The role of artificial intelligence (AI) in comprehensive treatment planning

Two dentists. One patient. Same radiographs. Two totally different treatment plans. As most readers would know, this is not hypothetical. Somewhere along the line, we confused variability with judgment. This has been romanticized as the art of dentistry, but that art can affect patients’ trust, case acceptance, and practice efficiency. As the French say, “On ne voit que ce qu’on regarde, et on ne regarde que ce qu’on a déjà dans l’esprit.” This roughly translates to the eyes cannot see what the brain does not know.

A treatment plan is just a portrait of the dentist who made it … what they learned in school … what they have seen in practice. Materials and approaches that arrived after dental school do not exist in their world. That is not a knock on anyone. Every dentist, no matter how decorated, is working within the borders of what they have seen.

Clinical circumstances narrow those borders further. A busy schedule, followed by a difficult patient, and a quiet accumulation of decision fatigue doesn’t make us incompetent. It makes us human. The patient in your chair has no idea any of this is happening. They trust that what they are being offered is the best option available. The honest question every clinician should sit with is this: Is it really?

Variability comes with a cost

Inconsistent treatment planning does not stay within the operatory. It travels. When patients hear different treatment options from different providers, it creates confusion. When a dentist doesn’t sound too sure of their recommendation, patients hesitate. And ultimately, it travels to the monthly report in the form of a production dip.

According to research by Henry Schein, case acceptance ranges around 50%–60% for established patients and 25%–35% for new patients.¹

A study going back to 1996, showed that treatment decisions are not only shaped by what a dentist sees but also what a dentist knows. A dentist’s education, experiences, background, and subjective biases shape the treatment decisions.²

A more recent scoping review was more direct: when clinicians treat the same case differently, patients pay the price in suboptimal care.³

In a solo practice, it shows up as inconsistent quality. If you scale it to a multiprovider practice or a DSO, the problem becomes something harder to fix entirely. It becomes an operational concern—one that mentorship or CE hours cannot touch. That requires systems.

How AI is helping in treatment decisions

While dealing with complex cases, we often reach out to a colleague. We send radiographs by text. We describe the cases over lunch or a phone call. We post photographs in a study club group chat. Our dental community is our most valuable resource and a direct acknowledgment that none of us knows everything.

This system is flawed and has its limitations. It depends entirely on who is available. It is subject to the same gaps and biases of whoever you reach.

Platforms like Trust AI and OpenEvidence are developing conversational clinical decision support systems that doctors can engage with directly.⁴ While they cannot replace the conversation with your colleague, they can provide an unbiased opinion backed by current evidence. A system trained on thousands of cases and journal publications does not carry the gaps in judgment. Medicine understood that years before dentistry did. Structured decision support improves diagnostic consistency and keeps the clinician in charge.⁵

Future of comprehensive treatment planning using AI

The FDA has cleared AI detection features for radiograph analysis.⁶ AI is making inroads into smile design software, prosthetic design, implant planning, insurance verification, patient communication, automated periodontal charting, and clinical notes. Every one of these tools does its job well. Overjet or Pearl can detect caries or bone loss.⁷ Perio charting software collects accurate data. Each platform lives in its own corner. There is no system that takes everything these tools know and connects them in a meaningful way. These tools reduced the guesswork in their individual lanes. They did nothing for the variability in the one that matters most. The dentists are still doing the mental math.

Every dentist knows this moment—a day you are running 30 minutes behind, an insurance flag at the front desk, and the kind old lady in the waiting room who is turning snippy. You look at the radiographs, do a quick exam, and make a call—probably a good one. But the same radiographs at 9 a.m. on a Tuesday might have told a different story.

Additional reading: Bridging gaps and building trust with dental AI

The human intelligence (HI) works with real constraints. Working memory can process only a limited number of variables simultaneously. A well--trained AI model can incorporate multiple variables such as age, gender, medical history, risk factors, and radiograph and photograph with evidence--based techniques and materials simultaneously.⁸ It can provide us with a list of all the possible scenarios. Treatment options will remain the same between different providers and offices. Patients can start trusting the profession again, and case acceptance can go up.

Case acceptance and practice transformation

All this ultimately comes down to a single moment: the patient sitting across from you. That decision is rarely about clinical details. Patients are not evaluating the sequence of treatment. They are deciding if the plan in front of them makes sense. Confidence is contagious. So is uncertainty.

Consistency is what makes confidence repeatable—not just for one clinician on a good day, but across providers, offices, and the full range of conditions a practice operates under. A structured treatment plan does not happen because a dentist happened to be well rested and unhurried. It happens because the system behind the dentist made sure nothing was missed.

The patient presented with a clear approach to full--mouth rehabilitation is a different patient than one handed a list of procedures with no context. One commits; the other hesitates. That difference shows up in case acceptance rate, scheduling consistency, revenue, and the quiet fact that a patient who trusts their treatment plan tends to refer their friends.⁹

Looking ahead

AI is beginning to help us understand the radiographic findings in the clinical context. The next frontier is helping dentists with clinical decisions, comprehensively and consistently. The role of AI should remain supportive. Dentists’ accountability is nonnegotiable.¹⁰

No single platform yet integrates all these inputs into a comprehensive planning framework, but the components exist.

Dentistry can feel like a battlefield, and we should use resources that give us better outcomes. Dentists are accountable for every decision they make, and AI should make it easier to arrive at a definitive treatment plan. Systematic planning builds patient confidence. Patient confidence drives case acceptance. Case acceptance drives practice growth. The barrier has always been consistency. That is exactly what AI is positioned to fix. 

Editor's note: This article appeared in the June 2026 print editon of Dental Economics magazine. Dentists in North America are eligible for a complimentary print subscription. Sign up here.

References

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2. Kay EJ, Blinkhorn AS. A qualitative investigation of factors governing dentists’ treatment philosophies. Br Dent J. 1996;180(5):171-176. doi:10.1038/sj.bdj.4809010
3. Murdoch AIK, Blum J, Chen J, et al. Determinants of clinical decision making under uncertainty in dentistry: a scoping review. Diagnostics (Basel). 2023;13(6):1076. doi:10.3390/diagnostics13061076
4. OpenEvidence. Accessed 2025. https://www.openevidence.com/
5. Shortliffe EH, Sepulveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199-2200. doi:10.1001/jama.2018.14670
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7. Krois J, Ekert T, Meinhold L, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8495. doi:10.1038/s41598-019-44839-3
8. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769-774. doi:10.1177/0022034520915714
9. Newsome PRH, Wright GH. A review of patient satisfaction: 1. Concepts of satisfaction. Br Dent J. 1999;186(4):161-165. doi:10.1038/sj.bdj.4800052
10. Mörch CM, Atsu S, Cai W, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100(13):1452-1460. doi:10.1177/00220345211013808