eng Rovedar Journal of Lab Animal Research 2980-9703 2025-10-30 4 5 57 62 10.58803/jlar.v4i5.84 94 Negar Ababaf Shoushtari 0 https://orcid.org/0009-0000-4748-5760 Amin Shahinzadeh 0 https://orcid.org/0009-0008-3249-2878 Elaheh Mahmoodi 1 https://orcid.org/0009-0000-9542-3441 Bardia Mohammadi 0 https://orcid.org/0009-0005-5597-5555 Marziyeh Saki 2 https://orcid.org/0009-0007-9897-9673 Safa Najafi 3 https://orcid.org/0000-0003-2454-2851 Sirous Rafiei Asl 4 https://orcid.org/0000-0003-1219-1054 Doctor of Veterinary Medicine Student, Islamic Azad University of Shoushtar, Shoushtar, Iran Doctor of Veterinary Medicine, Graduated, Islamic Azad University of Shoushtar, Shoushtar, Iran Doctor of Veterinary Medicine, Graduated, Islamic Azad University of Shoushtar, Shoushtar, Iran. Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Nursing Care Research Center in Chronic Diseases, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Department of Medical and Surgical Nursing, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Cancer, Environmental and Petroleum Pollutants Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Department of Pathology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Alimentary Tract Research Center, Clinical Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Recent advances in artificial intelligence (AI) have opened new horizons in medical sciences, especially in pathology and surgery. Although AI can enhance diagnostic accuracy and surgical outcomes, its widespread clinical use faces challenges such as validation, interpretability, and ethical and legal issues. The present study aimed to examine recent developments in AI across pathology, including automated recognition of pathological images and surgery (robotic surgical assistant systems and predictive outcome analysis). The present study analyzed current challenges in implementing AI and provided a perspective on how it can become a reliable tool to improve the quality of patient care and treatment outcomes. An initial and extensive search was conducted across reputable scientific databases, including Google Scholar, PubMed, and Scopus, using relevant keywords. Within the clinical pathology section, this review provided a comprehensive examination of how deep learning algorithms and image processing are automating and enhancing data analysis in cytology, hematology, histopathology, and digital pathology. The ability of AI to discover nuanced patterns in vast datasets, greatly improve diagnostic accuracy, and speed up reporting are all impressive capabilities. One of these challenges is the need for large, standardized datasets for algorithm training. Other challenges include clinical validation, ethical concerns, and early costs. In conclusion,  this review anticipates that the integration of AI into clinical pathology and surgical workflows promises enhanced quality of care for laboratory animals alongside more accurate and reliable insights. https://jlar.rovedar.com/index.php/JLAR/article/view/84 Artificial intelligence Clinical pathology Digital pathology Laboratory animal Surgery