of AI in healthcare.
• Strategies for Improvement:
  • Establish a regionally aligned equity and governance framework for AI in MedTech.
  • Develop clear guidelines for ethical AI use and ensure transparency in AI algorithms and decision-making.

Case Studies

1. Aortic Aneurysm CT Scans:

   • Challenge: Traditional methods of measuring aortic aneurysms are time-consuming and inconsistent.
   • Solution: Siemens Healthineers introduced AI-Rad Companion Chest CT to streamline the process.
   • Outcomes: Reduced inter-reader variability by 42.5% and cut radiologist reporting time by 63%.

2. Operating Room Efficiency and Learning:

   • Challenge: Documenting surgical videos for learning and quality improvement is time-intensive.
   • Solution: Johnson & Johnson MedTech's Polyphonic™ digital ecosystem uses AI for real-time sharing and post-case analysis.
   • Outcomes: Enhanced surgical learning and improved patient and hospital experiences.

3. Tumor Diagnosis in Biopsy Tissues:

   • Challenge: Limited access to diagnostics in underserved areas.
   • Solution: Qritic's QAi Prostate uses AI and ML to classify tumor areas in biopsy tissues.
   • Outcomes: More accurate and timely diagnoses, supporting better healthcare accessibility and scalability.

Call-to-Action

• The report calls for a collaborative effort among stakeholders, including governments, industry, payors, and regulators, to address the challenges and realize the value of AI in MedTech.
• It suggests forming regional working groups to align on reimbursement strategies, value assessment, and governance frameworks.
• There is a need to invest in AI readiness, education, and digital sustainability to ensure long-term success and trust in AI-driven healthcare solutions.

Conclusion

The integration of AI in MedTech has the potential to revolutionize healthcare in Asia Pacific. However, realizing this potential requires addressing key challenges related to access, capability, and trust. Through strategic collaboration and investment in these areas, the region can become a global leader in AI and MedTech innovation.