Abstract
Objective
To determine the perceptions and self-reported knowledge base of AI and machine learning (AI/ML) among professional veterinary students.
Methods
First-, second-, third-, and fourth-year professional veterinary students from the School of Veterinary Medicine at the University of California-Davis were surveyed in a cross-sectional study regarding their knowledge level, attitudes, and feelings regarding AI/ML in veterinary medicine. Responses were summarized, and descriptive statistics were performed.
Results
One hundred seventy-six of 594 (29.6%) veterinary students responded to the survey. One hundred forty-one out of 176 (80%) students reported slight or no knowledge surrounding AI/ML, and 139/176 (79%) of students were moderately to extremely interested in learning about AI/ML applications in veterinary medicine. Sixty-five out of 176 (37%) students reported learning about AI/ML concepts in their veterinary curriculum. Most students expect to use these tools in their practice (104/176 [59%]) and suspect that AI/ML will improve veterinary medicine (135/176 [77%]).
Conclusions
Artificial intelligence and machine learning applications in veterinary medicine are increasingly available. Professional veterinary students are eager to learn about these technologies and recognize their relevance to their future careers.
Clinical Relevance
Many professional veterinary programs do not provide structured AI/ML literacy training. Artificial intelligence education should be incorporated into the curriculum to ensure that future veterinarians can critically evaluate and effectively integrate AI/ML tools into clinical practice.
Artificial intelligence and machine learning (AI/ML) are rapidly growing industries impacting health care on many fronts. Veterinary healthcare companies and researchers are leveraging AI/ML to build tools that interface with veterinarians, veterinary staff, and animal owners.1–9 These advances have the opportunity to improve patient care, increase efficiency, and provide support to clients.10 Indeed, clinical decision support systems that rely on AI/ML models are already deployed and relied on in veterinary medicine that can decipher radiographs,11 predict the onset of chronic disease,6 and analyze clinical pathology specimens.12 With the investment in this area of research, the profession is poised to transform how patient data are handled and leveraged to enhance clinical decision-making. Veterinarians must have basic literacy in AI/ML methodology to rationally apply output from AI/ML tools, just as veterinarians are taught the fundamental competencies related to the mechanics of other diagnostic modalities.
Studies assessing the knowledge and attitudes of medical students toward AI/ML have shown that medical students recognize that AI/ML technology will impact their careers and that these tools will play an important role in healthcare.13–20 Additionally, most medical students agree that AI/ML fundamentals should be a core competency in their professional training.13–20 Further, those teaching medical students also recognize a lack of AI/ML training in medical education.21 Despite recognizing AI/ML as a critical tool in healthcare and the desire to learn more about these topics, most medical students reported having limited technical knowledge and a lack of presence in their professional education.14,15,18,19,22,23 These studies emphasize the current AI/ML knowledge void in the next generation of healthcare providers.
While this topic has been investigated in the human medical field, little is known regarding veterinary students’ level of education and perceptions of the impact of AI/ML in veterinary medicine. One study24 assessing the view of veterinary students regarding AI in Turkey determined that students agree that AI should be used in veterinary medicine, and it is important for veterinary students to obtain specific training in this field. This study aims to establish baseline survey information regarding the knowledge and attitudes toward AI/ML technology in veterinary medicine from professional veterinary students in the United States.
Methods
Survey participants
A voluntary sample of survey participants was recruited via email from the first- through fourth-year veterinary students enrolled in the professional veterinary medicine (PVM) program at University of California-Davis in September 2023. The study was reviewed by the University of California-Davis Institutional Review Board and was not considered “Human Subject Research,” so it was exempt from ethical approval. All procedures followed ethical standards, including providing informed consent to participate, voluntary participation, and anonymous data collection.
Survey design
Survey questions were adapted from surveys administered to medical students assessing attitudes toward AI/ML.18,20 Content validity was established by 3 subject matter experts before administering the survey, ensuring it was appropriate for veterinary medicine. The final survey was subdivided into 4 subsections (Table 1). The first section collected the students’ years in the veterinary curriculum as demographic data (years 1 to 4). The second section assessed the students’ knowledge of and interest in learning about AI/ML with binary yes or no questions and questions using a 5-point Likert scale (extremely, very, moderately, slightly, or not at all). The third section assessed students’ attitudes toward AI/ML using a 5-point Likert scale (strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, or strongly disagree). Questions 7, 8, 9, 12, 13, and 14 were considered “positive connotation” questions, and questions 10, 11, 15, and 16 were considered “negative connotation” questions. The last section consisted of an open-answer question asking for additional comments regarding experience with AI/ML.
Survey administered to students regarding their knowledge, attitude, and feelings toward AI and machine learning (AI/ML) in veterinary medicine.
| Survey questions | |||||
|---|---|---|---|---|---|
| 1 | Please indicate your year in the curriculum. | ||||
| First | Second | Third | Fourth | ||
| 2 | Have you heard of artificial intelligence or machine learning? | ||||
| Yes | No | ||||
| 3 | Have you used a veterinary-based AI/ML too? | ||||
| Yes | No | ||||
| 4 | Have any AI/ML concepts been introduced to you during your veterinary curriculum? | ||||
| Yes | No | ||||
| 5 | How knowledgeable are you about AI/ML? | ||||
| Extremely | Very | Moderately | Slightly | Not knowledgeable at all | |
| 6 | How interested are you in learning about AI/ML applications in veterinary medicine? | ||||
| Extremely | Very | Moderately | Slightly | Not interested at all | |
| Please indicate the extent to which you agree or disagree with the following statements: | |||||
| 7 | I will use AI/ML tools in my veterinary career. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 8 | AI/ML tools will become the standard of care in veterinary medicine. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 9 | I view AI/ML as a partner rather than a competitor. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 10 | Integration of AI/ML into veterinary medicine frightens me. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 11 | AI/ML will replace some veterinarians. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 12 | AI/ML will improve veterinary medicine. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 13 | AI/ML should be a part of the veterinary school curriculum. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 14 | Patients will be safer if a veterinarian incorporates AI/ML tools. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 15 | AI/ML tools must be viewed skeptically. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 16 | I am concerned about liability when using an AI/ML tool. | ||||
| Strongly agree | Somewhat agree | Neither agree nor disagree | Somewhat disagree | Strongly disagree | |
| 17 | Optional: Any additional comments in regards to your experience with machine learning or AI? | ||||
The question is printed in bold with response choices below.
Survey administration
A prospective, cross-sectional survey study was conducted utilizing a voluntary sample of respondents. An electronic survey tool (Qualtrics; Silver Lake) was utilized to administer the survey. An email was sent to all currently enrolled students to recruit students to participate in the survey. Students submitted their institutional email to ensure only eligible students participated. Students were incentivized to complete the survey by entering a drawing for a gift card.
Data analysis
Responses were exported from the online survey tool. The responses to each question were summarized with descriptive statistics, and histograms were used to visualize the responses. Knowledge of AI/ML questions with binary output was assessed for differences in proportions between PVM years with a χ2 test.
Questions that utilized a Likert scale were assessed for normality. An unpaired Wilcoxon rank-sum test was performed to compare results between students who report being moderately to extremely knowledgeable of AI/ML and those who report being slightly or not knowledgeable. When summarizing results, somewhat and strongly agree were combined into an agree category, and conversely, somewhat and strongly disagree were combined into a disagree category. Statistical analysis was performed using commercial software (GraphPad; Prism, 10.2.2).
Results
Study demographics
The survey was distributed to 594 students, with 176 student respondents, yielding a response rate of 29.6%. The respondents included 47/152 (30.9%) of the first-year students, 31/146 (21.2%) of the second-year students, 58/144 (40.3%) of the third-year students, and 40/152 (26.3%) of the fourth-year students (Figure 1).
Demographic data. Year of the survey respondents’ professional veterinary medicine (PVM) curriculum.
Citation: American Journal of Veterinary Research 2025; 10.2460/ajvr.25.03.0082
Knowledge of and exposure to AI/ML
Of the respondents, 158/176 (89.8%) students answered yes to “Q2. Have you heard of artificial intelligence or machine learning?” (Figure 2). This included 38/47 (80.9%), 29/31 (93.5%), 53/58 (91.4%), and 38/40 (95%) of first-, second-, third-, and fourth-year PVM students, respectively (P = .95). When asked “Q3. Have you used a veterinary-based AI/ML tool?” 21/176 (11.9%) of the respondents answered yes, including 4/47 (8.5%), 2/31 (6.5%), 7/58 (12.1%), and 8/40 (20%) of students in PVM years 1 through 4, respectively (P = .32). Sixty-five of 176 (36.9%) students responded yes to “Q4. Have any AI/ML concepts been introduced to you during your veterinary curriculum?” This included 15/47 (31.9%), 8/31 (25.8%), 24/58 (41.4%), and 18/40 (45%) students in PVM years 1 through 4, respectively (P = .29).
Professional veterinary medicine students’ knowledge of AI and machine learning (AI/ML). A—Assessment of knowledge of AI/ML with possible responses of yes (teal) or no (red). B—Assessment of knowledge of AI/ML with possible responses: extremely knowledgeable or interested (dark teal), very knowledgeable or interested (light teal), moderately knowledgeable or interested (gray), slightly knowledgeable or interested (light red), or not at all knowledgeable or interested (dark red).
Citation: American Journal of Veterinary Research 2025; 10.2460/ajvr.25.03.0082
Of the 176 respondents asked “Q5. How knowledgeable are you about ML/AI?” none reported they were extremely knowledgeable, 6 (3.4%) were very knowledgeable, 29 (16.5%) were moderately knowledgeable, 91 (51.7%) were slightly knowledgeable, and 50 (28.4%) were not knowledgeable at all (Figure 2). Of the 176 respondents asked “Q6. How interested are you in learning about AI/ML applications in veterinary medicine?” 30 (17.0%) were extremely interested, 54 (30.7%) were very interested, 55 (31.3%) were moderately interested, 35 (19.9%) were slightly interested, and 2 (1.1%) were not interested at all (Figure 3).
Professional veterinary medicine students’ feelings and attitudes toward integrating AI/ML into veterinary medicine. The survey questions presented a statement, and possible responses were strongly agree (dark teal), somewhat agree (light teal), neither agree nor disagree (gray), somewhat disagree (light red), or strongly disagree (dark red).
Citation: American Journal of Veterinary Research 2025; 10.2460/ajvr.25.03.0082
Attitudes toward AI/ML
The responses to questions regarding feelings and attitudes toward AI/ML are summarized in Figure 3. Of the 176 respondents, 104 (59.1%) agreed and 14 (8.0%) disagreed that they would use AI/ML tools in their careers. Of the 176 respondents, 83 students (47.2%) agreed that AI/ML tools would become the standard of care in veterinary medicine, while 42 (23.4%) disagreed.
When assessing the remaining questions with a positive connotation, 124/176 students (70.5%) agreed with “AI/ML is a partner rather than a competitor,” 135/176 (76.7%) agreed with the statement “AI/ML will improve veterinary medicine,” 112/176 (63.6%) agreed with the statement “AI/ML should be a part of the veterinary school curriculum,” and 51/176 (29.0%) agreed that “Patients will be safer if a veterinarian incorporates AI/ML tools.” When assessing the questions with negative connotations, 66/176 (38%) students disagreed with the statement “Integration of AI/ML into veterinary medicine frightens me,” 40/176 (23%) disagreed with “AI/ML will replace some veterinarians,” 131/176 (74%) with “AI/ML tools must be viewed skeptically,” and 126/176 (72%) with “I am concerned about liability when using an AI/ML tool.”
When students were stratified based on their AI/ML knowledge level, there were no statistically significant differences in their feelings and attitudes toward AI (Q7 to Q16) between students with a moderate to extremely high knowledge of AI and those with a slight or no knowledge of AI (data not shown).
Open-ended comments regarding AI/ML
Sixteen students responded to the optional question, “Provide any additional comments in regards to your experience with AI/ML.” When the responses were categorized based on content, 8 respondents commented on wanting to learn more about AI/ML or having a low baseline knowledge base, and 8 commented about an experience using an AI/ML tool in practice or in a research setting. Further, 9 students responded with concerns surrounding the integration of AI/ML in veterinary medicine. These concerns encompassed ethical concerns surrounding ownership of training data, questions related to model performance evaluation, veterinary clients by-passing veterinarian advice in favor of AI tools, and veterinarians becoming complacent.
Discussion
This study assessed the self-reported knowledge base and attitudes toward the use of AI in veterinary medicine among a cohort of PVM students. These veterinary students expressed low exposure rates to AI/ML in their curriculum and low knowledge base but demonstrated that most students are eager to learn about the topic. Most students agreed that AI/ML tools will be used during their veterinary careers and will improve veterinary medicine. This study supports the promotion of AI literacy in veterinary medical curricula and the growing acceptance of integrating AI tools into workflows.
Our findings align with studies23 of other healthcare students, suggesting low levels of knowledge. However, most students support AI integration into the curriculum18 and strongly believe AI will improve healthcare.25 One report24 of veterinary students in Turkey also determined that most veterinary students had heard of AI and most agreed that AI should be used in veterinary medicine.
Basic AI/ML literacy is crucial to be able to deploy these tools rationally.21 Our study indicated that less than 20% of the students were moderately or very knowledgeable about AI/ML and 36% had AI concepts introduced during their professional veterinary curriculum. These data suggest that veterinary professionals may not be receiving the training needed to appropriately leverage AI/ML tools and understand the limitations, ethical considerations, and potential liability when incorporating them into their veterinary practice. Interestingly, results from this survey support an overall positive view of AI/ML tools, with students disagreeing with statements relating to skepticism or veterinary liability when applying AI/ML tools in practice. This further highlights the need for AI/ML education, as tools must be critically evaluated by the end user before integration into the workflow, as there is a lack of regulatory approvals for veterinary AI/ML tools, as exists in the human healthcare setting. Further, users must understand that errors in AI tools, such as hallucinations in large language models, have been well documented, and all output needs critical evaluation to prevent errors in clinical decision-making or patient harm. The inclusion of AI in veterinary and medical curricula has been proposed to improve AI literacy and instill the skillset necessary to critically evaluate all AI/ML tools in the next generation of healthcare providers.26,27
Survey respondents generally had positive attitudes about integrating AI/ML into veterinary workflows. Students agreed that AI/ML would be a partner rather than a competitor, that it would improve veterinary medicine, and that AI/ML tools would become the standard of care in veterinary medicine. This is despite agreeing with the statement that AI will likely replace some veterinarians, and most students neither agree nor disagree that AI will make patients safer. This is similar to findings in other studies where most healthcare students report a positive outlook on integrating AI into their field, despite some agreeing that it may replace some physicians.13,14,25,28
The major limitation of this study was the narrow cohort, including students from only one veterinary school in the United States. Larger scale surveys assessing perspectives worldwide would give a broader overview of students’ attitudes and feelings toward AI. Further, this study was advertised to students to complete voluntarily. This could have led to selection bias, such that students interested in the topic may have been more inclined to participate, skewing our results.
This study suggests that participating veterinary students understand the importance of AI/ML tools in veterinary medicine and are motivated to learn more about the subject. Despite their self-reported low level of knowledge on the topic, they anticipate integration into their career with low rates of skepticism and fear. Veterinary schools must proactively prepare students to understand how to apply AI technology and understand its limitations.
Acknowledgments
None reported.
Disclosures
The authors have nothing to disclose. No AI-assisted technologies were used in the composition of this manuscript.
Funding
The authors have nothing to disclose.
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