Summary:
AI in healthcare can be transformative, but if action is not taken on six pivotal transitions, the health sector is at risk of not reaching its full potential and falling behind.
Artificial intelligence (AI) is part of a broader digital revolution that has the potential to transform healthcare in many ways. At the front end, AI enabled tools can improve care, from enhancing clinicians’ capabilities and freeing up time for them to focus on patient relationships to enabling patients to take greater control of their own health; at the back end, augmenting non-patient-facing elements improves the efficiency of all facets of healthcare delivery and life sciences by optimizing system-wide operations. However, the adoption of AI at scale remains slow, and there is a risk that its transformative potential in healthcare may not be fully realized.
The World Economic Forum Digital Healthcare Transformation (DHT) initiative has conducted research on AI adoption in healthcare through interviews with experts. Three major challenges were identified that hinder the scaling of AI in healthcare:
1. Complexity of AI in health deterring policymakers and business leaders: Despite the perception that AI in health receives significant attention and energy, it struggles to gain traction on political and strategic agendas.
2. Misalignment of technical choices with strategic visions: Health leaders often delegate technical decisions, missing opportunities to align technology with their strategic goals.
Additionally, misaligned incentives often hinder decisions from supporting shared goals and collective ideals.
3. Low confidence in AI within a fragmented regulatory and governance framework: Rising public distrust in AI and industry skepticism could hinder its adoption in health.
Six pivotal transitions are needed to enable AI-driven healthcare to reach systemic and global scale:
1. From dreaming of breakthroughs to delivering near-term benefits that accelerate a long-term vision: Focus on operational applications of AI in health and collaborate with private-sector leaders to demonstrate returns, leading to long-term investments.
2. From the private sector progressing technology independently to public–private ecosystems driving shared objectives and benefits: Align public and private leaders on priorities, recognize the potential value of AI in medical applications and agree on how to share this value.
3. From fighting on infrastructure to winning on services: Prioritize shared infrastructure such as digital public infrastructures (DPIs) at the forefront of technical choices. Where feasible, seek shared investments for public-good solutions that would align with private-sector service offerings.
4. From leaders with good intentions to leaders who make responsible technical decisions: Upskill and engage leaders at all levels to make strategic decisions with full awareness of the technical aspects.
5. From waiting for guidelines to proactively building trust: Actively engage in improving post-market surveillance to detect early AI-related risks with speed and transparency, as well as considering AI ethical committees and principles.
6. From dispersed data to deliberate integration: Advocate for local control of data within a globally connected and patient-centred system to both ensure patient privacy and safety and drive innovation.
These shifts will drive deployments of AI in healthcare that deliver truly transformative improvements in well-being, continuous access to personalized AI health assistants, enhanced operational excellence for healthcare systems and leapfrogging by low- or middle-income countries (LMICs). Realizing this vision necessitates overcoming risks and challenges related to privacy, cybersecurity, upskilling clinicians and patients, equitable access and regulation.
AI presents an opportunity for the private sector to build businesses that promote better health worldwide and for the public sector to reinvent approaches for managing population health. It also enables the public and private sectors to join forces to address the enduring healthcare challenges facing the world. The collaborative efforts required will vary by country, depending on digital maturity and specific issues. In LMICs, the focus will be on establishing foundational technology and expanding access to quality care in order to address ongoing challenges such as high disease burden and weak health information systems. In advanced economies, however, interoperability will be essential for improving efficiency and outcomes to meet the needs of increasingly strained healthcare systems.
