Dr. Tony Bader
• Doctor of Dental Surgery (DDS), Saint Joseph University (2010)
• AI in Healthcare Specialization, Stanford University (2025)
• Master’s in Digital Marketing and E-commerce, Isabel I University (2024)
• European Master in Executive Leadership, IEAD Business School (2025)
• Tony Bader is a multidisciplinary advisor working across governance, AI, public policy, and strategic communication. He supports institutions and NGOs with evidence-based strategy, digital transformation, and organizational development. His work spans policy design, AI in healthcare, and public engagement. He also delivers high-impact training programs that strengthen communication, negotiation, and digital skills.
introduction
introduction
Artificial intelligence (AI) systems are increasingly being incorporated into dental imaging, caries detection, periodontal assessment, cone beam computed tomography (CBCT) interpretation, treatment planning, and patient communication. These tools promise to enhance the consistency and speed of diagnostic workflows, especially in radiographically complex cases. However, as with all medical technologies, benefits must be balanced with ethical and clinical obligations. Dentists remain the final decision makers, and understanding the limitations of AI models is essential for safe practice. This article highlights the most critical ethical implications that should guide the responsible integration of AI decision support in dentistry.
ALGORITHMIC BIAS IN DENTAL AI SYSTEMS
ALGORITHMIC BIAS IN DENTAL AI SYSTEMS
Algorithmic bias occurs when an AI model consistently produces results with lower accuracy or reliability in certain groups of patients. Bias usually originates from the training data that the developers use. When datasets lack diversity in age, ethnicity, anatomy, or disease presentation, AI may misinterpret or underdiagnose conditions in underrepresented populations.
In dentistry, this can appear in several ways:
- caries detection models that perform better on certain age groups
- bone density assessment algorithms trained mostly on one demographic
- CBCT lesion detection systems validated on small or homogenous datasets
If clinicians are unaware of these limitations, biased outputs may unintentionally reinforce diagnostic inequalities. Ethically, dentists must be conscious of whether an AI system has been validated on diverse patient populations and whether its performance varies between subgroups.
PATIENT SAFETY AND CLINICAL OVERSIGHT
PATIENT SAFETY AND CLINICAL OVERSIGHT
AI decision support tools can enhance diagnostic confidence, but they also create new safety risks. These risks appear when a dentist relies on automated suggestions without adequate clinical verification. For example, an AI model may flag distal caries where none exist or miss early periapical changes due to image noise. Over-reliance on automation increases the chance of false positives, false negatives, or incomplete assessments.
Safe clinical integration requires:
- independent verification of all AI outputs
- understanding of the model’s accuracy, limitations, and failure modes
- avoidance of a “black box” mentality where AI is assumed to be correct
AI should support human judgment rather than replace it. Patient safety depends on maintaining a balanced relationship between the clinician’s experience and the system’s automated recommendations.
“When used consciously and ethically, AI has the potential to elevate the quality of care while sustaining the highest standards of trust and professionalism.”
CLINICAL RESPONSIBILITY AND PROFESSIONAL ACCOUNTABILITY
CLINICAL RESPONSIBILITY AND PROFESSIONAL ACCOUNTABILITY
An important ethical question is who holds responsibility when an AI-generated recommendation leads to a misdiagnosis or clinical error. Current global standards place full responsibility on the treating dentist, not the software developer or the algorithm.
This means:
- clinicians must exercise independent judgment
- AI cannot be cited as a final authority
- dentists must document their reasoning when an AI suggestion is used or rejected
AI can inform decisions, but it cannot shift professional accountability. Clinicians must recognize that the technology is a tool and that the moral and legal duty of care remains entirely with them.
TRANSPARENCY AND EXPLAINABILITY IN AI TOOLS
TRANSPARENCY AND EXPLAINABILITY IN AI TOOLS
One of the biggest challenges with modern AI models is the lack of transparency in how they reach conclusions. Many systems rely on deep learning architectures that can identify patterns but cannot easily explain the reasoning behind their outputs.
Transparency matters for three reasons:
- clinical trust: Dentists should understand the basis of a recommendation
- patient communication: Patients have the right to know why a treatment is proposed
- regulatory oversight: Authorities need clarity to evaluate safety
Manufacturers should provide information about model validation, accuracy metrics, data sources, and known limitations. Clinicians should request this information when choosing any AI-enabled software.
REGULATORY GAPS AND THE NEED FOR CLEAR STANDARDS
REGULATORY GAPS AND THE NEED FOR CLEAR STANDARDS
Dental AI tools currently operate in an ever-developing regulatory landscape. Many are classified as medical devices, but global standards remain inconsistent. Some AI systems receive clearance for very specific uses, such as caries detection on two-dimensional radiographs, while others operate in gray areas without a unified evaluation framework.
Regulatory gaps include:
- lack of standardized validation requirements
- unclear reporting obligations when errors occur
- insufficient monitoring of real-world performance
- limited transparency requirements for training data
Clinicians should choose AI tools that demonstrate clear regulatory approvals and published validation results. Professional organizations, including digital dentistry societies, have an important role in helping set guidelines and educating practitioners about safe integration.
CONCLUSION
CONCLUSION
AI decision support systems offer real value to dentistry, including improved diagnostic consistency, workflow efficiency, and enhanced patient communication. However, these benefits come with ethical and clinical responsibilities. Dentists must remain aware of algorithmic bias, guard patient safety, maintain full professional accountability, demand transparency from developers, and stay informed about evolving regulatory frameworks.
Responsible adoption requires a balanced mindset that combines the strengths of automated systems with the irreplaceable expertise of trained clinicians. When used consciously and ethically, AI has the potential to elevate the quality of care while sustaining the highest standards of trust and professionalism.
REFERENCES
1) Mangano FG, et al. Intraoral scanners in dentistry: a review. BMC Oral Health. 2017;17:149.
2) Srivastava G, et al. Accuracy of intraoral scanner for edentulous arches: a systematic review. J Prosthodont Res. 2023.
3) Gehrke P, et al. Factors impacting intraoral scanning accuracy in implant dentistry. Materials. 2024;17:500.
4) Bornstein MM, et al. Cone beam computed tomography in implant dentistry. Int J Oral Maxillofac Implants. 2014;29(Suppl):55–77.
5) Jacobs R, et al. CBCT in implant dentistry: recommendations. J Oral Rehabil. 2018;45(7):538-550.
6) Jindanil T, et al. Smartphone facial scanning in dentistry. Orthod Craniofac Res. 2024;27(1):18–29.
7) Andrews J, et al. Validation of 3D facial imaging with iPhone TrueDepth. J Prosthet Dent. 2023;130(5):579-587.
8) Abuduwaili K, et al. Comparison of photogrammetry, IOS and conventional impressions. BMC Oral Health. 2025;21:636.
9) Pozzi A, et al. Photogrammetry vs IOS in complete-arch implant impressions. Clin Implant Dent Relat Res. 2025.
10) Zou J, Li L. Passive fit verification of frameworks from photogrammetry scans. J Prosthet Dent. 2024;131(4):756-759.
11) Tahayeri A, et al. 3D printing in dentistry: an overview. Dent Mater. 2020;36(1):54-68.
12) Dawood A, et al. 3D printing in dentistry. Br Dent J. 2015;219(11):521–529.
13) Miyazaki T, et al. Dental CAD/CAM and CNC milling. Dent Mater J. 2009;28(1):44-56.
14) Joo HJ, et al. Mechanical properties of highly filled hybrid resins. Dent Mater J. 2021;40(2):374–381.
15) Zingari F, et al. Predictability of IOS error for full-arch implant scans. J Prosthodont Res. 2023;67(3):343-350.