AI Adoption Is Splitting Healthcare Into Uneven Tiers
Amanda DeMano argues that healthcare is entering a widening divide: hospitals with money, talent, and technical infrastructure are increasingly able to put artificial intelligence to work, while organizations without those advantages risk falling further behind. Her central warning is blunt. Without deliberate collaboration, AI will not simply add efficiency to a stressed system; it may deepen the gap between institutions that can operationalize it and those that cannot.
The issue is not abstract. In healthcare, access to new technology rarely depends on technology alone. It depends on capital, informatics staffing, data quality, governance, legal review, and the operational muscle needed to move a tool from pilot to practice. A hospital can purchase software and still fail to realize meaningful clinical value if its workflows are fragmented or its personnel are already stretched thin. In that setting, AI becomes another layer of complexity rather than relief.
DeMano’s warning lands because the mismatch is already visible. Large health systems and well-resourced hospitals can recruit specialized personnel, build the necessary digital foundation, and test tools at scale. Smaller hospitals, rural institutions, and safety-net organizations may be expected to adopt the same technologies without comparable support. The result is not merely slower uptake. It can become compounding divergence, where the organizations best positioned to deploy AI improve faster while the rest remain burdened by manual work, narrower margins, and fewer opportunities to modernize.
The risk goes beyond efficiency metrics. AI in healthcare touches triage, documentation, scheduling, imaging interpretation, population management, and administrative decision-making. When one organization can automate burdensome tasks and another cannot, the difference can affect clinician workload, appointment access, response times, and ultimately patient experience. Systems with stronger infrastructure may also be better positioned to validate algorithms, monitor for errors, and integrate tools safely. Those without that foundation may face a more difficult choice: adopt too quickly and accept operational risk, or delay and watch the gap widen.
There is a familiar pattern here. New clinical technologies often appear neutral at introduction and unequal in effect once they meet real-world budgets, staffing shortages, and uneven digital maturity. AI may repeat that pattern unless the healthcare field treats implementation as a shared challenge rather than a competition between individual institutions. A tool that requires sophisticated data pipelines and ongoing oversight will predictably favor organizations that already have both.
Collaboration is the mechanism DeMano places at the center of the response. In practical terms, that means hospitals, health systems, vendors, investors, and potentially policy leaders working to reduce the burden of entry for organizations with fewer resources. Shared infrastructure, common implementation frameworks, pooled expertise, and more realistic deployment models could limit the extent to which AI becomes a privilege of large institutions alone. Without such coordination, the market may reward the hospitals that can move fastest, not necessarily the ones serving the most vulnerable patients.
The clinical implications are easy to miss if AI is discussed only as a software story. Healthcare technology changes who gets help first, who waits longer, and whose staff can spend more time on direct care instead of repetitive administration. In a hospital already under strain, even modest gains in automation may matter. But when only a subset of hospitals can access those gains, the distribution of benefit becomes as important as the technology itself.
One sentence captures the problem clearly: AI can reduce strain in one hospital while magnifying strain in another.
That possibility should concern anyone focused on health equity. If the tools most likely to improve efficiency, standardize routine tasks, and support clinical decision-making become concentrated in the best-resourced systems, then the existing hierarchy in healthcare delivery may harden rather than soften. Patients do not experience infrastructure in the abstract; they experience access, wait times, staffing responsiveness, and the quality of the systems they enter. Technology that changes those conditions unevenly can widen disparities even when its intent is broadly beneficial.
The article frames collaboration as necessary not because it is idealistic, but because the alternative is structural divergence. AI adoption is already moving quickly enough to reward organizations with a head start. The unanswered question is how much the field is willing to invest in shared pathways before the advantage becomes self-reinforcing. If hospitals are left to absorb the cost, staffing demands, and governance burden on their own, the divide DeMano describes will likely deepen instead of narrow.
The most concrete challenge now is implementation capacity, not algorithmic novelty. Healthcare already knows how quickly access can splinter when new capabilities arrive unevenly. AI is no exception, and the institutions least able to absorb another operational burden are the ones most likely to be left behind.