Will AI cure healthcare’s woes or expose its weaknesses?

Artificial intelligence is already reshaping the world. In healthcare, the potential is especially profound: reduced administrative burden, faster diagnostics, personalised care, and smarter systems that adapt to patient needs. With AI now moving from lab to bedside, the question is not whether it will change healthcare — but how.

From hype to health system reality

Unlike the slow, often mandated rollout of electronic health records (EHRs), AI has rapidly expanded into the sector through clinicians and executives eager to transform care. Use cases like ambient documentation, clinical summarisation, and predictive analytics are already in use. Budget lines for AI are often growing faster than those for general IT spending (Bessemer Venture Partners, 2025).

AI adoption often begins with pilots. These are valuable for testing ideas, but the real gains come when systems shift from proof-of-concept to production. That requires more than enthusiasm — it means embedding AI into everyday workflows, designing for interoperability, and creating strategies that scale across teams, sites, and populations.

Healthcare leaders are familiar with the hurdles:

• Poor data quality and fragmentation
• High integration costs
• Limited in-house AI expertise
• Unclear governance frameworks

But each challenge is also an opportunity. By investing in data quality, adopting interoperability standards, and setting clear governance frameworks, organisations can create the conditions for AI to thrive. What begins as a barrier can become the very foundation for safe, effective, and system-wide adoption.

The digital maturity gap: healthcare’s hidden handbrake

Compared to industries such as finance and technology, the healthcare sector continues to lag behind in digital maturity. Many organisations still grapple with fragmented data, outdated infrastructure, and limited interoperability. But this isn’t a failure — it reflects the complexity of healthcare systems and the critical need to safeguard patient safety.

The BCG Digital Acceleration Index clearly illustrates this gap as healthcare scores significantly lower than sectors such as tech, finance, and consumer products. As technology evolves rapidly, healthcare’s slower pace of adoption widens the maturity gap, creating a growing divide between what’s possible and what’s actually implemented.

But digital maturity gaps don’t only exist across industries — they are also evident within healthcare itself.

The BVP Healthcare AI Adoption Index highlights that the vast majority of providers remain at the very start of their AI journey. Most are still experimenting in limited pilots rather than embedding AI into core operations. This illustrates just how early healthcare is in its adoption curve, and how much ground there is still to cover.

Martec’s Law captures this tension clearly: technology evolves exponentially, while organisations change logarithmically. In healthcare, this dynamic creates a widening gulf between innovation and adoption — between what AI can do, and what it is doing today.

Strategies for becoming AI ready

Moving from pilot to production takes more than a proof of concept; it demands a clear, system-wide strategy. The Frontiers in Digital Health framework, developed by Byberg and Crimi (2025), outlines five essential capabilities that hospitals should develop to support safe and scalable AI adoption.

1. Robust digital infrastructure

Invest in platforms that support real-time data ingestion, FHIR APIs, and structured data capture to enhance your data management capabilities.

2. Data governance

AI must be safe, ethical, and explainable. That means clear rules for consent, access, privacy and bias mitigation.

3. Workforce empowerment

AI adoption succeeds when it supports, not supplants, clinical decision-making. Provide training, redesign roles, and ensure staff feel empowered rather than replaced.

4. Collaborative partnerships

No one solves AI in healthcare alone. Progress depends on collaboration across clinical, technical, and organisational domains, bringing together innovators, care providers, and system leaders to co-design effective and trustworthy solutions.

5. Continuous evaluation

Monitoring and iteration are vital. Models degrade. Workflows shift. Evaluation must be baked into every deployment.

Trust, transparency and cultural change

Technology alone will not deliver transformation. Trust will determine whether AI thrives or fails in healthcare. Safe adoption requires:

• Strong governance
• Transparency
• Accountability

Too often, organisations chase the promise of AI without investing in the hard groundwork: high-quality data pipelines and robust oversight mechanisms.

Equally decisive is culture. AI adoption succeeds when physicians retain oversight, analysts utilise AI to augment their judgment, and staff are trained to view AI as a partner, not a threat. Leadership must establish clear boundaries, promote openness, and cultivate trust.

AI adoption isn’t only a technical shift, it’s a cultural one. Trust is built when clinicians feel supported rather than supplanted, when data is handled transparently, and when governance frameworks provide clarity around safety and accountability. The most successful initiatives are those where AI augments human decision-making, restores clinician focus on care, and gives patients confidence that technology is being used responsibly.

From potential to impact: what makes the difference

So, will AI cure or expose? The answer depends on how healthcare responds. Those who:

• Invest in robust data foundations
• Embed strong governance
• Build AI-ready cultures

…will harness AI’s potential to improve efficiency, quality, and equity.

Those unwilling or unable to adapt risk magnifying existing inefficiencies and vulnerabilities.

AI alone won’t cure healthcare’s challenges. But when combined with strong foundations, it becomes a catalyst for transformation. With the right data, governance, and culture in place, AI can help health systems move from reactive to preventative care, identify risks before they escalate, and deliver insights that support more timely, equitable, and efficient care. Rather than reflecting only where healthcare stands today, AI can accelerate the journey to where it needs to be.

The next step

This is where the next chapter begins. The health systems that will succeed are those that view AI not as an experiment, but as an integral part of the foundation for delivering care. That means weaving intelligence into the record itself, making insights available in real time, and ensuring clinicians are supported rather than burdened.

At Orion Health, we’ve been building those foundations for over three decades, connecting health systems at a national scale, unifying data, and enabling clinicians to work with clarity. With Amadeus AI, we’re taking the next step: embedding advanced, clinically validated intelligence directly into connected records. For health systems, this means moving faster from data to decisions. For clinicians, it means freedom from data overload and more time with patients. For populations, it means shifting from reactive to preventive care, detecting risks earlier and creating healthier communities.

AI won’t cure healthcare on its own. But with the right foundations, it can accelerate progress. Amadeus AI is designed to help health systems make that leap — safely, at scale, and with purpose.

Authored by Tom Varghese, Global Product Marketing & Growth Manager at Orion Health.

References

• Bessemer Venture Partners. The Healthcare AI Adoption Index. San Francisco: Bessemer Venture Partners, 2025.
• Boston Consulting Group. How Digital Divides Health Care Providers. December 2020.
• Crimi, Marco, and Emil Byberg. “Preparing Hospitals and Health Organizations for AI: Practical Guidelines for the Required Infrastructure.” Frontiers in Digital Health 7 (2023): 165006.
• Deloitte Insights. Health Care’s Quest for an Enterprise wide AI Strategy. Deloitte Center for Health Solutions, June 27, 2022.
• Deloitte Asia Pacific. AI at a Crossroads: Building Trust as the Path to Scale. Deloitte Access Economics and AI Institute, 2024.
• NEJM Catalyst, sponsored by Deloitte. Are Health Care Organizations Ready for Generative AI? Insights Report, July 2024.
• Ranjbar, Arian, Eilin Wermundsen Mork, Jesper Ravn, Helga Brøgger, Per Myrseth, Hans Peter Østrem, and Harry Hallock. “Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation?” Risk Management and Healthcare Policy (2024): 877–882.
• Reddy Nalla, Sai Madhav. “Laying the Foundation: Why Data Readiness Is the Cornerstone of Successful AI Initiatives.” Journal of Computer Science and Technology Studies 7, no. 7 (July 2025): 106–117.
• Yunusa, Edime. “Creating an Artificial Intelligence-Ready Organizational Culture: Harmonizing Human Existence with AI Strategic Decision-Making.” International Journal of Business Sustainability 1, no. 1 (2025): 67–83.