Using Comprehend, Medical Comprehend, Bedrock and others AI APIs/Services in Analysing Medical Records for Horses

Citation: Oleksii Fonin, "Using Comprehend, Medical Comprehend, Bedrock and others AI APIs/Services in Analysing Medical Records for Horses", Universal Library of Engineering Technology, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article presents a broad-based analysis of the opportunities and limitations of applying artificial intelligence services to the processing of medical records of horses. The study is conducted within an interdisciplinary paradigm that combines methods of text data analysis, a comparative review of machine learning algorithms, and a systematization of the experience of applying natural language processing technologies in veterinary medicine. Special attention is paid to the problems of insufficiently representative datasets, the risks of false-positive classifications, and the need to adapt existing solutions to the specifics of sports medicine. The strengths and weaknesses of various approaches are analyzed, ranging from regular expressions and coding algorithms to deep learning models, including their application for predicting outcomes in horses with abdominal pathologies. It is noted that the greatest effectiveness is demonstrated by hybrid systems that combine automated data extraction with expert validation. From a comparative perspective, it is shown that foreign studies predominantly focus on dogs and cats, whereas the area related to equine sports remains underexplored. It is established that further development requires the creation of specialized horse databases, the standardization of clinical records, and the integration of multi-level analytical models. Promising directions include the automation of condition monitoring, early detection of injuries, and the development of personalized support programs, which makes it possible to shift from reactive treatment to preventive control. The article will be useful for artificial intelligence researchers and equestrian specialists interested in improving diagnostic efficiency, reducing injuries, and implementing innovative technologies in sports medicine.

Keywords: Artificial Intelligence, Medical Records Processing, Horses, Equestrian Sports, Machine Learning.

https://doi.org/10.70315/uloap.ulete.2025.0204001