B8437 - Digitalisation of Information, Big Data, and Artificial Intelligence

Learning outcomes

By the end of the course, the student gains a solid knowledge on the importance of digitalisation of information, what type of big data can be used in different domains of veterinary medicine and how Artificial Intelligence can support the veterinary work. In particular, the student: understands the importance of information digitalisation and data sharing process; is able to comprehend what big data are, their origin, and why big data can be useful in the veterinary profession; acquires knowledge to understand the values and limitations of artificial intelligence applied in different contexts in the veterinary domain.

Course contents

This course is part of the Integrated Course “Fundamentals of the Veterinary Profession (I.C.)”.

The integrated course "Fundamentals of the veterinary profession" aims to provide the student with adequate knowledge of the fundamental principles of veterinary legislation and economics, in both national and international contexts, the concepts of One Health and Circular Health, as well as the role of the veterinarian and national and supranational institutions in safeguarding animal and human health and welfare. Additionally, the student gains expertise in information digitalization, big data, and artificial intelligence, as well as in ethics, communication, and teamwork, all of which are essential for personal and professional growth.

The Integrated Course “Fundamentals of the Veterinary Profession (I.C.)” contributes to the achievement of the following Day One Competences: 1.2, 1.7, 1.9, 1.10, 1.11, 1.14, 1.24, 1.26

CONTENUTI SPECIFICI DELL’INSEGNAMENTO:

There is relevant growth and potential in the development, application, and clinical use of artificial intelligence (AI) in human and veterinary medicine. These new opportunities also come with challenges to understanding, interpreting, and adopting this powerful and evolving technology given the pace of research and commercial product developments.

The course covers the following topics, which will be exploited through applicative examples in veterinary medicine and related fields.

• Introduction to information digitalization. Why is it important?
• Definition of big data. Difference between data and big data.
• How big are big data? Examples of big data suitable for use in veterinary medicine
• Definition of artificial intelligence.
• Opportunities, limitations and ethics in the use of artificial intelligence
• Examination of how AI is used through case studies in veterinary medicine.
• Real-world AI-powered tools of AI in veterinary Medicine: an overview of state-of-the-art AI solutions

Readings/Bibliography

Readings/Bibliography

The teaching materials for this course are available on the Virtuale Learning Environment (https://virtuale.unibo.it/?lang=en ).

Required readings:

• University Policy for an Ethical and Responsible Use of Generative Artificial Intelligence in Teaching and Research (https://www.unibo.it/en/university/statute-standards-strategies-and-reports/artificial-intelligence )

Supplementary reading:

• Akinsulie OC, et al.,. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Front Vet Sci. 2024 Jan 31;11:1347550. doi: 10.3389/fvets.2024.1347550. PMID: 38356661; PMCID: PMC10864457.
• Chu CP (2024) ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research. Front. Vet. Sci. 11:1395934. doi: 10.3389/fvets.2024.1395934
• Silvia Burti, et al., , Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations, Research in Veterinary Science, Volume 175, 2024, 105317, ISSN 0034-5288,
• https://doi.org/10.1016/j.rvsc.2024.105317.
• Coghlan, S., Quinn, T. Ethics of using artificial intelligence (AI) in veterinary medicine. AI & Soc 39, 2337–2348 (2024). https://doi.org/10.1007/s00146-023-01686-1

Teaching methods

The course includes both theoretical lectures and practical sessions.

a. theoretical lectures: frontal lectures carried out by slides show and other media (videos, tutorials) integrated by Q/A sessions

b. seminar/practical: practical lectures carried out by groups of students under the supervision of the teacher (problem solving approach)

The involvement of external experts is foreseen for specific topics.

Assessment methods

The final exam of the Integrated Course "“Fundamentals of the Veterinary Profession (I.C.)" consists of two parts (scheduled on the same day).

• The first part is a written test comprising multiple choice questions and open questions covering topics from all courses. Each correct question will earn 1 point (multi choice) and 2 points (open question);

No points will be subtracted in case of wrong answer or unanswered question. No supplementary materials such as books, notebooks, class notes, etc. or electronic devices (e.g., calculators, tablets, smartwatches, computers) may be used during the exam, except for those explicitly allowed by the instructor.

The test assignment will last 2h and will be considered as successful with a minimum grade of 18/30. If needed, a follow-up discussion will allow the Candidate to comment on the wrong answers.

• The second part is an oral exam. The oral exam will consist in an oral presentation of an individual/group assignment report covering topics from the course “One Health, Circular Health, and Veterinary Competent Authorities” aimed at evaluating the acquisition of knowledge and competences in this course. The report should be presented through a power point presentation not exceeding 5 slides for a total of 10 minutes oral discussion. The result of the oral exam will be communicated at the end of the session. The minimum passing grade is 18/30. A minimum score of 18/30 is required to pass the oral exam.

The exam is considered passed only if all parts are successfully completed. The final grade is determined by the average of the grades from the various parts of the exam, expressed out of 30. A minimum final grade of 18/30 is required.

Negative results are not graded numerically but recorded as “withdrawn” or “failed” in the electronic transcript on AlmaEsami, and do not affect the student’s academic record.

Grades for individual parts and the final grade will be published on the AlmaEsami platform (https://almaesami.unibo.it/?lang=en ) within 5 working days of the date of the exam.

Students may reject the final grade 3 times, by informing the course examiner via email within 5 working days.

The designated course contact for this course is Prof. Marco De Nardi

Students can register for exams through the AlmaEsami platform (http://almaesami.unibo.it/ ). Exams are scheduled during the designated periods in the academic calendar. Additional sessions are available for students beyond the standard program duration.

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

With regard to the learning assessment, limited, declared, and non-substantive use of AI is permitted for support activities, such as summarization and rephrasing. Substantive use of AI to complete parts of the assessment task is not permitted.

Teaching tools

The theoretical lectures take place in a teaching room properly supplied with multimedia equipment: slide projector for theoretical lessons, computers. Students can use smartphone for interactive sessions. Web survey tools (i.e. Mentimeter or similar) will be used to facilitate interaction with students and stimulate discussions.

During lectures, game-based learning platform (i.e. Kahoot) is used to encourage active learning by multiple choice questions to the audience.

Scientific publications in specialized journals and specialized web sites will be used for interactive discussions.

In case of difficulty understanding the course content, the instructor is available for clarification meetings, which must be scheduled via email.

Office hours

See the website of Marco De Nardi