AI Is Easy. Adoption Isn’t: The Real Bottleneck in Digital Health
Artificial intelligence in healthcare has never been more accessible. Low‑ and no‑code tools mean almost anyone can spin up a model or prototype an agent, yet inside hospital systems, most AI never makes it past a pilot or stays at the edges of care. The challenge is no longer building AI—it is earning a durable place for it in everyday clinical practice.
From Breakthrough to Routine Use
We interviewed Dr. Tina Manoharan (experience leading teams at Siemens Healthineers, Philips, and Evident) who has seen AI move from heavy, bespoke systems that demanded massive datasets and complex infrastructure to today’s flexible platforms and large language models that almost anyone can configure.
“Building AI has become easy. Making it part of daily clinical routine is the hard part.”
The hype usually diverges from reality when spectacular results on small, curated datasets or at a single marquee reference center are presented as broadly transformative. A model that shines in one academic hospital can struggle across different scanners, geographies, and patient populations. It may be a genuine research breakthrough, but that does not automatically make it a scalable clinical product.
Why Adoption Is Still Hard in 2026
Tina defines adoption in demanding, practical terms: physicians using an AI solution in clinical routine, every day, for every relevant patient, with the system working reliably and measurably improving outcomes while lowering costs and supporting staff. By that standard, far fewer AI systems count as truly “adopted” than marketing suggests.
Common reasons AI fails to scale include:
Underestimating the complexity and variability of hospital workflows.
Assuming a couple reference sites represent all hospitals or regions.
Relying on nicely curated data that do not reflect messy real‑world practice.
“If you don’t understand the workflow, you don’t understand the problem you’re solving.”
Where AI Breaks Down
When AI fails in healthcare, the root cause is rarely the algorithm alone. Tina sees several recurring bottlenecks.
Point solutions without an ecosystem
Many vendors build narrow algorithms—one for lung, another for breast, another for liver—without considering how hospitals will manage and integrate dozens of separate tools. Without a unifying platform or “app store” approach, IT teams struggle to deploy, monitor, and update AI at scale.Shallow integration into clinical systems
AI outputs often live in separate viewers or portals and do not flow into the EMR (patient record) or reporting systems at the right time or in the right place. If a report does not appear naturally in the patient record where clinicians expect it, they will not rely on it in daily practice.No lifecycle or monitoring
Models launched with strong performance may drift as guidelines change, hardware is upgraded, or patient populations shift. Tina describes cases where AI worked beautifully for a year, then quietly degraded because no one was monitoring or retraining it. “Deployed and forgotten” AI is risky in a clinical environment.Value that doesn’t justify disruption
If a solution is expensive and only saves a few seconds per case, it will not survive procurement and budgeting. Hospitals need tangible value: more cases processed, more complex cases addressed, fewer repeat exams, shorter turnaround times, or clearly improved outcomes and staff experience. Another valuable metric to consider if TCO, or total lifecycle cost of patient management.
“In healthcare, AI is not a one‑off project—it’s a lifecycle you have to own.”
Clinicians, Trust, and the Reality of Workflows
AI builders often underestimate how many different stakeholders shape adoption. Tina notes that teams frequently co‑design with an enthusiastic end user but overlook department heads, IT, purchasing, and health‑system administrators, each with different incentives and definitions of value.
“You don’t sell to one clinician—you’re entering a whole ecosystem.”
She also emphasizes that trust is built more on reliability than on explanations alone. Clinicians need systems that are up, fast, and consistent across time and patient cohorts. If a tool is down during busy hours or behaves unpredictably after a silent update, trust evaporates quickly.
Explainability and transparency still matter—especially clarity on training data, performance boundaries, and confidence levels—but they build on a foundation of reliability and high uptime.
“Explainability helps, but if it isn’t reliable, no one cares how beautifully you explain it.”
Rethinking Value and ROI
Traditional ROI calculations often undercount the value of AI in healthcare because they focus narrowly on immediate financial gains cost savings. Tina encourages leaders to look at avoided downstream costs, reduced repeat exams, improved throughput, and lower burnout alongside financial metrics.
She also highlights “return on insight”: AI that enables clinicians to handle more complex cases, expand screening, or shift time from routine tasks to higher‑value work. However, incentives—reimbursement, productivity targets, and risk‑sharing models—must evolve to reward these gains, or even strong AI solutions will stall in adoption.
“If your incentives don’t change, your AI will hit the same old walls.”
What Successful Adoption Will Look Like
In the coming years, successful AI in healthcare will likely be quiet and embedded rather than flashy and separate. Tina envisions solutions that are:
Seamlessly integrated into clinical workflows so clinicians barely notice they are using “AI.”
Acting as a sparring partner—triaging cases, pre‑screening routine work, supporting differential diagnosis and precision medicine—while clinicians retain ultimate responsibility.
Managed via platforms that allow hospitals to combine third‑party tools with models they build or fine‑tune themselves for local needs.
Radiology (where digital images and reimbursement frameworks are more mature) is already out in front, with pathology not far behind and other areas following as the data infrastructure and incentives catch up.
For innovators and healthcare leaders alike, the mindset shift is to treat AI not as a one‑time technology purchase but as a long‑term, collaborative capability. When the focus moves from building models to delivering reliable, workflow‑native, lifecycle‑managed value, AI stops being “easy” in theory and becomes indispensable in practice.
This article is based on an interview with Tina Manoharan, AI Advisor to Factor 7 Medical. For more resources on digital health and MedTech innovations, explore more blogs below.
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