_1788848829 20260908062709000 Rovedar Daryoushbabazadeh@gmail.com Rovedar Journal of Lab Animal Research J Lab Anim. Res 2980-9703 04 30 2026 5 2 Mohammad Mehdi Ranjbar Kamrani https://orcid.org/0009-0007-6559-3782 Abdolhossein Malekian https://orcid.org/0009-0002-0858-0620 Mahsa Onagh https://orcid.org/0009-0003-0174-2399 Mohammad Manian https://orcid.org/0009-0003-4353-2242 Mohammad Shahraki https://orcid.org/0009-0009-6138-6350 Maedeh Vasei https://orcid.org/0009-0001-9654-4250 Artificial intelligence (AI) is gradually transforming surgical approaches in human and veterinary medicine, but its application in soft tissue surgery is still in its early stages. The present study aimed to comprehensively examine the roles of AI in soft-tissue surgery and to outline the prospects for using AI in veterinary medicine based on in vivo studies. A comprehensive search was performed across PubMed, Scopus, Web of Science, and the Cochrane Library for peer-reviewed articles published from January 2020 to January 2026. Subsequently, 17 studies were selected and evaluated. The current findings indicated that deep learning algorithms, especially convolutional neural networks and computer vision-based models, have been successful in different areas. These areas included preoperative and intraoperative image navigation and recording, automatic detection of tissue edges and differentiation of vital structures from damaged tissues, and guidance of surgical robots during delicate cutting and suturing movements. In vivo studies in small animal models (rats and rabbits) have confirmed the high accuracy of AI-based technologies under physiological conditions, yet there remains a significant gap between these technologies and their routine clinical application in veterinary medicine. Main challenges in translating AI systems from experimental in vivo studies to routine clinical application in veterinary medicine included the need for large amounts of labeled data across different animal species, anatomical variation between breeds, the high cost of robotic hardware, and the lack of common evaluation guidelines. The present study indicated that veterinary medicine is moving towards the development and use of real-time decision-support systems, telesurgery, and multimodal data integration. 04 30 2026 38 43 https://creativecommons.org/licenses/by/4.0 10.58803/jlar.v5i2.106 https://jlar.rovedar.com/index.php/JLAR/article/view/106 https://jlar.rovedar.com/index.php/JLAR/article/download/106/136 https://jlar.rovedar.com/index.php/JLAR/article/download/106/136 Angelou V, Fiska A, Tsingotjidou A, Patsikas M, and Papazoglou LG. Surgical anatomy of the gastrointestinal tract in cats. Animals. 2023; 13(16): 2670. DOI: 10.3390/ani13162670 Little JP, and Adam CJ. Towards determining soft tissue properties for modelling spine surgery: Current progress and challenges. Med Biol Eng Comput. 2012; 50(2): 199-209. DOI: 10.1007/s11517-011-0848-6 Santos MK, Ferreira Júnior JR, Wada DT, Tenório APM, Barbosa MHN, and Marques PMA. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras. 2019; 52(6): 387-396. DOI: 10.1590/0100-3984.2019.0049 Deivayanai VC, Swaminaathanan P, Vickram AS, Saravanan A, Bibi S, Aggarwal N, et al. Transforming healthcare: The impact of artificial intelligence on diagnostics, pharmaceuticals, and ethical considerations – a comprehensive review. Int J Surg. 2025; 111(7): 4666-4693. DOI: 10.1097/JS9.0000000000002481 Guni A, Varma P, Zhang J, Fehervari M, and Ashrafian H. Artificial intelligence in surgery: The future is now. Eur Surg Res. 2024; 65(1): 22-39. DOI: 10.1159/000536393 Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019; 69(2): 127-157. DOI: 10.3322/caac.21552 Choucha A, Travaglini F, De Simone M, Evin M, Farah K, and Fuentes S. The Da Vinci Robot, a promising yet underused minimally invasive tool for spine surgery: A scoping review of its current role and limits. Neurochirurgie. 2025; 71(3): 101624. DOI: 10.1016/j.neuchi.2024.101624 Li T, Badre A, Alambeigi F, and Tavakoli M. Robotic systems and navigation techniques in orthopedics: A historical review. Appl Sci. 2023; 13(17): 9768. DOI: 10.3390/app13179768 Al-Raeei M. The applications of artificial intelligence in the diagnosis and treatment of diseases in soft tissue: From healthcare to future insights. Explor Res Hypothesis Med. 2026; 11(1): e00034 DOI: 10.14218/ERHM.2025.00034 Misra S, Ramesh K, and Okamura AM. Modeling of tool-tissue interactions for computer-based surgical simulation: A literature review. Presence. 2008; 17(5): 463-491. DOI: 10.1162/pres.17.5.463 Han Z, and Dou Q. A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality. Comput Assist Surg. 2024; 29(1): 2357164. DOI: 10.1080/24699322.2024.2357164 Farokhmoradi H, and Salari-Kakhk F. Errors, contaminations, and confounding factors in in vivo studies: Challenges and practical solutions. J Lab Anim Res. 2025; 4(3): 22-31. DOI: 10.58803/jlar.v4i3.78 Vickram AS, Infant SS, Manikandan S, Sowndharya BB, Gulothungan G, and Chopra H. 3D bio-printed scaffolds and smart implants: Evaluating functional performance in animal surgery models. Ann Med Surg. 2025; 87(6): 3618-3634. DOI: 10.1097/MS9.0000000000003333 Khandia R, Rajput S, and Gurjar P. Artificial Intelligence in animal anatomy: Exploring the technologies, applications, benefits, and challenges. Anat Anz. 2026; 265: 152796. DOI: 10.1016/j.aanat.2026.152796 Kiraga Ł, and Dzikowski A. Ethical concerns of the veterinarian in relation to experimental animals and in vivo research. Animals. 2023; 13(15): 2476. DOI: 10.3390/ani13152476 Pereira AI, Franco-Gonçalo P, Leite P, Ribeiro A, Alves-Pimenta MS, Colaço B, et al. Artificial intelligence in veterinary imaging: An overview. Vet Sci. 2023; 10(5): 320. DOI: 10.3390/vetsci10050320 Hennessey E, DiFazio M, Hennessey R, and Cassel N. Artificial intelligence in veterinary diagnostic imaging: A literature review. Vet Radiol Ultrasound. 2022; 63: 851-870. DOI: 10.1111/vru.13163 Xiao S, Dhand NK, Wang Z, Hu K, Thomson PC, House JK, et al. Review of applications of deep learning in veterinary diagnostics and animal health. Front Vet Sci. 2025; 12: 1511522. DOI: 10.3389/fvets.2025.1511522 Burti S, Banzato T, Coghlan S, Wodzinski M, Bendazzoli M, and Zotti A. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations. Res Vet Sci. 2024; 175: 105317. DOI: 10.1016/j.rvsc.2024.105317 Esteves-Monteiro M, Santos J, Fontes-Sousa AP, and Baptista CS. Diagnostic imaging features of mammary gland tumors in dogs and cats. Animals. 2025; 15(24): 3506. DOI: 10.3390/ani15243506 Jin YG, Wu G, Seo JW, Park SJ, Hur SH, Aliyeva D, et al. AI veterinary assistance: Enhancing clinical decision-making in animal healthcare. IEEE Access. 2025; 13: 119292-119304. DOI: 10.1109/ACCESS.2025.3587787 Boulanger P. MedROAD V2: An AI-integrated electronic medical record system with advanced clinical decision support. AI Med. 2026; 1(1): 4. DOI: 10.3390/aimed1010004 Misra D, Avula V, Wolk DM, Farag HA, Li J, Mehta YB, et al. Early detection of septic shock onset using interpretable machine learners. J Clin Med. 2021; 10(2): 301. DOI: 10.3390/jcm10020301 Park JJ, Tiefenbach J, and Demetriades AK. The role of artificial intelligence in surgical simulation. Front Med Technol. 2022; 4: 1076755. DOI: 10.3389/fmedt.2022.1076755 Panagakis A, Katsaros I, Sotiropoulou M, Mylonakis A, Despotidis M, Sourgiadakis A, et al. Artificial intelligence and 3D reconstruction in complex hepato-pancreato-biliary (HPB) surgery: A comprehensive review of the literature. J Pers Med. 2025; 15(12): 610. DOI: 10.3390/jpm15120610 Park S, Kim T, Lee H, Lee K, and Yoon H. Artificial intelligence-assisted estimation of the center of rotation of angulation (CORA) in canine forelimb radiographs. Pak Vet J. 2025; 45(4): 1857-1866. DOI: 10.29261/pakvetj/2025.314 Kolomenskaya E, Butova V, Poltavskiy A, Soldatov A, and Butakova M. Application of artificial intelligence at all stages of bone tissue engineering. Biomed. 2024; 12(1): 76. DOI: 10.3390/biomedicines12010076 Henkel J, Woodruff MA, Epari DR, Steck R, Glatt V, Dickinson IC, et al. Bone regeneration based on tissue engineering conceptions—a 21st century perspective. Bone Res. 2013; 1(1): 216-248. DOI: 10.4248/BR201303002 Liu H, and Baena FRY. Automatic markerless registration and tracking of the bone for computer-assisted orthopaedic surgery. IEEE Access. 2020; 8: 42010-42020. DOI: 10.1109/ACCESS.2020.2977072 Chauhan S, and Rh A. Physiological motion and registration of abnormalities in liver during focused ultrasound surgery. Phys Procedia. 2016; 87: 48-53. DOI: 10.1016/j.phpro.2016.12.009 Liu H, Han Y, Emerson D, Rabin Y, and Kara LB. A data-driven approach for real-time soft tissue deformation prediction using nonlinear presurgical simulations. PLoS One. 2025; 20(4): e0319196. DOI: 10.1371/journal.pone.0319196 Gomes C, Coheur L, and Tilley P. A review of multimodal AI in veterinary diagnosis: Current trends, challenges, and future directions. IEEE Access. 2025; 13: 97846-97858. DOI: 10.1109/ACCESS.2025.3575343 Tonutti M, Gras G, and Yang GZ. A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery. AI Med. 2017; 80: 39-47. DOI: 10.1016/j.artmed.2017.07.004 Nasr-Esfahani E, Karimi N, Jafari MH, Soroushmehr SMR, Samavi S, Nallamothu BK, et al. Segmentation of vessels in angiograms using convolutional neural networks. Biomed Signal Process Control. 2018; 40: 240-251. DOI: 10.1016/j.bspc.2017.09.012 Weng J, Bhupathiraju SH, Samant T, Dresner A, Wu J, and Samant SS. Convolutional LSTM model for cine image prediction of abdominal motion. Phys Med Biol. 2024; 69(8): 085024. DOI: 10.1088/1361-6560/ad3722 Sabique PV, Ganesh P, and Sivaramakrishnan R. Stereovision based force estimation with stiffness mapping in surgical tool insertion using recurrent neural network. J Supercomput. 2022; 78(12): 14648-14679. DOI: 10.1007/s11227-022-04432-4 Farshian A, Götz M, Cavallaro G, Debus C, Nießner M, Benediktsson JA, et al. Deep-learning-based 3-d surface reconstruction-a survey. Proc IEEE. 2023; 111(11): 1464-1501. DOI: 10.1109/JPROC.2023.3321433 Seetohul J, Shafiee M, and Sirlantzis K. Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions. Sensors. 2023; 23(13): 6202. DOI: 10.3390/s23136202 Wade L, Needham L, McGuigan P, and Bilzon J. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ. 2022; 10: e12995. DOI: 10.7717/peerj.12995 Tejedor P, and Denost Q. The integration of artificial intelligence with robotic instruments in surgical practice. Br J Surg. 2024; 111(10): znae248. DOI: 10.1093/bjs/znae248 Mo F, Lyu Z, Zuo K, Rong S, Cheng H, Zhao W, et al. Embodied intelligence in surgical robotics: From perception-action loops to cognitive collaboration. Authorea Preprints. 2026. DOI: 10.36227/techrxiv.176823151.11578168/v1 Tucan P, Vaida C, Horvath D, Caprariu A, Burz A, Gherman B, et al. Design and experimental setup of a robotic medical instrument for brachytherapy in non-resectable liver tumors. Cancers. 2022; 14(23): 5841. DOI: 10.3390/cancers14235841 Cao Y, Zhang J, Li H, Ren B, Zhao Y, Gao C, et al. A review of critical deep learning-based image guidance technologies for surgical robots. IEEE/ASME Trans Mechatron. 2026; 30(1): 60-81. DOI: 10.1109/TMECH.2025.3584339 Loukas C. Video content analysis of surgical procedures. Surg Endosc. 2018; 32(2): 553-568. DOI: 10.1007/s00464-017-5878-1 Ghaffar Nia N, Kaplanoglu E, and Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell. 2023; 3(1): 5. DOI: 10.1007/s44163-023-00049-5 Liu S, Roemer F, Ge Y, Bedrick EJ, Li ZM, Guermazi A, et al. Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies. Osteoarthr. Cartil. 2023; 31(9): 1242-1248. DOI: 10.1016/j.joca.2023.05.006 Rather IH, Kumar S, and Gandomi AH. Breaking the data barrier: A review of deep learning techniques for democratizing AI with small datasets. Artif Intell Rev. 2024; 57(9): 226. DOI: 10.1007/s10462-024-10859-3 Zhang L, Guo W, Lv C, Guo M, Yang M, Fu Q, et al. Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress. Anim Res One Health. 2024; 2(1): 93-109. DOI: 10.1002/aro2.44 Becker CB, Hansen MS, Nielsen SS, and Jensen HE. Machine-learning for quantitative histopathology of piglet intestinal tissues: Challenges with limited training data. Front Vet Sci. 2025; 12: 1620338. DOI: 10.3389/fvets.2025.1620338 Li M, Jiang Y, Zhang Y, and Zhu H. Medical image analysis using deep learning algorithms. Front Public Health. 2023; 11: 1273253. DOI: 10.3389/fpubh.2023.1273253 Tan KL, Wang P, Sun R, Wong CY, Wood J, Agarwal M, et al. Enhancing animal care through digital tools: A challenge and hindrance perspective of technology adoption in a zoological institution. Behav Inf Technol. 2026; 45(6): 987–1006. DOI: 10.1080/0144929X.2025.2542406 Shlobin NA, Huang J, and Wu C. Learning curves in robotic neurosurgery: A systematic review. Neurosurg Rev. 2022; 46(1): 14. DOI: 10.1007/s10143-022-01908-y Knevel R, and Liao KP. From real-world electronic health record data to real-world results using artificial intelligence. Ann Rheum Dis. 2023; 82(3): 306-311. DOI: 10.1136/ard-2022-222626 Goetz L, Seedat N, Vandersluis R, and van der Schaar M. Generalization-a key challenge for responsible AI in patient-facing clinical applications. NPJ Digit Med. 2024; 7(1): 126. DOI: 10.1038/s41746-024-01127-3 Gefen A, Creehan S, and Black J. Critical biomechanical and clinical insights concerning tissue protection when positioning patients in the operating room: A scoping review. Int Wound J. 2020; 17(5): 1405-1423. DOI: 10.1111/iwj.13408 Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, and Bignami EG. Artificial intelligence in operating room management. J Med Syst. 2024; 48(1): 19. DOI: 10.1007/s10916-024-02038-2 Iber BT, Sethi P, Saini R, Dhiman N, and Madan A. Emerging global technologies in veterinary sciences. In: Gaikwad PS, Shukla VH, and Choudhary P, editors. One health integration: Global perspectives on animal health and sustainable agriculture. Wiley, 2025. p. 227-256. DOI: 10.1002/9781394295982.ch08 Marey A, Arjmand P, Alerab ADS, Eslami MJ, Saad AM, Sanchez N, et al. Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology. Egypt J Radiol Nucl Med. 2024; 55(1): 183. DOI: 10.1186/s43055-024-01356-2 Budha RR, Khan SWAM, Lokhande T, Rao GSNK, and Aaghaz S. Diagnostic and surgical uses of explainable AI (XAI). In: Malviya R, and Sundram S, editors. Explainable and responsible artificial intelligence in healthcare. Wiley, 2025. p. 65-88. DOI: 10.1002/9781394302444.ch3 Zhang Y, Weng Y, and Lund J. Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics. 2022; 12(2): 237. DOI: 10.3390/diagnostics12020237 Zaffino P, Moccia S, De Momi E, and Spadea MF. A review on advances in intra-operative imaging for surgery and therapy: Imagining the operating room of the future. Ann Biomed Eng. 2020; 48(8): 2171-2191. DOI: 10.1007/s10439-020-02553-6 Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, et al. Estimation of physiologic pressures: Invasive and non-invasive techniques, ai models, and future perspectives. Sensors. 2023; 23(12): 5744. DOI: 10.3390/s23125744 Gholizadeh M, Bakhshali MA, Mazlooman SR, Aliakbarian M, Gholizadeh F, Eslami S, et al. Minimally invasive and invasive liver surgery based on augmented reality training: a review of the literature. J Robot Surg. 2023; 17(3): 753-763. DOI: 10.1007/s11701-022-01499-2