Unleashing the future: Exploring the transformative prospects of artificial intelligence in veterinary science
DOI:
https://doi.org/10.18006/2024.12(3).297.317Keywords:
Machine learning, Deep learning, Veterinary medicine, Telemedicine, Diagnostic radiology, Predictive medicineAbstract
Artificial intelligence (AI) has emerged as a transformative paradigm, promising revolutionary advancements in animal healthcare. Leveraging AI's unparalleled capacity for rapid data analysis significantly enhances diagnostic precision and speed, thereby facilitating informed decision-making by veterinarians. Predictive medicine powered by AI not only anticipates disease outbreaks but also enables tracking zoonotic diseases and predicting individual health risks for animals. AI helps to generate personalized treatment plans by analyzing genetic, environmental, and historical data. Remote monitoring and telemedicine, empowered by AI, overcome geographical constraints and offer continuous care, enabling veterinarians to track vital signs and intervene promptly. However, as AI becomes integral to veterinary practice, ethical considerations surrounding data privacy, transparency, and responsible AI use are crucial. This review explores the scope of AI in enhancing research and drug development, highlighting its ability to improve the discovery process and contribute to novel therapeutic interventions. It emphasizes the necessity of maintaining a delicate balance between AI-driven automation and the expertise of veterinary professionals. As the veterinary community moves toward embracing the transformative potential of AI, this comprehensive examination provides valuable insights into the current scenario. It discusses the challenges, opportunities, implications, and ethical considerations that shape the future of AI in veterinary science.
References
Abani, S., Volk, H. A., De Decker, S., Fenn, J., Rusbridge, C., et al. (2023). ChatGPT and scientific papers in veterinary neurology; is the genie out of the bottle? Frontiers in Veterinary Science, 10, 1272755. https://doi.org/10.3389/fvets.2023.1272755 DOI: https://doi.org/10.3389/fvets.2023.1272755
Adrien-Maxence, H., Emilie, B., Alois, D. L. C., Michelle, A., Kate, A., et al. (2022). Comparison of error rates between four pretrained DenseNet convolutional neural network models and 13 board-certified veterinary radiologists when evaluating 15 labels of canine thoracic radiographs. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63(4), 456–468. https://doi.org/10.1111/vru.13069 DOI: https://doi.org/10.1111/vru.13069
Akinsulie, O. C., Idris, I., Aliyu, V. A., Shahzad, S., Banwo, O. G., et al. (2024). The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Frontiers in Veterinary Science, 11, 1347550. https://doi.org/10.3389/ fvets.2024.1347550 DOI: https://doi.org/10.3389/fvets.2024.1347550
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. DOI: https://doi.org/10.3390/app13127082
Alhasan, M., & Hasaneen, M. (2021). Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Computerized Medical Imaging and Graphics, 91, 101933. https://doi.org/10.1016/j.compmedimag.2021.101933 DOI: https://doi.org/10.1016/j.compmedimag.2021.101933
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., et al. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z DOI: https://doi.org/10.1186/s12909-023-04698-z
Álvarez-Machancoses, Ó., & Fernández-Martínez, J. L. (2019). Using artificial intelligence methods to speed up drug discovery. Expert Opinion on Drug Discovery, 14(8), 769–777. https://doi.org/10.1080/17460441.2019.1621284 DOI: https://doi.org/10.1080/17460441.2019.1621284
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., et al. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8 DOI: https://doi.org/10.1186/s40537-021-00444-8
Anholt, R. M., Berezowski, J., Maclean, K., Russell, M. L., Jamal, I., & Stephen, C. (2014). The application of medical informatics to the veterinary management programs at companion animal practices in Alberta, Canada: A case study. Preventive Veterinary Medicine, 113(2), 165–174. https://doi.org/10.1016/j.prevetmed.2013.11.005 DOI: https://doi.org/10.1016/j.prevetmed.2013.11.005
Appleby, R. B., & Basran, P. S. (2022). Artificial intelligence in veterinary medicine. Journal of the American Veterinary Medical Association, 260(8), 819–824. https://doi.org/10.2460/ javma.22.03.0093 DOI: https://doi.org/10.2460/javma.22.03.0093
Aubreville, M., Bertram, C. A., Marzahl, C., Gurtner, C., Dettwiler, M., et al. (2020). Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region. Scientific Reports, 10(1), 16447. https://doi.org/10.1038/s41598-020-73246-2 DOI: https://doi.org/10.1038/s41598-020-73246-2
Awaysheh, A., Wilcke, J., Elvinger, F., Rees, L., Fan, W., & Zimmerman, K. L. (2019). Review of Medical Decision Support and Machine-Learning Methods. Veterinary Pathology, 56(4), 512–525. https://doi.org/10.1177/0300985819829524 DOI: https://doi.org/10.1177/0300985819829524
Bansal, G. J. (2006). Digital radiography. A comparison with modern conventional imaging. Postgraduate Medical Journal, 82(969), 425–428. https://doi.org/10.1136/pgmj.2005.038448 DOI: https://doi.org/10.1136/pgmj.2005.038448
Bellamy, J. E. C. (2023). Artificial intelligence in veterinary medicine requires regulation. The Canadian Veterinary Journal = La Revue Veterinaire Canadienne, 64(10), 968–970.
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine, 3, 118. https://doi.org/10.1038/s41746-020-00324-0 DOI: https://doi.org/10.1038/s41746-020-00324-0
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023). The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel, Switzerland), 16(6), 891. https://doi.org/10.3390/ph16060891 DOI: https://doi.org/10.3390/ph16060891
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2 DOI: https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Boissady, E., de La Comble, A., Zhu, X., & Hespel, A.M. (2020). Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 61(6), 619–627. https://doi.org/10.1111/vru.12912 DOI: https://doi.org/10.1111/vru.12912
Bollig, N., Clarke, L., Elsmo, E., & Craven, M. (2020). Machine learning for syndromic surveillance using veterinary necropsy reports. PloS One, 15(2), e0228105. https://doi.org/10.1371/ journal.pone.0228105 DOI: https://doi.org/10.1371/journal.pone.0228105
Bouchemla, F., Akchurin, S. V., Akchurina, I. V., Dyulger, G. P., Latynina, E. S., & Grecheneva, A. V. (2023). Artificial intelligence feasibility in veterinary medicine: A systematic review. Veterinary World, 16(10), 2143–2149. https://doi.org/10.14202/ vetworld.2023.2143-2149 DOI: https://doi.org/10.14202/vetworld.2023.2143-2149
Bouhali, O., Bensmail, H., Sheharyar, A., David, F., & Johnson, J. P. (2022). A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Veterinary Sciences, 9(11), 620. https://doi.org/10.3390/vetsci9110620 DOI: https://doi.org/10.3390/vetsci9110620
Burrell, D. N. (2023). Dynamic Evaluation Approaches to Telehealth Technologies and Artificial Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations. Merits, 3(4), Article 4. https://doi.org/10.3390/ merits3040042 DOI: https://doi.org/10.3390/merits3040042
Burti, S., Longhin Osti, V., Zotti, A., & Banzato, T. (2020). Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs. Veterinary Journal (London, England: 1997), 262, 105505. https://doi.org/10.1016/j.tvjl.2020.105505 DOI: https://doi.org/10.1016/j.tvjl.2020.105505
Carotenuto, G., Malerba, E., Dolfini, C., Brugnoli, F., Giannuzzi, P., Semprini, G., Tosolini, P., & Fracassi, F. (2019). Cushing’s syndrome-an epidemiological study based on a canine population of 21,281 dogs. Open Veterinary Journal, 9(1), 27–32. https://doi.org/10.4314/ovj.v9i1.5 DOI: https://doi.org/10.4314/ovj.v9i1.5
Carovac, A., Smajlovic, F., & Junuzovic, D. (2011). Application of Ultrasound in Medicine. Acta Informatica Medica, 19(3), 168–171. https://doi.org/10.5455/aim.2011.19.168-171 DOI: https://doi.org/10.5455/aim.2011.19.168-171
Celniak, W., Wodziński, M., Jurgas, A., Burti, S., Zotti, A., Atzori, M., Müller, H., & Banzato, T. (2023). Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models. Scientific Reports, 13(1), 19518. https://doi.org/10.1038/s41598-023-46345-z DOI: https://doi.org/10.1038/s41598-023-46345-z
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing Drug Discovery via Artificial Intelligence. Trends in Pharmacological Sciences, 40(8), 592–604. https://doi.org/10.1016/j.tips.2019.06.004 DOI: https://doi.org/10.1016/j.tips.2019.06.004
Cohen, E. B., & Gordon, I. K. (2022). First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology. Veterinary Radiology & Ultrasound, 63(Suppl 1), 840–850. https://doi.org/10.1111/ vru.13171 DOI: https://doi.org/10.1111/vru.13171
Coleman, M. C., & Moore, J. N. (2024). Two artificial intelligence models underperform on examinations in a veterinary curriculum. Journal of the American Veterinary Medical Association, 1–6. https://doi.org/10.2460/javma.23.12.0666 DOI: https://doi.org/10.2460/javma.23.12.0666
Cravero, A., Pardo, S., Sepúlveda, S., & Muñoz, L. (2022). Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review. Agronomy, 12(3), Article 3. https://doi.org/10.3390/agronomy12030748 DOI: https://doi.org/10.3390/agronomy12030748
Currie, G., Hespel, A.M., & Carstens, A. (2023). Australian perspectives on artificial intelligence in veterinary practice. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 64(3), 473–483. https://doi.org/10.1111/vru.13234 DOI: https://doi.org/10.1111/vru.13234
Dave, T., Athaluri, S. A., & Singh, S. (2023). ChatGPT in medicine: An overview of its applications, advantages, limitations, future prospects, and ethical considerations. Frontiers in Artificial Intelligence, 6, 1169595. https://doi.org/10.3389/frai.2023.1169595 DOI: https://doi.org/10.3389/frai.2023.1169595
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94 DOI: https://doi.org/10.7861/futurehosp.6-2-94
de Manuel, A., Delgado, J., Parra Jounou, I., Ausín, T., Casacuberta, D., et al. (2023). Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic. Big Data & Society, 10(1), 20539517231179199. https://doi.org/10.1177/20539517231179199 DOI: https://doi.org/10.1177/20539517231179199
de Melo, R. T., Rossi, D. A., Monteiro, G. P., & Fernandez, H. (2020). Veterinarians and One Health in the Fight Against Zoonoses Such as COVID-19. Frontiers in Veterinary Science, 7, 576262. https://doi.org/10.3389/fvets.2020.576262 DOI: https://doi.org/10.3389/fvets.2020.576262
Dumortier, L., Guépin, F., Delignette-Muller, M.L., Boulocher, C., & Grenier, T. (2022). Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats. Scientific Reports, 12(1), 11418. https://doi.org/10.1038/s41598-022-14993-2 DOI: https://doi.org/10.1038/s41598-022-14993-2
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43. https://doi.org/10.1007/s44163-023-00089-x DOI: https://doi.org/10.1007/s44163-023-00089-x
Elsohaby, I., & Villa, L. (2023). Zoonotic diseases: Understanding the risks and mitigating the threats. BMC Veterinary Research, 19(1), 186. https://doi.org/10.1186/s12917-023-03736-8 DOI: https://doi.org/10.1186/s12917-023-03736-8
Ezanno, P., Picault, S., Beaunée, G., Bailly, X., Muñoz, F., Duboz, R., Monod, H., & Guégan, J.F. (2021). Research perspectives on animal health in the era of artificial intelligence. Veterinary Research, 52(1), 40. https://doi.org/10.1186/s13567-021-00902-4 DOI: https://doi.org/10.1186/s13567-021-00902-4
Ganasegeran, K., & Abdulrahman, S. A. (2019). Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics. Human Behaviour Analysis Using Intelligent Systems, 6, 141–155. https://doi.org/10.1007/978-3-030-35139-7_7 DOI: https://doi.org/10.1007/978-3-030-35139-7_7
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5 DOI: https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Guitian, J., Arnold, M., Chang, Y., & Snary, E. L. (2023). Applications of machine learning in animal and veterinary public health surveillance. Revue Scientifique Et Technique (International Office of Epizootics), 42, 230–241. https://doi.org/10.20506/ rst.42.3366 DOI: https://doi.org/10.20506/rst.42.3366
Han, R., Yoon, H., Kim, G., Lee, H., & Lee, Y. (2023). Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel, Switzerland), 16(9), 1259. https://doi.org/10.3390/ph16091259 DOI: https://doi.org/10.3390/ph16091259
Hangl, J., Krause, S., & Behrens, V. J. (2023). Drivers, barriers and social considerations for AI adoption in SCM. Technology in Society, 74, 102299. https://doi.org/10.1016/j.techsoc.2023.102299 DOI: https://doi.org/10.1016/j.techsoc.2023.102299
Hennessey, E., DiFazio, M., Hennessey, R., & Cassel, N. (2022). Artificial intelligence in veterinary diagnostic imaging: A literature review. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63 Suppl 1, 851–870. https://doi.org/10.1111/vru.13163 DOI: https://doi.org/10.1111/vru.13163
Hespel, A.M., Zhang, Y., & Basran, P. S. (2022). Artificial intelligence 101 for veterinary diagnostic imaging. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63 Suppl 1, 817–827. https://doi.org/10.1111/vru.13160 DOI: https://doi.org/10.1111/vru.13160
Ho, C. W. L., Soon, D., Caals, K., & Kapur, J. (2019). Governance of automated image analysis and artificial intelligence analytics in healthcare. Clinical Radiology, 74(5), 329–337. https://doi.org/10.1016/j.crad.2019.02.005 DOI: https://doi.org/10.1016/j.crad.2019.02.005
Hooper, S. E., Hecker, K. G., & Artemiou, E. (2023). Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Veterinary Sciences, 10(9), 537. https://doi.org/10.3390/vetsci10090537 DOI: https://doi.org/10.3390/vetsci10090537
Huang, D.H., & Chueh, H.E. (2021). Chatbot usage intention analysis: Veterinary consultation. Journal of Innovation & Knowledge, 6(3), 135–144. https://doi.org/10.1016/j.jik.2020.09.002 DOI: https://doi.org/10.1016/j.jik.2020.09.002
Hur, B., Hardefeldt, L. Y., Verspoor, K., Baldwin, T., & Gilkerson, J. R. (2022). Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning. Australian Veterinary Journal, 100(5), 220–222. https://doi.org/10.1111/avj.13145 DOI: https://doi.org/10.1111/avj.13145
Ibrahim, A., Lashen, A., Toss, M., Mihai, R., & Rakha, E. (2022). Assessment of mitotic activity in breast cancer: Revisited in the digital pathology era. Journal of Clinical Pathology, 75(6), 365–372. https://doi.org/10.1136/jclinpath-2021-207742 DOI: https://doi.org/10.1136/jclinpath-2021-207742
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001 DOI: https://doi.org/10.1016/j.aac.2022.10.001
Jiang, B., Yin, X., & Song, H. (2020a). Single-stream long-term optical flow convolution network for action recognition of lameness dairy cow. Computers and Electronics in Agriculture, 175, 105536. https://doi.org/10.1016/j.compag.2020.105536 DOI: https://doi.org/10.1016/j.compag.2020.105536
Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020b). Supervised Machine Learning: A Brief Primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002 DOI: https://doi.org/10.1016/j.beth.2020.05.002
Jiang, Y., Irvin, J. A., Ng, A. Y., & Zou, J. (2024). VetLLM: Large Language Model for Predicting Diagnosis from Veterinary Notes. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 29, 120–133.
Johnson, K. B., Wei, W.Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884 DOI: https://doi.org/10.1111/cts.12884
Joslyn, S., & Alexander, K. (2022). Evaluating artificial intelligence algorithms for use in veterinary radiology. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63 Suppl 1, 871–879. https://doi.org/10.1111/vru.13159 DOI: https://doi.org/10.1111/vru.13159
Kamel Boulos, M. N., Peng, G., & VoPham, T. (2019). An overview of GeoAI applications in health and healthcare. International Journal of Health Geographics, 18, 7. https://doi.org/10.1186/s12942-019-0171-2 DOI: https://doi.org/10.1186/s12942-019-0171-2
Kim, E., Fischetti, A. J., Sreetharan, P., Weltman, J. G., & Fox, P. R. (2022). Comparison of artificial intelligence to the veterinary radiologist’s diagnosis of canine cardiogenic pulmonary edema. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63(3), 292–297. https://doi.org/10.1111/vru.13062 DOI: https://doi.org/10.1111/vru.13062
La Perle, K. M. D. (2019). Machine Learning and Veterinary Pathology: Be Not Afraid! Veterinary Pathology, 56(4), 506–507. https://doi.org/10.1177/0300985819848504 DOI: https://doi.org/10.1177/0300985819848504
Leary, D., & Basran, P. S. (2022). The role of artificial intelligence in veterinary radiation oncology. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63 Suppl 1, 903–912. https://doi.org/10.1111/vru.13162 DOI: https://doi.org/10.1111/vru.13162
Li, S., Wang, Z., Visser, L. C., Wisner, E. R., & Cheng, H. (2020). Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 61(6), 611–618. https://doi.org/10.1111/vru.12901 DOI: https://doi.org/10.1111/vru.12901
Lungren, M. P., & Wilson, D. U. (2022). Artificial intelligence in veterinary care will be a major driving force behind ai advancements in healthcare. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63 Suppl 1, 913–915. https://doi.org/10.1111/vru.13161 DOI: https://doi.org/10.1111/vru.13161
Lustgarten, J. L., Zehnder, A., Shipman, W., Gancher, E., & Webb, T. L. (2020). Veterinary informatics: Forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open, 3(2), 306–317. https://doi.org/10.1093/jamiaopen/ooaa005 DOI: https://doi.org/10.1093/jamiaopen/ooaa005
Lynch, S. R., Bothwell, T., & SUSTAIN Task Force on Iron Powders. (2007). A comparison of physical properties, screening procedures and a human efficacy trial for predicting the bioavailability of commercial elemental iron powders used for food fortification. International Journal for Vitamin and Nutrition Research. Internationale Zeitschrift Fur Vitamin- Und Ernahrungsforschung. Journal International De Vitaminologie Et De Nutrition, 77(2), 107–124. https://doi.org/10.1024/0300-9831.77.2.107 DOI: https://doi.org/10.1024/0300-9831.77.2.107
Marks, N. (2024). The Progressive Veterinary Practice. The Veterinary Clinics of North America. Small Animal Practice, 54(2), 265–276. https://doi.org/10.1016/j.cvsm.2023.10.011 DOI: https://doi.org/10.1016/j.cvsm.2023.10.011
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297 DOI: https://doi.org/10.1016/j.heliyon.2024.e26297
Müller, T. R., Solano, M., & Tsunemi, M. H. (2022). Accuracy of artificial intelligence software for the detection of confirmed pleural effusion in thoracic radiographs in dogs. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63(5), 573–579. https://doi.org/10.1111/vru.13089 DOI: https://doi.org/10.1111/vru.13089
Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., et al. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322 DOI: https://doi.org/10.3389/fsurg.2022.862322
Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel, Switzerland), 13(17), 2760. https://doi.org/10.3390/ diagnostics13172760 DOI: https://doi.org/10.3390/diagnostics13172760
Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors (Basel, Switzerland), 23(16), 7045. https://doi.org/10.3390/s23167045 DOI: https://doi.org/10.3390/s23167045
Niazi, S. K. (2023). The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Design, Development and Therapy, 17, 2691–2725. https://doi.org/10.2147/DDDT.S424991 DOI: https://doi.org/10.2147/DDDT.S424991
Nie, A., Zehnder, A., Page, R. L., Zhang, Y., Pineda, A. L., Rivas, M. A., Bustamante, C. D., & Zou, J. (2018). DeepTag: Inferring diagnoses from veterinary clinical notes. NPJ Digital Medicine, 1, 60. https://doi.org/10.1038/s41746-018-0067-8 DOI: https://doi.org/10.1038/s41746-018-0067-8
Nitta, N., Sugimura, T., Isozaki, A., Mikami, H., Hiraki, K., et al. (2018). Intelligent Image-Activated Cell Sorting. Cell, 175(1), 266-276.e13. https://doi.org/10.1016/j.cell.2018.08.028 DOI: https://doi.org/10.1016/j.cell.2018.08.028
Nosrati, H., & Nosrati, M. (2023). Artificial Intelligence in Regenerative Medicine: Applications and Implications. Biomimetics, 8(5), Article 5. https://doi.org/10.3390/biomimetics8050442 DOI: https://doi.org/10.3390/biomimetics8050442
Nyquist, M. L., Fink, L. A., Mauldin, G. E., & Coffman, C. R. (2024). Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program. Journal of Veterinary Dentistry, 8987564231221071. https://doi.org/10.1177/ 08987564231221071 DOI: https://doi.org/10.1177/08987564231221071
Ogilvie, T., & Kastelic, J. (2022). Technology is rapidly changing our world, including veterinary medicine. The Canadian Veterinary Journal = La Revue Veterinaire Canadienne, 63(12), 1177–1178.
Olawade, D. B., Wada, O. J., David-Olawade, A. C., Kunonga, E., Abaire, O., & Ling, J. (2023). Using artificial intelligence to improve public health: A narrative review. Frontiers in Public Health, 11, 1196397. https://doi.org/10.3389/fpubh.2023.1196397 DOI: https://doi.org/10.3389/fpubh.2023.1196397
Öztürk, H., Özgür, A., & Ozkirimli, E. (2018). DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics (Oxford, England), 34(17), i821–i829. https://doi.org/10.1093/ bioinformatics/bty593 DOI: https://doi.org/10.1093/bioinformatics/bty593
Paudyal, R., Shah, A. D., Akin, O., Do, R. K. G., Konar, A. S., Hatzoglou, V., Mahmood, U., Lee, N., Wong, R. J., Banerjee, S., Shin, J., Veeraraghavan, H., & Shukla-Dave, A. (2023). Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers, 15(9), 2573. https://doi.org/10.3390/cancers15092573 DOI: https://doi.org/10.3390/cancers15092573
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010 DOI: https://doi.org/10.1016/j.drudis.2020.10.010
Paynter, A. N., Dunbar, M. D., Creevy, K. E., & Ruple, A. (2021). Veterinary Big Data: When Data Goes to the Dogs. Animals: An Open Access Journal from MDPI, 11(7), 1872. https://doi.org/10.3390/ani11071872 DOI: https://doi.org/10.3390/ani11071872
Pereira, A. I., Franco-Gonçalo, P., Leite, P., Ribeiro, A., Alves-Pimenta, M. S., Colaço, B., et al. (2023). Artificial Intelligence in Veterinary Imaging: An Overview. Veterinary Sciences, 10(5), 320. https://doi.org/10.3390/vetsci10050320 DOI: https://doi.org/10.3390/vetsci10050320
Perera, T. R. W., Skerrett-Byrne, D. A., Gibb, Z., Nixon, B., & Swegen, A. (2022). The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges. Animals : An Open Access Journal from MDPI, 12(17), 2194. https://doi.org/10.3390/ani12172194 DOI: https://doi.org/10.3390/ani12172194
Pomerantz, L. K., Solano, M., & Kalosa-Kenyon, E. (2023). Performance of a commercially available artificial intelligence software for the detection of confirmed pulmonary nodules and masses in canine thoracic radiography. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 64(5), 881–889. https://doi.org/10.1111/vru.13287 DOI: https://doi.org/10.1111/vru.13287
Pratiwi, R., Ramadhanti, S. P., Amatulloh, A., Megantara, S., & Subra, L. (2023). Recent Advances in the Determination of Veterinary Drug Residues in Food. Foods (Basel, Switzerland), 12(18), 3422. https://doi.org/10.3390/foods12183422 DOI: https://doi.org/10.3390/foods12183422
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., et al. (2023). AI in drug discovery and its clinical relevance. Heliyon, 9(7), e17575. https://doi.org/10.1016/j.heliyon.2023.e17575 DOI: https://doi.org/10.1016/j.heliyon.2023.e17575
Rezaei, T., Khouzani, P. J., Khouzani, S. J., Fard, A. M., Rashidi, S., et al. (2023). Integrating Artificial Intelligence into Telemedicine: Revolutionizing Healthcare Delivery. Kindle, 3(1), 1–161. DOI: https://doi.org/10.62486/latia20233
Rezayi, S., Niakan Kalhori, S. R., & Saeedi, S. (2022). Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. BioMed Research International, 2022, 7842566. https://doi.org/10.1155/2022/7842566 DOI: https://doi.org/10.1155/2022/7842566
Samui, P., & Kothari, D. P. (2011). Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica, 18(1), 53–58. https://doi.org/10.1016/ j.scient.2011.03.007 DOI: https://doi.org/10.1016/j.scient.2011.03.007
Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science (New York, N.Y.), 361(6400), 360–365. https://doi.org/10.1126/science.aat2663 DOI: https://doi.org/10.1126/science.aat2663
Santamaria, S. L., & Zimmerman, K. L. (2011). Uses of informatics to solve real world problems in veterinary medicine. Journal of Veterinary Medical Education, 38(2), 103–109. https://doi.org/10.3138/jvme.38.2.103 DOI: https://doi.org/10.3138/jvme.38.2.103
Sarker, I. H. (2021a). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1 DOI: https://doi.org/10.1007/s42979-021-00815-1
Sarker, I. H. (2021b). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x DOI: https://doi.org/10.1007/s42979-021-00592-x
Sarker, I. H. (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x DOI: https://doi.org/10.1007/s42979-022-01043-x
Schmid, D., Scholz, V. B., Kircher, P. R., & Lautenschlaeger, I. E. (2022). Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs. Veterinary Radiology & Ultrasound: The Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 63(6), 763–770. https://doi.org/10.1111/vru.13132 DOI: https://doi.org/10.1111/vru.13132
Schofield, I., Brodbelt, D. C., Kennedy, N., Niessen, S. J. M., Church, D. B., Geddes, R. F., & O’Neill, D. G. (2021). Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice. Scientific Reports, 11(1), 9035. https://doi.org/10.1038/s41598-021-88440-z DOI: https://doi.org/10.1038/s41598-021-88440-z
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: How artificial intelligence transforms population and personalized health. Npj Digital Medicine, 1(1), 1–2. https://doi.org/10.1038/s41746-018-0058-9 DOI: https://doi.org/10.1038/s41746-018-0058-9
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1485. https://doi.org/10.1002/widm.1485 DOI: https://doi.org/10.1002/widm.1485
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. Sensors (Basel, Switzerland), 23(23), 9498. https://doi.org/10.3390/s23239498 DOI: https://doi.org/10.3390/s23239498
Sharma, N., Sharma, R., & Jindal, N. (2021). Machine Learning and Deep Learning Applications-A Vision. Global Transitions Proceedings, 2(1), 24–28. https://doi.org/10.1016/j.gltp.2021.01.004 DOI: https://doi.org/10.1016/j.gltp.2021.01.004
Sharma, S., Rawal, R., & Shah, D. (2023). Addressing the challenges of AI-based telemedicine: Best practices and lessons learned. Journal of Education and Health Promotion, 12, 338. https://doi.org/10.4103/jehp.jehp_402_23 DOI: https://doi.org/10.4103/jehp.jehp_402_23
Siachos, N., Neary, J. M., Smith, R. F., & Oikonomou, G. (2024). Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities. The Veterinary Journal, 304, 106091. https://doi.org/10.1016/j.tvjl.2024.106091 DOI: https://doi.org/10.1016/j.tvjl.2024.106091
Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), Article 5. https://doi.org/10.3390/ computers12050091 DOI: https://doi.org/10.3390/computers12050091
Vilhekar, R. S., & Rawekar, A. (2024). Artificial Intelligence in Genetics. Cureus, 16(1), e52035. https://doi.org/10.7759/ cureus.52035 DOI: https://doi.org/10.7759/cureus.52035
Visan, A. I., & Negut, I. (2024). Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life, 14(2), Article 2. https://doi.org/10.3390/life14020233 DOI: https://doi.org/10.3390/life14020233
von Lilienfeld, O. A. (2018). Quantum Machine Learning in Chemical Compound Space. Angewandte Chemie International Edition, 57(16), 4164–4169. https://doi.org/10.1002/anie.201709686 DOI: https://doi.org/10.1002/anie.201709686
Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., & Chavda, V. P. (2023). Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics, 15(7), 1916. https://doi.org/10.3390/pharmaceutics15071916 DOI: https://doi.org/10.3390/pharmaceutics15071916
Wu, K., Li, X., Zhou, Z., Zhao, Y., Su, M., et al. (2024). Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling. Frontiers in Pharmacology, 15. https://doi.org/10.3389/fphar.2024.1330855 DOI: https://doi.org/10.3389/fphar.2024.1330855
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., et al. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation (Cambridge (Mass.)), 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179 DOI: https://doi.org/10.1016/j.xinn.2021.100179
Yang, S., & Kar, S. (2023). Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity. Artificial Intelligence Chemistry, 1(2), 100011. https://doi.org/10.1016/j.aichem.2023.100011 DOI: https://doi.org/10.1016/j.aichem.2023.100011
Yelne, S., Chaudhary, M., Dod, K., Sayyad, A., & Sharma, R. (2023). Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus, 15(11), e49252. https://doi.org/10.7759/cureus.49252 DOI: https://doi.org/10.7759/cureus.49252
Yoon, Y., Hwang, T., & Lee, H. (2018). Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks. Veterinary Journal (London, England: 1997), 237, 43–48. https://doi.org/10.1016/j.tvjl.2018.05.009 DOI: https://doi.org/10.1016/j.tvjl.2018.05.009
You, J., McLeod, R. D., & Hu, P. (2019). Predicting drug-target interaction network using deep learning model. Computational Biology and Chemistry, 80, 90–101. https://doi.org/10.1016/ j.compbiolchem.2019.03.016 DOI: https://doi.org/10.1016/j.compbiolchem.2019.03.016
Zad, N., Tell, L. A., Ampadi Ramachandran, R., Xu, X., Riviere, J. E., et al. (2023). Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association, 179, 113920. https://doi.org/10.1016/j.fct.2023.113920 DOI: https://doi.org/10.1016/j.fct.2023.113920
Zhang, L., Guo, W., Lv, C., Guo, M., Yang, M., Fu, Q., & Liu, X. (2024). Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress. Animal Research and One Health, 2(1), 93–109. https://doi.org/10.1002/aro2.44 DOI: https://doi.org/10.1002/aro2.44
Zhang, L., Tan, J., Han, D., & Zhu, H. (2017). From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discovery Today, 22(11), 1680–1685. https://doi.org/10.1016/j.drudis.2017.08.010 DOI: https://doi.org/10.1016/j.drudis.2017.08.010
Zuraw, A., & Aeffner, F. (2022). Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Veterinary Pathology, 59(1), 6–25. https://doi.org/10.1177/03009858211040484 DOI: https://doi.org/10.1177/03009858211040484
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Experimental Biology and Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
