Advancing precision medicine in companion animal oncology: Integrating AI, advanced radiology, and surgical innovation
Precision medicine (PM) is bringing about a paradigm shift in the companion animal oncology. PM approach consider a unique treatment and method of diagnosing for each patients. in essence of PM, new cutting-edge technology such as artificial intelligence (AI) hand in hand to novel imaging approach and surgical innovation improving the cancer management. This review examines open-access and published English-language articles containing keywords such as AI, advanced radiology, surgical innovation, and precision medicine in the field of companion animals from 2014 to 2025 in PubMed, Scopus, Web of Science, and Google Scholar. The PM principles have changed surgical oncology, including the integration of oncogenomics, the use of minimally invasive techniques, and robotic-assisted procedures that make it easier to move around. In addition, AI makes these surgical frontiers even bigger by helping with detailed preoperative planning, real-time guidance during surgery, and objective skill training. Also, AI has pivotal role in enhancing diagnostic accuracy, automating image segmentation, and bolstering clinical decision support within modern radiology. This paper also talks about how useful intraoperative imaging is for quickly checking the margins and new precision medicine technologies like Next-Generation Sequencing (NGS), liquid biopsies, and non-coding RNAs for finding and predicting diseases. AI has recently sped up frameworks for comparative oncology, which has made the translation medicine easier. On the other hand, the PM method has its own problems, like high costs and the need for experts to handle big issues. It needs clear rules for sharing data and teams of experts from different fields. This integrated framework is very important for raising the standard of animal care, coming up with new ideas, and making a big difference in both human and animal medicine.
Authors
- Masoud Mohammadnezhad (ORCID: https://orcid.org/0000-0002-5048-9719)
- S. Majd
- Mohammad Arad Zandieh (ORCID: https://orcid.org/0000-0002-6048-3505)
- Mohammad Foad Rasoul Panah
- Maryam Bagheri (ORCID: https://orcid.org/0000-0002-0356-1407)
- M. Rezaee
- Amirhossein Abdolhosseini
Institutions
- Islamic Azad University, Science and Research Branch (IR)
- University of Tehran (IR)
- Islamic Azad University Kazeron (IR)
- Islamic Azad University, Shoushtar Branch (IR)
Publication Details
- Journal
- Letters In Animal Biology
- Published
- 2025-08-17
- DOI
- https://doi.org/10.62310/liab.v5i2.228
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00