AI in Healthcare: Bridging Bold Ideas and Real-World Challenges

Artificial intelligence shows up in healthcare news almost daily. One story touts a diagnostic breakthrough, another a scheduling shortcut, another a digital assistant promising to cut paperwork in half, they all sound transformative on the page. In practice, the sticking points are smaller but more challenging: a stroke patient lost in referral handoffs, a cardiology follow-up delayed because systems cannot communicate, or a clinician who hesitates to trust an unfamiliar output. The math is rarely the barrier. The context is. 

During my doctoral work in neuroimaging, the technical models performed as designed; however, the barriers to real-world use were different. Imaging data could not flow into hospital records, regulatory approval processes were slow, and clinicians had little reason to rely on outputs they did not fully understand. Two decades later, the same obstacles of interoperability, regulation, and clinician trust still determine how artificial intelligence moves from research into practice. 

The widening gap between promise and reality 

Investment in artificial intelligence for healthcare continues to grow. A market forecast estimates that the sector will reach $173.6 billion by 2029, with annual growth exceeding 40 percent. Surveys suggest high expectations: more than four in five healthcare stakeholders believe AI will influence clinical decisions, and a similar share expect it to lower labor costs through automation. Another report notes that 86 percent of organizations already describe themselves as using AI, with projections that the market will surpass $120 billion by 2028. 

However, hospital adoption remains limited. A 2022 PubMed1 analysis reported that fewer than one in five U.S. hospitals had implemented AI tools, and only about 4% were using them at a more advanced level.  In clinical diagnosis, fewer than one in five institutions reported significant success. Even among hospitals that use predictive models, about two-thirds apply them to inpatient trajectory, outpatient risk scoring, or scheduling support. The results remain uneven, and trust in the outputs is limited.  The difference between projected growth and lived adoption remains the key issue. 

Structural barriers, not technical ones 

The obstacles slowing adoption are not primarily technical. Clinicians describe their needs in terms of patient care, such as continuity after a stroke or reliable follow-up for cardiology patients. Turning those clinical needs into software is not straightforward. It depends on people who understand how medicine and technology can work together over time. Decisions about payment and procurement are typically made by administrators who are often removed from the clinical floor, which can result in the costs and benefits being allocated to different groups.  Even tools cleared by the FDA can lose credibility when they underperform outside controlled trials. Culture adds another layer, as health systems tend to be risk-averse and heavily regulated. Without direct leadership support, progress slows to a crawl. 

Lessons from real pilots 

During the COVID-19 pandemic, I helped lead a study that tested wearable sensors for early detection of infection. The devices tracked signals such as heart rate and temperature, and the model predicted infection with about 82 percent accuracy, often several days before symptoms appeared. The results were promising, but hospitals and regulators hesitated. Few were willing to advise patients that they could still be contagious even when they felt well. The experience showed that technical success alone does not guarantee adoption. Trust, regulation, and workflow readiness determine whether new tools are adopted. 

What executives must do differently 

AI in healthcare should not be treated as an isolated innovation project. It is a matter of leadership and strategy. Executives need to press vendors for pilot results that demonstrate how tools perform in real-world operations, rather than relying solely on polished whitepapers. They should focus efforts on service lines such as neurology, cardiology, and oncology, where even modest improvements affect both patient outcomes and financial performance. Building understanding across the organization is equally important. Clinicians, compliance officers, and board members all need a clear view of what artificial intelligence can and cannot do if trust is to grow. Planning for adoption must begin at the design stage, with interoperable records, aligned data flows, defined governance, and feedback systems that enable the models to evolve. AI can ease administrative loads and flag patients at risk of slipping through the cracks. It cannot replace empathy or bedside judgment. Those qualities remain the foundation of care and must be preserved. 

Moving forward 

Artificial intelligence will matter in healthcare only when strong technical work is matched by steady operational leadership. Success requires realism about what systems can handle and patience in aligning processes that were never designed to share information. Interoperability is central. Without it, algorithms remain confined to trials and pilots. With it, they can be integrated into daily workflows and deliver results that are visible to patients and providers. 

References 

1. Pew Research Center. How Americans View the Use of Artificial Intelligence in Health and Medicine. Pew Research Center; 2023. Available at: https://www.pewresearch.org/science/2023/12/12/how-americans-view-the-use-of-artificial-intelligence-in-health-and-medicine/
2. PubMed Central (PMC). Wearable Sensor Studies for Infection Detection. PubMed Central (PMC); 2023. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/