AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters
Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. <i>Backgrounds and Objectives:</i> This study aimed to evaluate the reliability of AI-assisted dental–pe...
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MDPI AG
2025-03-01
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| Series: | Medicina |
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| Online Access: | https://www.mdpi.com/1648-9144/61/4/572 |
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| author | Oana-Maria Butnaru Monica Tatarciuc Ionut Luchian Teona Tudorici Carina Balcos Dana Gabriela Budala Ana Sirghe Dragos Ioan Virvescu Danisia Haba |
| author_facet | Oana-Maria Butnaru Monica Tatarciuc Ionut Luchian Teona Tudorici Carina Balcos Dana Gabriela Budala Ana Sirghe Dragos Ioan Virvescu Danisia Haba |
| author_sort | Oana-Maria Butnaru |
| collection | DOAJ |
| description | Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. <i>Backgrounds and Objectives:</i> This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. <i>Material and Methods:</i> A comparative study was conducted involving 60 practitioners divided into three groups—general dentists, specialists, and senior specialists—along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference “gold standard” validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. <i>Results:</i> AI’s consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, <i>p</i> = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. <i>Conclusions:</i> AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments. |
| format | Article |
| id | doaj-art-e1fea18da2664b0e80f5746300cc13c5 |
| institution | DOAJ |
| issn | 1010-660X 1648-9144 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Medicina |
| spelling | doaj-art-e1fea18da2664b0e80f5746300cc13c52025-08-20T03:13:55ZengMDPI AGMedicina1010-660X1648-91442025-03-0161457210.3390/medicina61040572AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal ParametersOana-Maria Butnaru0Monica Tatarciuc1Ionut Luchian2Teona Tudorici3Carina Balcos4Dana Gabriela Budala5Ana Sirghe6Dragos Ioan Virvescu7Danisia Haba8Department of Biophysics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Phamacy, 700115 Iasi, RomaniaDepartment of Prosthodontics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Prosthodontics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Oral Health, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Prosthodontics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Pediatric Dentistry, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Dental Materials, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Dental Radiology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, RomaniaArtificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. <i>Backgrounds and Objectives:</i> This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. <i>Material and Methods:</i> A comparative study was conducted involving 60 practitioners divided into three groups—general dentists, specialists, and senior specialists—along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference “gold standard” validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. <i>Results:</i> AI’s consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, <i>p</i> = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. <i>Conclusions:</i> AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.https://www.mdpi.com/1648-9144/61/4/572periodontal statusalveolar bone resorptionbone lossperiodontal pocketAIdental imaging |
| spellingShingle | Oana-Maria Butnaru Monica Tatarciuc Ionut Luchian Teona Tudorici Carina Balcos Dana Gabriela Budala Ana Sirghe Dragos Ioan Virvescu Danisia Haba AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters Medicina periodontal status alveolar bone resorption bone loss periodontal pocket AI dental imaging |
| title | AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters |
| title_full | AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters |
| title_fullStr | AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters |
| title_full_unstemmed | AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters |
| title_short | AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters |
| title_sort | ai efficiency in dentistry comparing artificial intelligence systems with human practitioners in assessing several periodontal parameters |
| topic | periodontal status alveolar bone resorption bone loss periodontal pocket AI dental imaging |
| url | https://www.mdpi.com/1648-9144/61/4/572 |
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