Healthcare could be one of AI’s greatest success stories. The sector is under immense strain: one in four healthcare professionals considers leaving their job weekly, and by 2030, the world faces a shortage of 10 million doctors. Meanwhile, medical knowledge is doubling every 73 days, and AI-powered tools are evolving at breakneck speed.
If innovation is moving forward, why is healthcare getting increasingly strained?
The problem: a fragmented system that slows care
A century ago, patients would visit their local practitioner for anything from a broken toe to childbirth. Today, medicine has become hyper-specialized – producing extraordinary advancements but also unintended inefficiencies. Patients now move through a web of specialists, each responsible for a narrow slice of care, often leading to delays and fragmented treatment – patients going from clinic to clinic, doctor to doctor.
This system works well for complex cases, but it also creates bottlenecks, forcing patients to navigate referral pathways that slow down even routine medical decisions. It’s no surprise that healthcare professionals are overburdened and patients wait longer than ever for care.
Why AI keeps getting stuck in pilot mode
AI is frequently positioned as the solution to these challenges, yet adoption is still in its early days. A recent Corti-YouGov study found that just one in five European healthcare professionals have used AI in the past month at work. Even in the US, where adoption is higher, a third of healthcare professionals spend up to three hours weekly correcting AI mistakes – a frustrating inefficiency when AI is meant to save time.
So why is AI struggling to scale beyond pilots? The common assumption is that healthcare is slow to adopt technology. But the reality is different: hospitals are filled with cutting-edge machines, from MRI scanners to robotic surgical systems. The real issue is that healthcare is cautious – quite rightly – about deploying unreliable or expensive AI tools, and in many cases, the results just aren’t cutting it.
The missing piece: specialized AI built for healthcare
The majority of AI applications today are built on general-purpose infrastructure, designed to handle everything from drafting emails to generating code. But healthcare isn’t like other industries. The stakes are higher, the terminology more complex, and the risks of misinformation more severe.
A one-size-fits-all approach won’t work. AI in healthcare needs to be as specialized as medicine itself – trained on medical data, tuned for clinical accuracy, and designed to integrate seamlessly into existing workflows. Instead of generalist AI that hallucinates medical information or produces vague, excessive text, healthcare professionals need AI that delivers concise, precise, and context-aware insights.
Imagine an AI that understands that a positive test result doesn’t mean something positive, or that the word “discharge” has multiple meanings depending on context. These aren’t small details – they can be the difference between life and death. Building AI for healthcare on models that have a deep understanding of the high stakes, highly nuanced and highly regulated sector is the path to success.
Fewer Swiss Army knives, more scalpels
General AI is a powerful tool, but in healthcare, precision matters more than versatility. The industry needs fewer Swiss Army knives and more scalpels – AI models tailored to specific use cases, designed to plug into existing tools, and built to support the diverse needs of specialists and generalists alike.
This rethink extends beyond just AI model architecture. It means re-evaluating how AI is developed, tested, and integrated into clinical environments. AI must prove its value beyond the pilot phase, meeting the same high standards as any other medical innovation.
A call to action for AI developers
For AI to truly transform healthcare, developers need to shift focus. It is a noisy market, and demand is high for AI tools. But without building on the right foundations, this fast-paced, promising ecosystem risks grinding to a halt. Building better, more specialized AI apps from specialized foundations will mean they will fit seamlessly into real-world clinical settings, and earn the trust of doctors and nurses.
The challenge is significant, but the rewards are even greater. If done right, AI won’t just reduce clinician burnout – it will create a healthcare system that delivers faster, more efficient, and more equitable care.
The opportunity is vast, but unlocking it requires a fundamental rethink of AI’s role in healthcare.
