AI in healthcare: Do Patients Even Want It - or Are We Forcing the Future on Them?

Artificial intelligence is rapidly penetrating the U.S. and Canadian healthcare systems. Large urban hospitals in the U.S. report above 80–90% use of AI, from clinical documentation assistants to predictive analytics solutions. 59% of Canadian health leaders assert AI has decreased the time they spend on administration. In North America, the AI in healthcare market is expected to grow from $14.30 billion in 2024 to $249.91 billion by 2032.

However, many healthcare providers are not structurally or technically prepared for AI adoption due to legacy healthcare systems. Hospitals still run on systems that are 10-20 years old, data is fragmented, and compliance ambiguity around HIPAA, PHIPA, and emerging AI regulations adds concerns. 

Health-tech companies, investors, and healthcare institutions are rushing to implement AI for efficiency, automation, and competitive advantage through healthcare software modernization. But patients still lack awareness about the benefits AI brings directly to them. 60% of Americans say they would feel uncomfortable if their medical advisor were relying on AI delivering their healthcare services.

This is where the major gap lies: the demand for AI implementation is shaped by the industry, not by the end users. Correspondingly, when tech companies are building healthcare AI solutions, do they address the real patient needs or force them to accept the future they are not ready for? And how to make technology fit into the modern healthcare environment?

The Real Roadblocks to AI in Healthcare

We are still in the early stages of understanding AI's potential in healthcare, and this raises concerns about its performance and capabilities. Therefore, there’s a need to analyze the barriers on the way to AI implementation as a first step toward wise modernization.

Legacy Systems

Systems used in healthcare organizations across the U.S. and Canada today were built decades ago, long before the introduction of cloud infrastructure, modern APIs, mobile workflows, or advanced security standards. These platforms carry the following common challenges:

● hard to update and scale ● poor integration opportunities● lack of streaming/event infrastructure● I/O bottlenecks● no GPU/accelerator support● limited integration interfaces (HL7 v2 only, no APIs)

Even a minor enhancement like adding a new feature can take months, cost a fortune, and put the entire system at risk. With outdated core software, any AI undertaking can be disrupted before it even starts. 

Jason Merrick explains: 

“This creates a loop where you want to introduce a new technology, but you cannot do it because the system is outdated. The longer you wait, the more the system becomes outdated, and the more difficult it becomes to introduce any new technologies.”

The system is simply unable to support real-time data flows, modern security controls, or the compute requirements of AI models.

Siloed Data

Even if the technology stack is upgraded, most healthcare data is fragmented and unstructured. Pieces of information live in isolated systems that don’t interact well with each other. Healthcare data in modern institutions may comprise EMRs, lab systems, imaging platforms, billing software, scheduling tools, cloud file storage, and even paper forms. All of them contain pieces of the clinical picture, but don’t tell the whole story.

Blind spots in fragmented data don’t allow for adequate prediction, summarizing, or deriving clinical insights.

Igor Omelianchuk notes: 

“All of this makes building modern tools very difficult. AI is not plug-and-play. It needs clean, well-organized data pipelines, and building those pipelines requires modernization work, integration work, and strong compliance review.”

Unless data is properly connected and structured, and high-quality data pipelines are established, AI technology in healthcare cannot deliver safe and reliable results. 

Compliance fear

While the healthcare sector has one of the most demanding regulatory frameworks, the adoption of AI in medical systems introduces new questions around privacy, safety, and liability. The current regulatory standards include HIPAA and FDA in the U.S., PIPEDA and provincial health-information laws in Canada. The abundance of rules on local and federal levels makes companies worry that moving data to the cloud or integrating AI tools could accidentally cause violations.

Jason Merrick specifies:

“In reality, compliant cloud deployments are standard practice if properly architected (HIPAA-eligible services, PHIPA data residency, etc.).”

Eventually, many organizations choose to wait instead of modernizing. Legacy systems continue to age, processes remain inefficient, and innovation is postponed. And this happens not because of technology limitations but because decision-makers are wary of potential incompliances.

Why Many AI Projects Fail

Although AI in healthcare is causing excitement, a surprising number of projects fail, underperform, or never come into practice. The reason is not in technology, but in the mismatch between what companies build and what patients or healthcare institutions really need. 

Created for investors instead of end users

AI healthcare initiatives are often shaped to impress investors rather than serve the needs of people who are supposed to use these products daily. Startups are excited to introduce AI-empowered features, forgetting to check whether those features solve a real problem. 

Jason Merrick comments: 

“Companies are proving to investors that they have cool tools, but they do not directly generate more money because users are not really asking for these solutions, so there is no additional value.”

Hospitals may also fall into the trap of pursuing innovation, implementing models that look great but carry little advantage in practice.

Adding AI without fixing underlying problems

Another reason for failed initiatives is an attempt to build AI on top of broken or inefficient systems. With chaotic workflows, inconsistent documentation processes, or different data entry in various departments, you won’t fix the underlying issue by adding AI. Conversely, the issue is going to escalate. Teams may overlook that before AI in the healthcare industry can deliver meaningful benefits, they need to conduct a lot of cleanup, standardization, and redesign work. 

Igor Omelianchuk expands the idea: 

“Healthcare organizations need help modernizing, rewriting, migrating, and rebuilding the system safely and step by step.” This establishes the foundation for successfully validating and implementing the AI initiative.

Infrastructural gaps

Even a valid idea can stall if the infrastructure is not ready, which is often the case with legacy systems. Many healthcare platforms lack the compute power, data quality, integration architecture, or security controls required for AI workloads. When fast and reliable data streams are impossible, AI tools become slow and even unsafe, deteriorating the potential benefits of advanced technology. 

What AI Should and Should Not Do

As Artificial Intelligence in healthcare becomes more powerful, it requires a careful identification of its role. It must not replace medical advisors, but support them, reinforcing their capabilities, minimizing burnout, and raising operational efficiency. 

Safe adoption requires differentiation between where AI adds value and where it poses risks.

Where AI helps

The role of AI in healthcare is hard to overestimate in areas that involve repetitive, data-heavy, and time-consuming tasks. The correct application relieves administrative load and spurs internal processes without entering a terrain where clinical decisions are made.

● Documentation and admin work. A tremendous amount of clinicians’ manual tasks can be replaced with AI-empowered solutions. Studies reveal that doctors spend 69.5% less time documenting when using an AI scribe. Listening tools with natural language processing (NLP), automated note generation, and claim-preparation assistants drastically cut the time spent on charting. Healthcare providers save hours that can be used for patient care, while AI deals with documents and summaries.
● Triage, scheduling, imaging, analytics. Efficient workflow organization and diagnostics assistance are also the areas where smart technology can help.Triage tools direct patients to the appropriate level of care, while AI-enabled scheduling systems match patient demand to provider availability. AI also assists in diagnostics by quickly revealing anomalies, highlighting urgencies, and accelerating doctors’ interpretation. Analytical models uncover operational weaknesses, patterns in overall health conditions, and inefficiencies in the use of resources.

Where AI must not replace humans

Despite the expanding opportunities of AI in healthcare industry, there are spheres where it shouldn’t be used without human oversight. These are areas where doctors’ judgments, responsibility, and ethical considerations are irreplaceable by algorithms.

● Diagnoses, prescriptions, medical decisions. Regardless of the treatment complexity, final judgment must remain in the competence of a licensed professional. AI-empowered assistants can suggest possibilities, but the final decision regarding diagnoses or prescriptions is the territory of human doctors. Medical decision-making requires a deep understanding of context and an ethical perspective. Added the enormously high responsibility for results, these factors step beyond what modern systems can safely handle.● AI doctors pose unacceptable risk. Automated decision engines can endanger patients’ health in a way that the professional community considers unacceptable. Without human control, AI models may allow misdiagnosis and bias, complemented by a lack of responsibility. This makes them an unsafe path in AI health. A smart technology may support human doctors, but it must never supersede them.

Jason Merrick summarizes: 

“No AI doctors, or AI providers, whether that's NPs, PAs, MDs, or DOs. I am confident that every prescription, every healthcare diagnosis, every order should come from a human live provider.”

By clearly understanding the opportunities and limitations of healthcare AI technology, medical institutions and development companies can leverage it precisely where it benefits both patients and doctors, without inflicting needless risks where human expertise is irreplaceable.

How to Choose the Right AI Strategy

The cornerstone of the proper AI strategy in healthcare is not chasing innovations but focusing on real problems that impede patient care, efficiency, and organizational growth. To implement AI solutions successfully, you should take an analytical, well-structured approach that stems from internal operations, the current state of the system, data readiness, and compliance. 

Start with operational problems

AI strategy should begin with identifying the primary areas for improvement. 

As emphasized by Andrew Lychuk, 

“The safest approach is to start with your real operational problems, not the technology.”

The potential issues may include reducing documentation load, improving triage, expediting imaging processes, or accelerating scheduling. These are the specific points where AI can provide a remedy. Conversely, if teams forget about actual gaps in pursuit of advanced tech, they can doom their project to failure.

Check if your data is usable

Quality data is fundamental for AI tools. In legacy systems, information is often fragmented, stored in disconnected EMRs, outdated databases, and even paper forms. Healthcare companies running on aging platforms should first organize their data to make it structured and accessible, with connected data pipelines. Without this foundation, artificial intelligence applications in medicine may deliver poor, unreliable results.

Build with compliance from the very beginning

Regulatory compliance is one of the core pains in AI for healthcare. However, reliable development companies that specialize in the healthcare industry have found the solution. HIPAA, FDA guidelines, PHIPA/PIPEDA, and provincial or state regulations must be inherent in the architecture building process from the very beginning. If you try to incorporate compliance after development, you can inflict unnecessary risks, delays, and expensive redesigns. 

Igor Omelianchuk notes: 

“With the right architecture and safeguards, cloud and AI solutions can be just as compliant and secure as older on-premises systems, or even more secure.”

Andrew Lychuk adds: 

“Healthcare companies don’t need just developers. They need partners who understand how to modernize without breaking compliance and who can bring compliance experts, making the best use of modern technologies and guiding them through the entire process.”

Use flexible, modern architecture

To accommodate smart tools, your system must be adaptable, scalable, and ensure effortless integrations. Legacy systems are rigid and cannot handle real-time data streams or continual upgrades. 

That’s where strategic modernization enters the scene. The transformation to modular services, clean APIs, and cloud readiness guarantees that AI will operate properly and safely evolve with organizational or regulatory changes.

Ultimately, a strong AI strategy is much more than adding advanced tools. 

It’s an ongoing effort that implies a deep correspondence with real needs, and combines continual industry monitoring, analyzing trends and regulations, and actively assessing the existing systems with their vulnerabilities and flaws.

Final Thoughts on the Use of Artificial Intelligence in Medicine

Artificial Intelligence technology in healthcare can lead to extraordinary progress, but only with an appropriate foundation. The ability to address actual issues, system modernization, data readiness, built-in compliance, and human oversight are the necessary components of meaningful AI implementation.

Outdated systems, fragmented data, and vague compliance are some of the reasons why innovation slows down.

As Jason Merrick points out, organizations are often “building on top of a bad foundation” while rushing toward AI. If the system is not updated for AI adoption and the data isn’t properly structured, even the most advanced intelligent tools will perform poorly sooner or later.  

Andrew Lychuk highlights security and compliance aspects:

“Existing systems already have certain security risks. To rewrite them and create new ones, the development team needs to go through a compliance audit or check all the functionality through a compliance angle.”

Integrating compliance into every development stage is crucial in this sense, as it helps prevent risks and eliminate uncertainty.

And above all, AI technologies in healthcare must be implemented under human control. Smart tools can accelerate documentation, triage, operations, and analysis, but they must never supersede clinical judgment. Diagnoses, prescriptions, and medical decisions must be made by qualified professionals.

The benefits of AI in medicine and healthcare can be maximized through a comprehensive approach and continual effort. Find the real problem to solve, reinforce the foundation, modernize strategically, and let AI strengthen medical experts who care for patients.