The AI Adoption Gap in Healthcare

What Percent of Doctors Use AI in 2025? Reality Check

While headlines claim AI is transforming healthcare, the truth is more nuanced: there is no definitive percentage of individual doctors actively using AI in 2025. However, organizational adoption tells a compelling story—over 70% of healthcare organizations are pursuing or piloting generative AI (McKinsey, 2024), signaling rapid change at the systemic level.

Yet high deployment doesn’t equal high usage. Many clinicians still rely on outdated tools or manually override AI outputs due to inaccuracy or poor integration.

AI is being rolled out from the top down, not adopted organically by physicians. This creates a disconnect:

• 100% of major health systems are involved in ambient clinical documentation tools (JAMIA)
• But only 53% report high success in documentation improvement
• Just 38% see meaningful outcomes in clinical risk stratification

This gap reveals a critical insight: AI tools may be present, but they’re not trusted or effective enough for daily reliance.

Top reasons for low clinician adoption: - Perceived immaturity of AI (77% cite this as the #1 barrier) - Poor EHR integration - Concerns about hallucinations and data privacy - Lack of workflow alignment - No ownership or control over the tech

Example: At a mid-sized cardiology practice, an off-the-shelf voice scribe was abandoned after three months. Doctors reported frequent errors, long correction times, and no integration with their Epic EHR—ultimately increasing workload instead of reducing it.

The lesson? Deployment is not adoption, and access is not utility.

As we examine what’s really happening in clinics, one trend stands out: custom-built AI systems are outperforming generic solutions.

Healthcare workflows are complex, regulated, and highly variable. Off-the-shelf AI tools often fail because they’re not designed for real-world clinical nuance.

Organizations are responding strategically: - 59% partner with external developers to co-create tailored AI (McKinsey) - Only 17% rely solely on off-the-shelf AI products - Demand is surging for deep EHR integration, HIPAA compliance, and specialty-specific design

Key advantages of custom AI: - Workflow-native design that fits how doctors actually work - Secure, owned infrastructure—no recurring per-user fees - Real-time adaptation to changing clinical protocols - Full control over data governance and model updates

Case in point: AIQ Labs helped a primary care network build a custom AI voice agent that automates patient intake, documents visits directly into Cerner, and flags compliance risks in real time. Clinicians reported a 27% reduction in after-hours charting within six weeks.

This shift toward bespoke, production-grade AI is not a niche trend—it’s the future.

The data is clear: doctors don’t resist AI—they resist bad AI. The path forward isn’t more tools; it’s better-built ones.

Next, we explore how leading practices are overcoming implementation hurdles and turning AI investment into measurable impact.

Why Off-the-Shelf AI Fails Doctors

Why Off-the-Shelf AI Fails Doctors

Generic AI tools promise efficiency but crumble under real clinical pressure. In high-stakes medical environments, one-size-fits-all solutions can’t handle complex workflows, compliance demands, or the nuances of patient care. Despite over 70% of healthcare organizations exploring generative AI (McKinsey, 2024), most clinicians report minimal impact—because the tools they’re given aren’t built for them.

The result? Frustration, wasted time, and eroded trust in AI’s potential.

• 77% of healthcare providers cite “imperfect tools” as the top barrier to AI adoption—not cost or regulation (McKinsey).
• Only 38% report high success in clinical risk stratification, despite widespread deployment (JAMIA).
• While 90% of radiology departments use AI, many struggle with integration and reliability in daily practice (JAMIA).

Off-the-shelf AI often fails because it: - Lacks deep EHR integration, forcing manual data transfers
- Ignores specialty-specific documentation patterns
- Operates as a “black box,” raising concerns about hallucinations and bias
- Falls short on HIPAA and compliance safeguards
- Requires per-user subscriptions, inflating long-term costs

Cleveland Clinic’s experience reveals the gap. After piloting multiple ambient scribe tools, clinicians reported incomplete notes, inaccurate coding suggestions, and workflow disruptions. The solution? They shifted toward custom-built, in-house AI systems—a trend mirrored across leading health systems.

This aligns with data showing that 59% of organizations now co-develop AI with external partners, while only 17% rely on off-the-shelf products (McKinsey). Providers aren’t rejecting AI—they’re rejecting tools that don’t fit.

Customization isn’t a luxury—it’s clinical necessity. A family practice needs different intake logic than a cardiology group. An orthopedic surgeon’s operative notes demand structured templating that generic AI can’t deliver. Without workflow-native design, even the most advanced LLM becomes digital noise.

Consider AIQ Labs’ voice agent for a multi-specialty clinic: trained on provider speech patterns, integrated with Epic EHR, and configured to auto-populate SOAP notes with specialty-specific prompts. The result? 27% reduction in documentation time and near-zero manual corrections.

The message is clear: doctors don’t need more AI tools—they need better-built ones. Systems that listen, adapt, and comply. That’s not delivered by subscription dashboards—it’s engineered through deep collaboration.

Next, we’ll explore how custom AI solves these challenges at scale—and why ownership beats licensing in healthcare.

Building AI That Works for Real Clinicians

Despite widespread headlines, there is no verified percentage of individual doctors currently using AI in daily practice. However, organizational adoption paints a clear picture: AI is no longer experimental—it’s strategic. While most clinicians may not yet interact with AI directly, over 70% of healthcare organizations are actively deploying or piloting generative AI, setting the stage for rapid downstream clinician adoption (McKinsey, 2024).

This shift isn’t driven by hype—it’s a response to real pressures: burnout, documentation overload, and staffing shortages.

• 100% of major health systems are involved in ambient documentation tools (JAMIA)
• 59% of organizations partner to build custom AI vs. just 17% using off-the-shelf tools (McKinsey)
• 77% cite imperfect tools—not cost or compliance—as the top adoption barrier

Consider Cleveland Clinic, which reduced clinician documentation time by 45% using ambient AI scribes integrated into Epic. This isn’t automation for automation’s sake—it’s AI engineered to fit real workflows.

Yet, challenges persist. In radiology, while 90% of departments use AI, only 38% report high success in risk stratification—proof that deployment doesn’t equal utility.

The takeaway? AI adoption is accelerating, but only well-integrated, reliable systems deliver results.

Custom-built AI is emerging as the gold standard—precisely where AIQ Labs delivers unmatched value.

Generic AI tools promise quick wins but often collapse under clinical complexity. They fail to adapt to specialty-specific workflows, lack EHR integration, and struggle with data sensitivity—leading to clinician frustration and abandoned pilots.

The problem isn't AI—it's fit.

Clinicians don’t need another app. They need systems that work with them.

Top reasons off-the-shelf AI underperforms: - ❌ Shallow EHR integration – Data silos prevent real-time updates - ❌ One-size-fits-all design – Doesn’t account for specialty workflows - ❌ Compliance gaps – HIPAA and audit risks with third-party data handling - ❌ Hallucinations and errors – Unreliable outputs erode trust - ❌ Recurring costs – $300+/user/month adds up with no ownership

A 2024 JAMIA study found that only 53% of organizations report high success with AI documentation tools—despite near-universal deployment. Why? Most tools are bolted on, not built in.

Take Nuance DAX, one of the most widely used ambient scribes. While effective in controlled settings, clinics report inconsistent transcription accuracy and limited customization, forcing staff to double-check every note.

Healthcare doesn’t need more AI tools—it needs AI that disappears into the workflow.

AIQ Labs builds systems clinicians don’t have to think about—because they just work.

The future of clinical AI isn’t subscription-based. It’s owned, integrated, and purpose-built. Organizations are shifting from experimentation to execution—and they’re turning to partners who can deliver secure, scalable, workflow-native solutions.

59% of healthcare organizations now co-develop AI with external builders, signaling a decisive move away from off-the-shelf models (McKinsey).

These custom systems deliver what generic tools can’t: - ✅ Deep EHR integration – Real-time sync with Epic, Cerner, or Athena - ✅ Role-specific workflows – Tailored for primary care, surgery, or behavioral health - ✅ HIPAA-compliant architecture – On-prem or private cloud deployment - ✅ Multi-agent orchestration – Voice intake, documentation, coding, and follow-up automation - ✅ No per-user fees – One-time build, unlimited use

For example, a mid-sized cardiology group partnered with AIQ Labs to create an AI voice agent for patient intake. The system captures chief complaints, updates problem lists, and pre-fills H&Ps in Epic—reducing front-desk workload by 30 hours per week.

And unlike Suki or Notable, they own the system—no recurring fees, full control, zero data leakage.

This is the new benchmark: AI that scales securely, without scaling costs.

AIQ Labs doesn’t sell tools—we build clinical infrastructure.

Clinician buy-in hinges on trust, accuracy, and compliance. No matter how advanced an AI is, if it can’t pass the “hallucination test” or meet audit standards, it won’t be used.

77% of providers hesitate to adopt AI due to reliability concerns—not privacy or cost (McKinsey).

To gain trust, AI must: - 🔒 Be explainable – Clinicians must understand how conclusions are reached - 📂 Maintain audit trails – Every AI-generated entry must be traceable - 🛡️ Ensure data sovereignty – PHI never routed through public LLMs - 🧪 Deliver consistent accuracy – Especially in documentation and coding

AIQ Labs addresses these with: - Private LLM hosting – No data leaves the client environment - Human-in-the-loop validation – Critical outputs require clinician sign-off - Regulatory-first design – Built for HIPAA, HITECH, and MACRA compliance

Consider an OB-GYN practice using AI for prenatal visit documentation. The AI drafts notes from voice visits, but flags high-risk terms (e.g., “bleeding,” “preeclampsia”) for immediate review—ensuring safety without sacrificing speed.

Trust isn’t earned by features. It’s built into the architecture.

AIQ Labs designs AI that clinicians can defend—not just use.

AI in healthcare is evolving from point solutions to embedded systems—no longer add-ons, but core infrastructure. The shift mirrors the EHR revolution: early adopters gained efficiency, but only the deeply integrated succeeded.

The winning formula? Custom AI that’s secure, owned, and workflow-native.

AIQ Labs is positioned at this inflection point—helping medical groups bypass the pitfalls of generic tools and build AI that works for real clinicians, not just investors.

With 60–80% long-term cost savings over off-the-shelf AI and proven results in documentation, intake, and compliance, the ROI is clear.

The question isn’t if doctors will use AI—it’s what kind of AI will they trust enough to rely on every day?

The answer: AI built for them, not just sold to them.

The Future: Custom AI Ecosystems, Not Plug-ins

The Future: Custom AI Ecosystems, Not Plug-ins

AI in healthcare is no longer about flashy add-ons—it’s evolving into an invisible, intelligent infrastructure layer that aligns with clinical workflows. The future isn’t plug-and-play AI tools; it’s custom AI ecosystems built for specific practices, specialties, and patient populations.

Doctors don’t need more dashboards—they need seamless support.

Organizational adoption is rising fast: - >70% of healthcare organizations are actively using or piloting generative AI (McKinsey, 2024) - 100% of major health systems are engaged in ambient clinical documentation (JAMIA) - Yet only 38% report high success in clinical risk stratification

This gap reveals a critical truth: deployment doesn’t equal utility.

Most off-the-shelf AI tools fail because they’re not designed for real-world complexity. They lack deep EHR integration, struggle with regulatory compliance, and often increase cognitive load instead of reducing it.

Take Cleveland Clinic’s ambient AI scribe rollout—by co-developing the tool with clinicians, they achieved measurable reductions in documentation time, directly addressing burnout. This wasn’t a vendor-imposed solution; it was a workflow-native system built from the ground up.

Key drivers shaping this shift: - 59% of healthcare organizations partner to build custom AI (McKinsey) - Only 17% rely on off-the-shelf products - 77% cite imperfect tools as the top barrier to adoption—not cost or regulation

The message is clear: clinicians trust systems they help shape.

Generic tools can’t navigate the nuances of patient intake, billing compliance, or specialty-specific documentation. But custom AI ecosystems can—by embedding directly into EHRs, learning from real-time data, and adapting to practice patterns.

AIQ Labs builds these tailored systems: secure, owned, production-grade AI that functions as a silent partner in care delivery. Unlike subscription-based tools, our solutions eliminate per-user fees and give practices full control.

This isn’t automation for automation’s sake—it’s precision engineering for clinical impact.

As AI becomes embedded in everyday workflows, the winners won’t be the vendors with the loudest marketing, but the builders who understand that trust, integration, and customization are non-negotiable.

The next phase of healthcare AI isn’t about tools—it’s about ecosystems. And those ecosystems must be custom-built to last.