Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study
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BACKGROUND: Effective patient education is critical in enhancing treatment outcomes and reducing anxiety in dental procedures. This study compares the effectiveness of AI-generated educational materials with traditional methods in improving patient comprehension and reducing anxiety during endodontic and restorative dental treatments. METHODS: A cross-sectional, comparative study was conducted with 100 participants undergoing restorative or endodontic procedures. Patients were randomized into two groups: those receiving AI-generated instructional materials (via ChatGPT) and those receiving traditional education (verbal explanations and pamphlets). Baseline knowledge and post-intervention knowledge retention were assessed using structured tests. Patient perceptions of clarity, usefulness, comprehensiveness, trust, and anxiety were measured using Likert-scale surveys. Three dental experts evaluated the educational content for accuracy and suitability. Statistical analysis included t-tests and Cohen's kappa to measure inter-rater reliability. RESULTS: AI-generated materials significantly outperformed traditional methods in all measured dimensions, including clarity (4.42 vs. 3.25), usefulness (4.63 vs. 3.50), comprehensiveness (4.50 vs. 3.29), trust (4.00 vs. 2.96), and anxiety reduction (mean anxiety score: 2.63 vs. 3.38, p < 0.001). Pre- and post-intervention knowledge assessments revealed substantial knowledge improvement in the AI group. Expert evaluations confirmed the accuracy and suitability of AI-generated materials, with high inter-rater reliability (κ = 0.75, p < 0.001). CONCLUSIONS: AI-generated educational materials demonstrate superior effectiveness in improving patient comprehension and reducing anxiety compared to traditional methods. Their integration into dental practice could enhance patient satisfaction and streamline the educational process, particularly for complex or anxiety-inducing procedures. Future research should explore their application in diverse dental specialties and assess long-term impacts on patient behavior and clinical outcomes.
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In dentistry, AI-generated materials were associated with improved patient understanding and reduced anxiety.
“In dentistry, AI-generated materials were associated with improved patient understanding and reduced anxiety (20).”
From ChatGPT for diabetes education: potential, accuracy, and accessibility in patient support (2026) · cites this paper as [20]
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Cites
Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
Zhang, P., & Kamel Boulos, M. N. (2023) · Future Internet · doi:10.3390/fi15090286 · Cited by 411 · cited 2× · see citing works
“Patient anxiety and misunderstandings are frequent during these procedures [11, 19].”
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis
Robertson, C., Woods, A., Bergstrand, K., Findley, J., Balser, C., & Slepian, M. J. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000237 · Cited by 158 · see citing works
Integration of AI-Generated Images in Clinical Otolaryngology
Javan, R., Cole, J., Hsiao, S., Cronquist, B., & Monfared, A. (2024) · Cureus · doi:10.7759/cureus.68313 · Cited by 9 · cited 1× · see citing works
“This could be particularly useful for patients with lower literacy levels or visual learning preferences [21].”
Flagged first, then by citations
Research works printed but not linked: 21
- [2]Alasker A, Alsalamah S, Alshathri N, Almansour N, Alsalamah F, Alghafees M, AlKhamees M, Alsaikhan B. Performance of large Language models (LLMs) in providing prostate cancer information. BMC Urol. 2024;24(1):177. 39180045 PMC11342655 · DOI
- [3]Thorat V, Rao P, Joshi N, Talreja P, Shetty AR. Role of artificial intelligence (AI) in patient education and communication in dentistry. Cureus. 2024;16(5). PMC11155216 38846249
- [4]Nishi NJ, Pervin S, Shaude S. Dental fear: a narrative review. Int J Res Med Sci. 2022;10(3):777. · DOI
- [5]Ho JC, Chai HH, Lo EC, Huang MZ, Chu CH. Strategies for effective Dentist-Patient communication: A literature review. Patient preference and adherence. 2024 Dec 31:1385–94. PMC11225999 38974679
- [6]Nashwan AJ, Abujaber AA, Choudry H. Embracing the future of physician-patient communication: GPT-4 in gastroenterology. Gastroenterol Endoscopy. 2023;1(3):132–5. · DOI · Paper page
- [8]Vassis S, Powell H, Petersen E, Barkmann A, Noeldeke B, Kristensen KD, Stoustrup P, Petersen ED. Large-Language models in orthodontics: assessing reliability and validity of ChatGPT in pretreatment patient education. Cureus. 2024;16(8). PMC11437517 39347180
- [9]Woodmansey K. Readability of educational materials for endodontic patients. J Endod. 2010;36(10):1703–6. 20850682 · DOI
- [10]Ahmed S, Sharma P, Mahaprasad A, Patel A, Chohan H, Murugesan S. Assessing the influence of patient anxiety on the efficacy of endodontic procedures. J Pharm Bioallied Sci. 2024;16(Suppl 3):S2685–7. 39346408 PMC11426610 · DOI
- [12]Müller A, Mertens SM, Göstemeyer G, Krois J, Schwendicke F. Barriers and enablers for artificial intelligence in dental diagnostics: a qualitative study. J Clin Med. 2021;10(8):1612. 33920189 PMC8069285 · DOI
- [13]Bhattad PB, Pacifico L. Empowering patients: promoting patient education and health literacy. Cureus. 2022;14(7). PMC9411825 36043002
- [15]Aydin S, Karabacak M, Vlachos V, Margetis K. Large Language models in patient education: A scoping review of applications in medicine. Front Med;11:1477898. PMC11554522 39534227
- [16]Robinson CL, D’Souza RS, Yazdi C, Diejomaoh EM, Schatman ME, Emerick T, Orhurhu V. Reviewing the potential role of artificial intelligence in delivering personalized and interactive pain medicine education for chronic pain patients. J Pain Res. 2024 Dec;31:923–9. PMC10924768 38464902
- [18]Zemni I, Gara A, Nasraoui H, Kacem M, Maatouk A, Trimeche O, Abroug H, Fredj MB, Bennasrallah C, Dhouib W, Bouanene I. The effectiveness of a health education intervention to reduce anxiety in quarantined COVID-19 patients: a randomized controlled trial. BMC Public Health. 2023;23(1):1188. 37340300 PMC10280925 · DOI
- [19]Setzer FC, Li J, Khan AA. The use of artificial intelligence in endodontics. J Dent Res. 2024;103(9):853–62. 38822561 PMC11378448 · DOI
- [20]Cui S, Fiscella K, Xiao J. Culturally competent patient-dentist communication influences oral health disparities: what needs to change, why, and how. Quintessence Int. 2024(4):262–3. 38661338
- [22]Martin AR. (2024) Traditional Methods in advance. The CLR James Journal. 10.5840/clrjames20241219116 · DOI
- [23]Dutta A, Pasricha N, Singh RK, Revanna R, Ramanna PK. Harnessing artificial intelligence in dentistry: enhancing patient care and diagnostic precision. Asian J Den Sci [Internet]. 2024 Dec;7(12):379–86.
- [24]Delgado-Ruiz R, Kim AS, Zhang H, Sullivan D, Awan KH, Stathopoulou PG. Generative artificial intelligence (Gen AI) in dental education: opportunities, cautions, and recommendations. J Dent Educ. 2025;89(1):130–6. 39219015 · DOI
- [25]Schwendicke F, Mertens S, Cantu AG, Chaurasia A, Meyer-Lueckel H, Krois J. Cost-effectiveness of AI for caries detection: randomized trial. J Dent. 2022;119:104080. 35245626 · DOI
- [26]Popkin R, Taylor-Zapata P, Bianchi DW. Physician bias and clinical trial participation in underrepresented populations. Pediatrics. 2022;149(2):e2021054150. 35043204 PMC10100134 · DOI
- [27]Macapagal J, Calimag MM. Effect of a Video-Based case presentation educational intervention anchored on the theory of planned behavior on adoption of oral health behaviors among dental patients: A Quasi-experimental study. Odovtos-International J Dent Sci. 2022;23(2):148–60.
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ChatGPT for diabetes education: potential, accuracy, and accessibility in patient support
Abuzar, M., Rai, S., Pathan, T., Fakki, F., & Syed, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1768843 · Cited by 4 · see citing works
- Authors
- Islam, S.
- Affiliations
- Fatima Jinnah Dental College (PK)
- Corresponding author
- Islam, Shahid
- ORCID
- Islam, Shahid
- Venue
- BMC Oral Health
- Published
- 2025-05-07
- Volume
- 25
- Issue
- 1
- Pages
- 689-689
- ISSN
- 1472-6831 (ISSN-L)
- PMC
- PMC12060393
- OpenAlex
- W4410160686
- Cited by
- 27
- References
- 4
- Citations by year
- 2026: 22 · 2025: 5
- FWCI
- 6.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Oral Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Dentistry › Oral Surgery › Dental Anxiety and Anesthesia Techniques
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Keywords
- AI-generated educational content, patient education, dental anxiety, endodontics, restorative dentistry, ChatGPT, knowledge retention, patient understanding, trust in AI, verbal explanation, inter-rater reliability
- MeSH terms
- Generative Artificial Intelligence, Adult, Cross-Sectional Studies, Dental Restoration, Permanent, Dentistry, Operative, Endodontics, Female, Humans, Male, Middle Aged, Patient Education as Topic, Root Canal Therapy, Dental Anxiety, Comprehension
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