AI Dental Diagnostics: The Clinical Reality Behind the Hype
After months of using AI diagnostic tools in my practice, here's what actually works, what still needs improvement, and why I'm cautiously optimistic about the future.
Everyone’s talking about AI in dentistry. The marketing materials promise revolutionary detection capabilities, improved accuracy, and enhanced patient communication. But what’s it actually like to use these tools on real patients, day after day?
I’ve spent the past several months integrating AI diagnostic assistance into my clinical workflow. Here’s my honest assessment—the wins, the frustrations, and the potential I see on the horizon.
What AI Diagnostic Tools Actually Do
Before diving into specifics, let’s clarify what we’re talking about. Current AI dental diagnostic platforms primarily analyze radiographic images to:
- Detect carious lesions at various stages
- Identify periapical pathology
- Measure bone levels for periodontal assessment
- Flag calculus and other findings
- Quantify existing restorations and their margins
The AI doesn’t make treatment decisions—it highlights areas of concern and provides measurements that supplement clinical judgment. Think of it as a second set of eyes that never gets tired or distracted.
The Genuine Wins
Consistency in Detection
Here’s something I didn’t expect: the AI catches things I occasionally miss, but not in the way you might think. It’s not finding pathology I wouldn’t have eventually noticed—it’s preventing the occasional oversight that happens during a busy day.
When you’re seeing 20+ patients and reviewing hundreds of surfaces, human attention naturally fluctuates. AI doesn’t have that problem. Every image gets the same level of scrutiny, whether it’s the first patient of the day or the last.
Patient Communication Transformed
This is where AI has exceeded my expectations. When I can show a patient a visual overlay highlighting an area of concern with a confidence score, the conversation changes entirely.
Instead of pointing at a grainy radiograph saying “see this shadow here?” I’m presenting objective measurements. The skeptical patient who questioned every diagnosis? Now they’re seeing quantified data that supports my clinical findings.
Treatment acceptance hasn’t just improved—the quality of patient understanding has improved. They’re making informed decisions rather than trusting blindly.
Calibration for the Entire Team
We’ve started using AI findings as a calibration tool for our hygienists’ radiographic assessments. When the AI and a team member disagree, it creates a learning opportunity. Not because the AI is always right—it isn’t—but because it forces us to articulate why we see something differently.
The Honest Limitations
Sensitivity vs. Specificity Tradeoffs
Current AI tends to err on the side of sensitivity—meaning it flags more potential findings to avoid missing pathology. This is probably the right design choice, but it means you’ll see false positives.
Some of these are obvious: the AI occasionally interprets anatomical variations as pathology, or flags early demineralization that I’d classify as “watch” rather than “treat.” This is where clinical judgment remains essential.
The danger isn’t the false positives themselves—it’s the risk of “alert fatigue” if clinicians start dismissing AI findings without careful evaluation.
Image Quality Dependency
AI can only analyze what it can see. Poor exposure, positioning errors, or artifacts significantly impact accuracy. Garbage in, garbage out.
This has actually pushed us to improve our radiographic technique. When you know an AI is analyzing every image, there’s added motivation to get it right.
Integration Friction
This varies by platform, but workflow integration remains a challenge. Some systems require separate image uploads; others integrate more seamlessly. The extra clicks add up over a busy day.
The ideal future is AI analysis happening automatically in the background, with findings appearing alongside the image without any additional steps. We’re not quite there yet.
What I’m Watching For
Longitudinal Analysis
The most exciting potential isn’t single-image analysis—it’s tracking changes over time. Imagine AI that compares today’s radiograph to the one from six months ago and quantifies exactly how a lesion has progressed (or stabilized).
Some platforms are starting to offer this. When it works well, it’s genuinely valuable. You’re not relying on memory or hunting through chart notes—the progression is visualized and measured.
Multi-Modal Integration
Current AI focuses primarily on radiographs. But what happens when we can integrate intraoral photos, scanner data, and radiographs into a unified analysis? The diagnostic picture becomes much richer.
I expect to see platforms moving in this direction. The technical challenges are significant, but the clinical value would be substantial.
Predictive Analytics
This is further out, but potentially transformative. AI that doesn’t just identify current pathology but predicts risk—which teeth are most likely to develop problems, which patients need more aggressive preventive protocols.
The data to build these models is being collected now. The clinical applications are coming.
Practical Recommendations
For dentists considering AI diagnostic integration:
Start with realistic expectations. This is a tool to augment your clinical judgment, not replace it. If you’re hoping AI will eliminate the need for careful radiographic interpretation, you’ll be disappointed.
Evaluate your workflow honestly. How much friction can you tolerate? Some platforms require more manual steps than others. Trial periods are essential.
Use it as a teaching tool. Whether for team calibration or patient education, the visual outputs are valuable beyond pure diagnostics.
Track your outcomes. Are you finding more pathology? Is treatment acceptance improving? Without data, you’re just guessing at the value.
The Bigger Picture
AI diagnostic tools aren’t going to revolutionize dentistry overnight. But they represent a meaningful step forward in how we leverage technology to improve patient care.
The companies building these platforms—Overjet, Pearl, and others—are solving real clinical problems. The technology will continue to improve. The integration will become smoother. The evidence base will grow.
My take: this isn’t hype. It’s early-stage technology that’s already providing value, with a trajectory toward becoming indispensable. The dentists who start building fluency with these tools now will have an advantage as the technology matures.
The question isn’t whether to adopt AI diagnostics. It’s how to integrate them thoughtfully into your practice—maintaining clinical judgment while leveraging what these tools do well.
I’d love to hear from other practitioners using AI diagnostic tools. What’s your experience been? What am I missing? Reach out via the contact page or find me on social media.