Author(s): Emily Parker, Sofia Rodriguez and Maxime Dubois

Abstract: The healthcare sector is increasingly adopting advanced technologies to enhance diagnostic accuracy and improve patient outcomes. Among these technologies, computer vision (CV) has emerged as a transformative tool in medical imaging, offering substantial advancements in disease detection, treatment planning, and patient monitoring. This paper examines the applications of computer vision in healthcare, specifically within the context of medical imaging. Medical imaging technologies, such as X-rays, CT scans, MRIs, and ultrasounds, have long been pivotal in diagnosing a variety of conditions, from fractures to cancer. However, the manual analysis of these images is time-consuming and subject to human error. Computer vision addresses these challenges by automating image interpretation, thereby enhancing efficiency and accuracy. This research investigates the integration of machine learning (ML) and deep learning (DL) algorithms in medical imaging for automating the detection of conditions like tumours, cardiovascular diseases, and neurological disorders. Additionally, the paper examines the benefits of CV in improving workflows, reducing healthcare costs, and assisting in real-time decision-making. We also discuss challenges associated with the adoption of CV in healthcare, such as data privacy concerns, the need for large annotated datasets, and the integration of AI models into clinical practice. The paper concludes with a review of ongoing research and future directions for CV applications in healthcare, highlighting the importance of interdisciplinary collaboration between computer scientists, clinicians, and regulatory bodies in advancing these technologies. By emphasizing case studies and recent developments, this paper aims to contribute to the understanding of how computer vision can revolutionize healthcare delivery and improve patient care.

DOI: 10.33545/27076571.2026.v7.i1a.236

Pages: 14-18 | Views: 8 | Downloads: 3

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