Perception, Perspective and Application of Artificial Intelligence among Oral Medicine and Radiology Fraternity- A Questionnaire Study

The study received approval from the Institutional Ethical Committee of St. Joseph Dental College and Hospital. [IEC Protocol Number: SJDC/CEC/OMR/2025/003]. The cross-sectional study involves human participants who could withdraw at any time, and participation was completely voluntary. Each participant’s identity was wholly protected during the investigation. The Google Form received responses from 70 participants in total. Snowball sampling technique was used. The study on our fraternity was limited to Andhra Pradesh. A web-based electronic questionnaire survey (Google Form) was the instrument utilized to gather the data.

 

Inclusion Criteria

The study included Academicians, Private dental practitioners and Post graduate students in Oral Medicine and Radiology Fraternity.

 

Exclusion Criteria

Subjects who were unwilling to participate in the questionnaire survey via the online Google Form were excluded from the study.

 

Participation was entirely voluntary, and informed consent was obtained digitally prior to survey completion. The study respected autonomy and confidentiality, and no identifying information was collected from non-participants. A 20-questionnaire is prepared regarding the AI in dentistry; the first ten are knowledge-based, and the remaining ten are attitude-based toward AI. The questions were mainly 19 closed-ends and 1 open-end question. After preparing all these questions, approval is taken from the three experienced academicians. A pre-designed online questionnaire was made using Google Forms, and the link was created using Google Docs and sent via social media and mail. Mandatory option is included not to skip any question without filling. The purpose of our study was very well explained, and the consent was taken online from the participants, which was the first component of the study questionnaire. Practitioners were asked to tick out any one option provided and fill any blank for open-ended questions. Data from the questionnaire were filled in an Excel worksheet and further evaluated. Following are questionnaires prepared for Knowledge and Attitude analysis in Oral Medicine and Radiology fraternity. A Google Form link is sent via email and social media (WhatsApp) which is active for 14 days; during that time, all participant responses are collected and locked. Responses sent after the given time frames are not added.

 

Table-1: Distribution of study population based on occupation

 

Table-1 depicts that among the all-groups Postgraduates constitute the largest segment of the study population, accounting for 40% (n=28). Academicians form the second-largest group at 31.4% (n=22), Dental practitioners comprise 28.6% (n=20).

 

Figure-1: Distribution of study population by occupation

 

Table-2: Overall responses for knowledge questions

 

Table-2 depicts that the knowledge domain responses reveal a high level of conceptual awareness regarding artificial intelligence (AI) among the study population. A substantial 90% (n=63) of participants reported familiarity with the concept of AI, and 77.1% (n=54) acknowledged understanding its applications in oral radiology. This indicates a strong foundational awareness, likely influenced by academic exposure and digital integration in postgraduate training. Despite this awareness, 61.4% (n=43) of respondents had not used AI tools in practice, suggesting a gap between theoretical knowledge and clinical implementation. This may reflect infrastructural limitations, lack of training, or cost-related barriers. Regarding diagnostic efficacy, 67.2% (n=47) of participants either agreed or strongly agreed that AI can reduce diagnostic errors in oral radiology, while 24.3% (n=17) remained neutral. This reflects cautious optimism toward AI’s potential to enhance diagnostic precision. When asked about patient outcomes, 47.1% (n=33) reported improvements with AI-assisted diagnostics, whereas   did not observe any difference, and 20% (n=14) marked it as not applicable—possibly indicating limited exposure or usage. Perceptions of cost-effectiveness were mixed: 40% (n=28) believed the cost of AI is justified, while 31.4% (n=22) was unsure and 28.6% (n=20) disagreed. This suggests economic feasibility remains a concern in adoption. Trust in AI systems for data security was moderate, with 42.9% (n=30) expressing confidence, while 57.2% were either unsure or did not trust AI with patient data—highlighting ethical and privacy apprehensions. Encouragingly, 67.1% (n=47) believed AI can assist in treatment planning and decision-making, and 75.7% (n=53) felt it could enhance radiographic interpretation. These findings underscore the perceived value of AI in augmenting clinical judgment and image analysis.

 

Figure-2: Overall responses for knowledge questions

 

Table-3: Over all responses for attitude questions

 

Table-3 depicts that attitudinal responses reflect a generally positive outlook toward AI integration in dentistry. A majority (65.7%, n=46) agreed that AI can aid in early detection of oral diseases potentially missed by human eyes, reinforcing its role in improving diagnostic sensitivity. However, only 25.7% (n=18) had attended AI-related workshops or training, while 74.3% (n=52) had not—indicating a significant gap in formal exposure and continuing education. A strong consensus emerged regarding curriculum reform, with 75.7% (n=53) advocating for AI inclusion in dental education. This aligns with the finding that 51.4% (n=36) considered AI literacy to be “very important” for dental professionals. The primary barrier to AI adoption was identified as lack of knowledge/training (65.7%, n=46), followed by cost (17.1%, n=12) and resistance to change (7.1%, n=5). These insights highlight the need for structured training programs and cost-effective solutions. Regarding diagnostic outcomes, 55.7% (n=39) observed differences when AI tools were used compared to traditional methods, suggesting tangible clinical impact. User experience with AI tools was mixed: 55.7% (n=39) rated them as neutral, while 34.3% (n=24) found them very user-friendly. This indicates room for improvement in interface design and usability. A significant 77.1% (n=54) believed AI can assist in maintaining quality control in radiology imaging, and 70% (n=49) felt it could support general dentists in decision-making typically requiring specialist input. Finally, 87.1% (n=61) agreed that AI can reduce the psychological workload of dental professionals, suggesting its potential to alleviate cognitive burden and improve workflow efficiency.

 

Figure-3: Overall responses for Attitude Questions

 

 

 

 

Figure-4: Overall Responses for Attitude Questions

 

Table-4:  Comparison of Knowledge Responses based on Qualification

 

Table-4 depicts the comparative analysis of knowledge-based responses among postgraduates, academicians, and dental practitioners which revealed no statistically significant differences across all items (p > 0.05). Familiarity with AI was high across all groups, with academicians showing the highest awareness (95.5%), followed by practitioners (90%) and postgraduates (85.7%). Understanding of AI applications in oral radiology was slightly higher among practitioners (85%) and academicians (81.8%) compared to postgraduates (67.9%). Despite this, actual usage of AI tools remained low across all groups, with the majority reporting no prior use. Perceptions regarding AI’s ability to reduce diagnostic errors varied, with postgraduates showing the highest agreement (64.3%), while academicians had a higher proportion of neutral and disagree responses. Views on patient outcomes, cost justification, and data security were mixed, with no significant qualification-based trends.

 

Table 5: Comparison of Attitude Responses based on Qualification

 

Table-5 depicts the comparative analysis of attitude responses among postgraduates, academicians, and dental practitioners which revealed mostly non-significant differences across variables, except for one item. A statistically significant difference (p = 0.05*) was observed in the perception of AI’s role in early detection of oral diseases. Postgraduates (78.6%) and practitioners (70%) showed higher agreement compared to academicians (45.5%), who had a notably higher proportion of disagreement (40.9%). Attendance at AI-related workshops was low overall, but practitioners (40%) and academicians (31.8%) reported higher participation than postgraduates (10.7%), though this difference was not statistically significant (p = 0.053). Support for incorporating AI into the dental curriculum was consistently high across all groups (72.7–78.6%). The most cited barrier to AI adoption was lack of knowledge/training, especially among practitioners (80%), followed by cost and resistance to change. Perceived differences in diagnostic outcomes with AI use were similar across groups, with practitioners showing slightly more positive responses (65%). Regarding AI literacy, all groups emphasized its importance, with over 50% rating it as “very important.” User-friendliness of AI tools was rated “neutral” by most, though practitioners showed greater favorability (50% rated them “very user friendly”).Confidence in AI’s role in quality control and decision support was high across qualifications, with practitioners again showing slightly stronger agreement. Finally, the belief that AI can reduce psychological workload was nearly unanimous across all groups (85.7–90%), indicating broad consensus on its potential to ease clinical stress.