Damon Deteso is a diagnostic radiologist who has practiced with Millennium Medical Imaging in Saratoga Springs, New York, since 2004, bringing experience across computed tomography, magnetic resonance imaging, ultrasound, X-ray imaging, and nuclear medicine. His work spans five area hospitals, supporting both emergency room imaging needs and outpatient procedures. Dr. Deteso also spent three years as a medical advisor with Imagen Technologies, where he focused on training artificial intelligence systems to interpret X-ray images, an area of expertise that informs the discussion below. He earned his medical degree from the University of Massachusetts Medical School and completed cross-sectional imaging training at the University of California, San Francisco, and San Francisco General Hospital. He also holds a physics degree from Holy Cross University, where he was inducted into the Sigma Pi Sigma National Physics Honor Society. Outside of medicine, Dr. Deteso enjoys skiing, live music, and travel.
Using Artificial Intelligence in Detecting Pathologies on X-Rays
Artificial intelligence (AI) is transforming health care by improving the speed, accuracy, and efficiency of medical imaging analysis. Among its most promising applications is the development of automated systems that detect pathologies on X-rays.
Interpreting X-rays requires specialized expertise, and even experienced radiologists can face challenges when reviewing large volumes of images under time constraints. AI-powered systems are helping address these challenges by assisting health care professionals in identifying abnormalities more consistently and efficiently.
AI systems designed for X-ray analysis rely primarily on machine learning, particularly deep learning techniques. These systems are trained using extensive datasets containing thousands or even millions of labeled X-ray images. During training, the algorithms learn to recognize patterns associated with normal anatomy, as well as various diseases and injuries.
Convolutional neural networks (CNNs), a type of deep learning model specifically developed for image recognition tasks, have become the foundation of many medical imaging applications. By analyzing subtle visual features that may not be immediately apparent to the human eye, these models can identify abnormalities with remarkable precision.
Automated pathology detection systems can recognize a wide variety of conditions across different parts of the body. In chest X-rays, AI can assist in detecting pneumonia, lung nodules, tuberculosis, pleural effusion, pneumothorax, pulmonary edema, rib fractures, and enlarged hearts.
Musculoskeletal X-rays benefit from AI models capable of identifying fractures, joint dislocations, arthritis, and bone lesions.
Dental X-rays can be analyzed to detect cavities, periodontal disease, impacted teeth, and other oral health conditions. As researchers continue to develop more advanced algorithms, the range of detectable pathologies continues to expand.
One of the most significant advantages of AI-assisted X-ray interpretation is increased efficiency. Hospitals and imaging centers often experience high patient volumes, leading to delays in image interpretation. Automated detection systems can rapidly analyze images within seconds, highlighting suspicious regions that require closer examination by radiologists.
In emergency departments, where timely diagnosis can significantly influence patient outcomes, AI can support faster clinical decision-making.
While AI is not intended to replace radiologists, it serves as an effective second reader that helps reduce diagnostic errors. Human interpretation can be influenced by fatigue, distractions, or heavy workloads, increasing the possibility of overlooked abnormalities. AI systems maintain consistent performance regardless of workload and can identify subtle findings that may otherwise be missed.
Developing reliable AI systems requires high-quality data and rigorous validation. Training datasets should include diverse patient populations, imaging equipment, and disease presentations to ensure that algorithms perform accurately across different clinical settings. Researchers should carefully label images using expert radiologist interpretations, allowing the models to learn from reliable examples.
Despite impressive advancements, several challenges remain. AI models may produce false positives, identifying abnormalities that are not actually present, or false negatives, missing genuine pathologies. These errors highlight the importance of maintaining human oversight throughout the diagnostic process.
Ethical considerations are equally important when implementing AI for X-ray analysis. Protecting patient privacy is essential because AI development depends on large collections of medical images and associated clinical data. Health care organizations must comply with strict data security regulations and anonymize patient information whenever possible.
The future of AI-assisted pathology detection is highly promising. Advances in deep learning, multimodal AI, and cloud-based computing continue to improve the capabilities of automated diagnostic systems. As technology continues to mature, AI-powered automated X-ray analysis will play an increasingly important role in delivering faster, more accurate, and more accessible health care for patients around the world.
About Damon Deteso
Dr. Damon Deteso is a diagnostic radiologist who has practiced with Millennium Medical Imaging in Saratoga Springs, New York, since 2004, working across five area hospitals. He spent three years as a medical advisor with Imagen Technologies, focusing on artificial intelligence interpretation of X-ray images. Dr. Deteso earned his medical degree from the University of Massachusetts Medical School and completed his cross-sectional imaging training at the University of California, San Francisco. He also holds a physics degree from Holy Cross University.