The integration of artificial intelligence (AI) and advanced analytics in healthcare has sparked debate in both practice and academia, particularly concerning its potential to displace and/or replace jobs (Lee and Yoon, 2021; Thompson et al., 2020). However, the inherent complexity of diagnostic imaging examinations does make AI a set of appealing technologies to be equipped in healthcare facilities (Zeltzer et al. 2023) to improve care quality. This study aims to develop a cost-effective AI solution that augments rather than replaces the existing medical professionals, adhering to the established ethical standards. Specifically, we developed two machine learning models trained on X-ray images: (1) an image classification model to distinguish between fractured and non-fractured images and (2) an object detection model to pinpoint fracture locations within the images. The classification model, trained on 612 fractured and 620 non-fractured images, achieved an accuracy of 64.3%, with a precision of 66% for fractured images and 62% for non-fractured ones. The object detection model, utilizing the same dataset with additional bounding box annotations for fractures, showed a box precision of 57.1% and a mean average precision of 47.7% at the 50th percentile Intersection over Union (IoU). These results demonstrate the feasibility of AI as a supportive tool in medical diagnostics, offering a promising avenue for enhancing healthcare services without increasing resource demand on the existing medical workforce. This study offers key implications for healthcare practice. First, the observed accuracies and precisions of our models can add ease to the imaging diagnostics process for both entry-level and experienced medical professionals. Second, our machine-learning model training operations help provide solutions to the nuanced challenges in automating AI diagnostics for healthcare practitioners. Our future work will focus on refining these models to better navigate the intricate patterns of fractures, potentially integrating more advanced imaging techniques or hybrid AI approaches. References Lee, D., and Yoon, S. N. 2021. “Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges,” International Journal of Environmental Research and Public Health (18:1), pp.271. Thompson, S., Whitaker, J., Kohli, R., and Jones, C. 2020. “Chronic Disease Management: How IT and Analytics Create Healthcare Value through the Temporal Displacement of Care,” MIS Quarterly (44:1), pp. 227-256. Zeltzer, D., Herzog, L., Pickman, Y., Steuerman, Y., Ber, R. I., Kugler, Z., ... and Ebbert, J. O. 2023. “Diagnostic Accuracy of Artificial Intelligence in Virtual Primary Care,” Mayo Clinic Proceedings: Digital Health (1:4), pp. 480-489.