AI in dentistry and dental studies

Table of Content:

Fifth Hand in Dentistry and Dental Studies AI

Okay, so I want to be honest about something. We all heard about four-hand dentistry and not the fifth hand. When I first started hearing about AI in dentistry, I kind of rolled my eyes. It felt like one of those buzzwords that professors throw around in lectures to sound current, but then nothing actually changes in the clinic. You still spend your afternoons struggling with rubber dam clamps and trying not to nick the gingiva during crown preps. AI felt like a very far-away problem.
But the more I look into it, the more I realize I was wrong to brush it off. And I think many dental students are in the same position. They are so focused on surviving their preclinical years, memorizing tooth morphology, getting their cavity preparations graded, and passing OSCEs, that they do not really stop and think about what the profession is going to look like when they actually graduate and start practicing. That is a mistake, because things are changing fast, and it is better to understand it now than to be blindsided by it five years into your career.
So I want to walk through what AI is actually doing in dentistry right now, what it means for those still in dental school, the concerns, and what the future probably looks like. This is not meant to be a scary article, and it is not meant to be an overly optimistic one either. I just want to think through it honestly

What Even Is AI in the Context of Dentistry

Before getting into the specific applications, it helps to understand what we mean when we say
‘AI’ in a dental setting. When researchers and clinicians talk about it, they are mostly referring to
machine learning and a subset of it called deep learning. These are systems that are trained on
enormous datasets, for example, hundreds of thousands of dental X-rays, and they learn to
recognize patterns in that data over time. The more data they are trained on, the better they get
at identifying things like caries, bone loss, root fractures, and lesions.

There is also a concept called convolutional neural networks, which is a specific type of deep
learning architecture that works best with image data. This is what powers many dental imaging
AI tools that clinics are starting to adopt. It essentially mimics the way the human visual cortex
processes images, but it can do it much faster and without fatigue.
The other thing worth knowing is that AI in dentistry is not just about images. It also covers
natural language processing, which supports patient communication and administrative tasks,
and predictive analytics, which helps identify patients at higher risk of developing certain
conditions before those conditions appear.

AI in Dental Imaging and Radiology

This is the area where AI has made the most progress and, honestly, where the evidence is the strongest. As dental students, you spend a huge amount of time learning how to read radiographs. Bitewings, periapicals, panoramic films, you are drilled on how to identify proximal caries, alveolar bone levels, calculus, periapical pathology, root resorption, and everything else. It takes years to get genuinely good at it because pattern recognition develops with experience. What AI tools are doing now is offering a kind of second opinion on those images in real time.
Studies have shown that at the individual-tooth level, AI platforms have achieved accuracy rates of over 94.9 percent across tasks such as detecting missing teeth, crowns, and pontics, performance that rivals or even exceeds that of human specialists in specific radiological tasks.
That is a genuinely impressive number. But here is the part that I think gets glossed over in a lot of the enthusiasm around these tools. When researchers tested AI using a stricter patient-level standard, meaning the AI had to be completely correct across the entire dentition rather than
just on individual teeth, the accuracy dropped to around 56.5 percent. That is a significant gap,
and it tells us something important. AI is very good at discrete, isolated tasks, but it still struggles with the kind of holistic, big-picture clinical thinking that a dentist does when they sit down with a patient and start connecting dots across a whole mouth.
Still, even as a supplementary tool, this is valuable. The idea is not that the AI replaces the
dentist’s diagnosis but that it acts as a second set of eyes. Imagine having a system that flags
something on a radiograph you might have missed because you were tired at the end of a long clinic session, or because the lesion was subtle. That kind of safety net is genuinely useful in practice.
For dental students, this also changes how you should think about radiology training. It is no
longer enough to just learn to identify pathology in images. You also need to understand what AI tools can and cannot do, how to interpret the flags they generate, and when to trust them versus when to override them based on clinical context. That is a new kind of skill that did not really
exist in dental education five years ago.

Treatment Planning and What AI Does There

Beyond imaging, AI is starting to make real inroads in treatment planning. This is the part of dentistry that requires synthesizing a large amount of information about a patient and turning it into a logical, evidence-based plan that accounts for their specific situation, priorities, systemic health, finances, and oral health trajectory over time.

What AI does here is help clinicians organize and cross-reference all of that data faster than a human can do it manually. These systems can pull together a patient’s imaging data, medical history, existing restorations, risk factors for caries and periodontal disease, and even their previous treatment responses, and generate personalized recommendations that align with current evidence-based protocols.

In orthodontics, especially, this has been transformative. Al can simulate treatment outcomes before a single bracket is placed or a set of aligners is ordered. That means the patient can see a visual prediction of what their teeth will look like at the end of treatment, making it much easier to have an informed conversation about options. It also helps the clinician identify cases in which one treatment approach is likely to be more stable in the long term than another.

In orthodontics, especially, this has been transformative. AI can simulate treatment outcomes before a single bracket is placed or a set of aligners is ordered. That means the patient can see a visual prediction of what their teeth will look like at the end of treatment, making it much easier to have an informed conversation about options. It also helps the clinician identify cases in which one treatment approach is likely to be more stable in the long term than another.

For those of you in dental school, I think treatment planning is one of the areas where AI will change how you are taught. Currently, treatment planning is largely learned through case presentations and supervisor feedback. You present a case, you get grilled on it, and over time, you develop better judgment. AI could supplement this by giving students immediate, evidence-based feedback on their proposed treatment plans and flagging areas where they might be missing something or where a different approach has a better track record in the literature.

iTero NIRI Technology and What It Actually Does

Okay, so this is the one that genuinely blew my mind when I first properly understood it. Most of us have heard of intraoral scanners by now. But the iTero Element 5D is not just a scanner. It is also carrying near-infrared imaging technology, NIRI, built directly into the same wand, and that changes quite a lot about how we can detect early caries without ever taking a radiograph.

Here is the science behind it in plain language. Near-infrared light operates at around 850 nanometres on the electromagnetic spectrum. When this light hits healthy enamel, it passes through relatively easily because healthy enamel is translucent. But when it hits dentin or an area of carious demineralization, those tissues reflect more of the infrared light back because they are less translucent. The scanner picks up those differences in reflectance and translates them into a greyscale image where brighter areas indicate less translucency, meaning dentin or a developing carious lesion, and darker areas indicate healthy enamel.

What this means practically is that you can visualize interproximal caries in real time, during a scan, without exposing the patient to ionizing radiation. For a patient who is already anxious about X-rays, or a pregnant patient, or a young child for whom you are trying to keep radiation dose as low as reasonably achievable, this is a genuinely significant alternative pathway.

The research behind it is fairly solid. A multisite clinical study conducted across five clinics in Germany and Canada compared iTero NIRI technology against bitewing radiographs across 3,502 posterior proximal tooth surfaces in 102 patients, and the NIRI system showed 96.9 percent agreement with bitewing diagnoses. That is a number worth sitting with, because bitewings are currently your gold standard for interproximal caries detection in routine practice.

There are nuances, though, and as students, you should know them. Research has shown that after rigorous training and calibration, NIRI can be used with moderate reliability, high specificity, and moderate sensitivity to detect noncavitated interproximal carious lesions. The high specificity is good news because it means the system is reliable at correctly identifying healthy surfaces. The moderate sensitivity means it does not catch every lesion every time, particularly very early enamel lesions. NIRI alone had significantly greater reliability and validity than bitewing radiography alone, which showed poor to fair agreement in one study evaluating the detection of noncavitated posterior interproximal caries.

What I find particularly interesting is that the system is based on the principle that healthy enamel is translucent, while dentin and carious lesions reflect more light and are less translucent, with the NIRI greyscale image showing structures at various levels of translucency with different brightness levels. Understanding that principle is important because it means the clinician must interpret the image rather than simply accept whatever the system flags. Bright does not automatically mean caries. You still need clinical context, the patient history, their diet, their plaque control, and whether there is a clinical cavity to correlate with the NIRI finding

The iTero system is also the first intraoral scanner to simultaneously record 3D optical scans, colour intraoral images, and NIRI images in a single pass. Previously, you would need separate devices for each of those things. Having all three captured in a single scan session, without additional radiation, and within a genuinely comfortable time frame for patients, is a meaningful step forward in how comprehensive a single appointment can be.

For students entering orthodontics or general practice, NIRI will become a routine part of the initial examination workflow. Learning to interpret these greyscale images is therefore worth investing time in now, before you are standing in front of a patient trying to explain why their tooth looks bright on a screen.

The iTero Digital Passport and Longitudinal Monitoring

This is the part that I think has the biggest implications for how we think about patient records and long-term care, and it is honestly underappreciated in most conversations about digital dentistry.

When a patient comes to a practice that uses an iTero scanner, every scan that is taken gets stored and linked to that patient’s profile. Over time, this builds what is effectively a longitudinal 3D archive of everything that has changed in their mouth. That is the foundation of iTero’s TimeLapse technology, and when you combine it with the Align Oral Health Suite software, it becomes genuinely powerful.

Using iTero TimeLapse, clinicians can compare the current scan with prior scans and highlight changes in gingival margins. In documented clinical cases, significant recession was observed in just 13 months, and the side-by-side 3D compare tool was useful for illustrating recurrent oral hygiene issues between appointments. Think about what that means for a patient who comes in every six months for a hygiene visit. Instead of relying solely on probing depth measurements, which are inherently subject to operator variability and patient tolerance, you can pull up two scans side by side and show the patient exactly where their gumline has shifted and by how much. That is a completely different kind of conversation.

The iTero system can monitor the evolution of a patient’s oral health, with clinical applications that include displaying consequences from bruxism, tooth erosion, gingival recession, and plaque buildup, and it automatically compiles the main scans and views, as well as any screenshots taken during consultation, into a personalized report that can be shared with the patient electronically. So the patient leaves the appointment with a document they can refer back to that clearly shows what has changed and what needs attention.

For caries specifically, the ability to track progression over time changes the clinical calculus around intervention timing. If a NIRI scan from six months ago showed an early enamel lesion and this month’s scan shows it has progressed toward the dentamoenamel junction, that longitudinal data supports a case for intervention that would be much harder to make from a single observation alone. Equally, if a lesion has remained stable or shows signs of remineralization following dietary changes and fluoride application, that is evidence supporting continued monitoring rather than immediate operative treatment. This is genuinely aligned with the philosophy of minimally invasive dentistry that most dental schools now teach.

Beyond caries and recession, the digital record tracks tooth wear from bruxism or acid erosion, monitors orthodontic treatment progress, records the position and alignment of teeth before and after any intervention, and flags changes in soft tissue morphology. The Align Oral Health Suite opens a view of the patient’s scan data that looks at tooth health, including caries, broken teeth, and failing old restorations, and a gum health view that, in monochrome mode, shows the patient areas of gingival recession and abfraction lesions.

Now, here is the orthodontic side of this that really matters for those of you interested in that field. The OrthoCAD software, integrated with the iTero system, can automatically perform a range of analytical measurements from the digital model. Clinicians can take measurements of tooth width, space, T-J Moyers, Bolton analysis, arch width, canine distance, and overbite/overjet, including point-to-point, point-to-plane, and plane-to-plane measurements.

Bolton analysis is something you all learn about in dental school, and most of you can calculate it manually in an orthodontic clinic using calipers on a plaster model, which takes time and is subject to measurement error. Having the system automatically calculate Bolton ratios from a digital scan in seconds, with consistent precision, is a practical upgrade that also removes a potential source of error from the diagnostic process. The OrthoCAD suite can also generate a Computerized Discrepancy Index and perform Phase III Grading, which means the orthodontic analysis that used to require separate model analysis appointments can be completed as part of the initial digital scan workflow.

With TimeLapse, dentists can monitor changes in tooth alignment, wear, and gum health by visualizing past and present scans as animated images, which is particularly valuable for tracking treatment progress and identifying emerging dental issues early. The visual feedback enables patients to participate in their own dental care by clearly seeing any changes.

What this effectively creates is a digital health passport for each patient’s mouth. Every scan adds another data point to a longitudinal record that can be reviewed, compared, and shared across providers. If a patient moves to a different city or needs to see a specialist, their complete digital oral health history can travel with them. That is a fundamentally different model of dental records than the combination of paper notes and old bitewings that has been the norm for decades.

For dental students, understanding how to use these longitudinal datasets will be a core clinical competency. The question will not just be “what do I see in this mouth today” but “what has changed since the last time this patient was scanned, and what does that change tell me about their oral health trajectory?”

AI in Periodontal Classification and Diagnosis

Periodontal diagnosis is one of the most complex things you do in dentistry, and I say that as someone who has spent a fair amount of time seeing a dentist in a perio clinic, trying to have a coherent conversation with the patient while bleeding scores, furcation grades, and bone level assessments are being recorded. The 2017 World Workshop classification system we now use, which stages and grades periodontitis based on severity and complexity, risk factors, and rate of progression, is genuinely more nuanced than what came before it. But that nuance also means there is more room for inconsistency between clinicians when they apply it.

This is exactly the kind of problem AI is well-suited to help with.

A systematic review published in 2025 found that AI’s diagnostic capabilities are comparable to those of a general dentist or periodontist, achieving an overall diagnostic accuracy rate of over 70 percent for periodontitis classification, with some models reaching 80 to 90 percent. Those numbers are meaningful because they suggest AI can function as a reliable first-pass screening tool, flagging cases that need specialist review and helping less experienced clinicians apply the classification system more consistently.

The radiographic side of periodontal diagnosis is where deep learning has made particular progress. Measuring alveolar bone levels on panoramic and periapical radiographs is technically demanding and requires careful identification of the cementoenamel junction and the alveolar bone crest. A deep learning hybrid framework was developed specifically to detect radiographic bone level and CEJ level on panoramic radiographs and then automatically classify periodontal bone loss according to the 2017 World Workshop staging criteria, achieving a Pearson correlation coefficient of 0.73 overall for the whole jaw and an intraclass correlation value of 0.91 compared with diagnoses made by radiologists. An intraclass correlation of 0.91 is excellent, suggesting this is not just a research curiosity but something that could be genuinely clinically useful.

AI models using patient-related data, signs and symptoms of the disease, immunological biomarkers, and microbial profiles aid in effective diagnosis and planning treatment for periodontal disease, and AI is being used for patient data segregation, identification of anatomical landmarks, diagnosis and grading of periodontal or peri-implant disease and conditions, and assessment of clinical features like pocket depth measurement, extent of bone loss, and prognosis of periodontal treatment.

That last point about pocket depth measurement is particularly interesting to me, because automated periodontal probing has been a kind of holy grail in periodontics for a while. Standard manual probing is operator-dependent, affected by probe angulation, probe force, the degree of gingival inflammation, and patient tolerance. AI-assisted probing systems that standardize the measurement process and reduce variability could make periodontal charting more reproducible and more comparable across time points and providers.

Deep learning algorithms such as convolutional neural networks and segmentation techniques demonstrated superior performance in detecting periodontal conditions, with accuracy rates surpassing 90 percent in some studies, and advanced models such as Multi-Label U-Net exhibited high precision in radiographic analyses, outperforming traditional methods while also facilitating predictive analytics for disease progression and personalized treatment strategies.

The predictive analytics piece matters a lot for how we think about periodontal maintenance. One of the ongoing challenges in periodontology is identifying which patients are likely to progress rapidly and which ones can be maintained with less intensive protocols. If AI can analyze a combination of clinical data, radiographic findings, systemic health information, and patient-specific risk factors to generate a risk stratification more accurate than what a clinician would produce manually, that changes how you triage recall intervals and prioritize interventions.

There is also emerging work on using non-invasive biomarkers alongside AI. A systematic review and meta-analysis evaluated the diagnostic accuracy of AI models trained on non-invasive or minimally invasive biomarkers, including saliva, gingival crevicular fluid, and immunological profiles for diagnosing and classifying periodontitis, finding that various AI models, such as random forest, artificial neural networks, and gradient boosting, showed promising results for non-invasive periodontal diagnosis. The idea of a salivary biomarker test interpreted by an AI that can tell you whether a patient has periodontitis and how severe it is, without any probing at all, is still in the research phase, but the trajectory is clear.

From a dental student’s perspective, perio is one of the subjects where AI literacy will matter most quickly. The 2017 classification system you are learning right now was designed to be evidence-based and systematic, which actually makes it more amenable to algorithmic application than older, more subjective systems. Learning the classification properly as you are supposed to, rather than just memorizing stages and grades for exams, means you will be better positioned to understand what the AI is doing when it applies those criteria and to catch the cases where its application is inappropriate or incomplete.

The iTero scanner also feeds directly into periodontal monitoring in practice. When you combine TimeLapse gingival recession tracking with AI-powered bone-level analysis from radiographs and clinical probing data, you build a multidimensional picture of a patient’s periodontal health over time that is far more informative than any single data point. The challenge for clinicians will be to synthesize all that information rather than just accepting whatever the AI outputs as a diagnosis.

Robotics and Surgical Applications

This one genuinely surprises a lot of people when they first hear about it. We tend to think of robotic surgery as something that belongs in orthopedics or cardiac surgery, but it is already showing up in dentistry, specifically in implant placement.

AI-guided robotic systems can analyze a patient’s 3D CBCT imaging and then assist in placing implants with sub-millimeter precision, following a pre-planned trajectory that minimizes risk to vital structures like the inferior alveolar nerve or the sinus floor. The idea is not that a robot drills the implant while the dentist goes and gets coffee, but rather that the robot provides real-time guidance and resistance against deviations from the planned path.

The incorporation of robotics in surgical procedures, particularly in dental implant placements, has demonstrated AI’s potential to minimize human error and improve procedural accuracy. For patients, that means fewer complications and better long-term outcomes. For the profession, it raises interesting questions about how implant surgery training should look in the future and whether the psychomotor skills we are developing in preclinical lab sessions will need to be complemented with simulation-based robotic training.

AI in INBDE & TOEFL

The Joint Commission on National Dental Examinations does not publicly state or deny that it uses artificial intelligence to grade the Integrated National Board Dental Examination. However, the exam relies heavily on advanced computerized psychometric analysis, statistical modeling, and automated data systems to ensure fairness, reliability, and exam security.

One major use of these systems is in question analysis. Every INBDE question is statistically evaluated based on factors such as difficulty level, candidate performance, reliability, and the ability to distinguish between stronger and weaker candidates. The exam also includes experimental or unscored questions that are used to test candidates and determine whether they are suitable for future exams. These questions are analyzed using large-scale computerized statistical methods before they are officially included in scoring exams.

The INBDE also uses scaled scoring rather than a simple raw percentage system. Since different exam versions may vary slightly in difficulty, statistical “equating” methods are used to maintain fairness across all test forms. This means candidates are evaluated according to standardized psychometric models rather than only the number of correct answers.

Another important area is test security and behavioral analysis. Modern high-stakes computerized exams commonly use automated systems to detect unusual answer patterns, rapid guessing, possible collusion, or abnormal testing behaviour. While the JCNDE does not fully disclose all security methods, its technical reports suggest that computerized response analysis is part of maintaining exam integrity.

The JCNDE has also announced plans for adaptive, multi-stage computerized testing in some examination systems. This reflects a broader movement toward more advanced digital assessment models that can evaluate candidates more efficiently and precisely using sophisticated statistical and performance-based algorithms. This system has been adapted by TOEFL 2026.

Starting with the updated TOEFL iBT format introduced for 2026, ETS officially announced that the Reading and Listening sections will use “multi-stage adaptive testing” (MST). In this system, all candidates begin with a similar first module, and then the difficulty of the next module changes based on their performance in the earlier section.

Unlike older fixed-format exams, where everyone received the same questions, adaptive testing personalizes part of the exam to the candidate’s estimated ability level. Stronger performance may route a candidate to more difficult question modules, while weaker performance may route a candidate to more moderate-level question modules. The final score is then calculated using both accuracy and question difficulty through psychometric models such as Item Response Theory (IRT).

ETS has also incorporated AI-supported technologies in TOEFL preparation and scoring systems. For example, TOEFL TestReady uses AI to provide personalized study recommendations and performance insights. In addition, the Speaking and Writing sections of TOEFL are scored using a combination of automated AI scoring systems and certified human raters.

Interestingly, ETS has researched computerized adaptive TOEFL systems for decades. Research reports dating back to the 1980s already discussed computerized adaptive TOEFL placement testing and multilevel testing models.

So, compared to the INBDE, TOEFL is actually further ahead publicly in implementing adaptive and AI-assisted testing systems. The INBDE currently uses advanced psychometric and computerized analytics, but TOEFL has publicly introduced adaptive, multi-stage testing into its operational exams.

What AI Means for Dental Students Specifically

This is the part I think about most because it directly affects you. There was research published in 2025 that specifically looked at how AI has been integrated into dental education and what the outcomes have been. The findings were genuinely interesting. AI has positively influenced things like procedural accuracy, diagnostic confidence, assessment efficiency, and content delivery. But it struggles to assess nuanced competencies like dexterity and clinical judgment

That last point is important because those nuanced competencies, the ones AI cannot easily evaluate, are also the ones that define what a good dentist actually is. Being technically proficient at a procedure is one part of it, but knowing when not to do a procedure, reading the patient, managing anxiety, making a judgment call in the middle of a complicated extraction, those things are still deeply human.

So what does this mean practically for you as students?

For one thing, it means you should actually learn to use these tools rather than ignoring them. Dental schools are now starting to teach students how to interpret AI performance metrics, verify dataset diversity, and understand the legal and regulatory requirements around these platforms. This is a genuinely new curriculum that is being added because it is becoming a necessary clinical skill.

It also means that some of the things you are currently assessed on might change. If AI cangrade cavity preparations more consistently and objectively than a human demonstrator, it might start being used as part of your assessments. There are already systems that use optical scanning to evaluate preps and give feedback on things like taper, depth, and wall convergence, which is much more precise than the naked eye.

There is also a real opportunity in AI-assisted learning. Right now a lot of your radiograph interpretation practice happens through looking at a set of films and comparing your findings to an answer sheet. AI-powered training tools could give you access to thousands of annotated cases, adapt to our specific weaknesses, and track our progress over time in a way that a textbook simply cannot.

Patient Communication and Education

One thing that gets overlooked in conversations about AI in dentistry is how much of the job is actually communication. Getting a patient to understand why they need a root canal, or convincing someone with dental anxiety to come back for a second appointment, or helping someone understand the long-term consequences of delaying treatment, that is a huge part of clinical practice.

AI-powered patient education tools are being developed to help with this. They can generate personalized, easy-to-understand explanations of proposed treatments, produce visual aids and 3D animations that show patients exactly what a procedure involves, and translate information into a patient’s preferred language. The goal is not to replace the chairside conversation but to supplement it, so that patients leave with a better understanding of what was discussed and what their options are

From a patient psychology perspective, this matters because one of the most common barriers to treatment acceptance is that patients simply did not understand what they were agreeing to. If AI tools can bridge that gap, it improves outcomes for everyone.

Oral Cancer and Early Detection

This is an application of AI that I feel particularly strongly about because oral cancer outcomes are so heavily dependent on stage at diagnosis. Five-year survival rates for early-stage oral cancer are dramatically better than for late-stage cases, but the disease is often caught late because the early signs can be subtle and easy to overlook in a routine examination.

AI systems are now being developed that can analyze mucosal photographs, histopathological images, and CT scans to identify suspicious lesions that might indicate malignancy. Current research shows that AI is already competing with human specialists in specific diagnostic tasks for oral cancer, particularly in analyzing histopathological images and CT scans.

For a general dentist doing a routine check, having an AI tool that flags potentially suspicious lesions for further investigation could genuinely save lives. This is one of those applications where the human stakes are high enough that even a marginal improvement in detection rates has enormous real-world significance.

The Administrative and Practice Management Side

I know this is the unglamorous part of the conversation, but bear with me because it matters. A significant proportion of a dentist’s day, or at least their staff’s day, involves things like scheduling, insurance coding, billing, patient recalls, and documentation. These are time-consuming administrative tasks that contribute to burnout and eat into the time that could be spent on patient care.

AI is already being applied to these areas in meaningful ways. Smart scheduling systems can predict which patients are likely to miss appointments and adjust the booking calendar accordingly. Automated billing and insurance coding tools reduce the error rate on claims, which means fewer rejected submissions and faster revenue cycles. Predictive analytics can identify which patients are overdue for recall and which ones are at highest risk of deterioration, so outreach can be prioritized intelligently.

For students going into private practice, understanding these tools is actually going to be a business advantage. Running a dental practice is genuinely hard from a management perspective, and anything that reduces administrative burden and improves efficiency makes a real difference to the sustainability of the business.

The Concerns and the Things You Should Be Thinking About

I do not want this to read like an uncritical celebration of AI in dentistry, because there are real concerns that deserve honest discussion.

The first is data bias. AI systems are only as good as the data they were trained on. If the training datasets were drawn predominantly from certain populations, the AI may perform significantly worse on patients from different ethnic backgrounds, age groups, or with different presentations of disease. This is a genuine equity issue and it is one that researchers and developers are actively trying to address, but it has not been solved yet.

The second concern is over-reliance. There is a risk that as AI tools become more
sophisticated, clinicians start to defer to them without exercising independent judgment. This is sometimes called automation bias and it is well-documented in other medical fields. The challenge in dental education is making sure that students develop genuine clinical reasoning skills alongside learning to use AI tools, so that they understand what the AI is doing and why, and can critically evaluate its outputs rather than just accepting them.

The third concern is regulatory. In the United States, many AI dental imaging platforms are classified as Class II medical devices, which requires transparency in training datasets and post-market monitoring. The European Union’s AI Act demands algorithmic explainability and bias audits. HIPAA updates around patient data used to train AI models are also evolving. As practitioners, you need to understand your legal obligations around these tools, and right now that regulatory landscape is still catching up with the technology.

The fourth concern is more philosophical and it is one that I find genuinely interesting. What does it mean for the patient-dentist relationship if more and more of the clinical decision-making is being supported or influenced by algorithmic outputs? There is something important about a clinician sitting with a patient, listening to them, using judgment formed through years of training and experience, and arriving at a recommendation. AI changes the texture of that relationship in ways that are not fully understood yet.

Where This Is All Heading

Researchers have outlined a kind of roadmap for how AI is likely to evolve in dentistry. The stages go something like this. First, AI competing with dentists in specific diagnostic tasks, which is already happening now. Second, AI competing in virtual planning and construction of restorations and prosthetics, which is also well underway. Third, AI competing in the actual physical execution of clinical work through robotics, which is emerging. And fourth, potentially AI taking the lead in some task-specific procedures entirely, though that is projected to be decades
away.

The consensus view from most researchers and clinicians is not that AI will replace dentists. The argument is that dentists who use AI effectively will replace those who do not. What that means for you as students is that learning to work with these tools is not optional anymore. It is becoming part of what it means to be a competent clinician

The future will likely involve something researchers are calling AI passports, which are digital records of a model’s lineage, accuracy metrics, and retraining history, that accompany AI tools the way a drug’s prescribing information accompanies a medication. That level of accountability and transparency is what will be needed before AI tools can be fully trusted in high-stakes clinical settings.

Final Thoughts

I think about all of this and I feel two things at once. There is genuine excitement here. The idea that you could be practicing in an era where early oral cancer detection rates improve significantly because of AI, where treatment plans are more personalized and evidence based than ever before, and where patients understand their care better and have better outcomes as a result. That is not a depressing future. That is a good one.

But it is also crucial to realize that AI is not just waiting for you in the clinic. It is already the gatekeeper to your career. Whether it is the multistage adaptive testing and AI assisted scoring systems determining your TOEFL readiness, or the advanced psychometric analytics and behavioral algorithms grading your INBDE, these technologies are actively shaping your path to licensure right now. Understanding how these algorithms evaluate your knowledge is no longer just nice to know. It is a strategic necessity for clearing your exams and getting into advanced standing programs.

There is a responsibility on you, as the generation of dentists who will be practicing through this massive transition, to engage with these tools critically rather than passively. You need to learn not just how to use them, but how to question them. You must maintain the clinical judgment and the human connection that no algorithm can replicate, and be willing to push back when you see applications being rolled out before the evidence is solid enough to justify their use on real patients.

The core of what makes a good dentist, including empathy, complex judgment, communication, manual skill, and a genuine commitment to the patient in front of you, is not going away. AI is going to be a fundamental part of the landscape you practice in, along with the very exams you take to get there. But when the screens are off, it is still going to be you sitting with the patient, listening, and making the call.

That is worth thinking about before you graduate

References

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AI in dentistry and dental studies