Medical imaging technology has advanced tremendously over the past few decades, leading to earlier and more reliable disease detection, diagnosis, and treatment planning. An exciting new development that promises to further revolutionize medical imaging is the integration of artificial intelligence (AI).
AI and its subset technology, deep learning, are already demonstrating enormous potential to transform radiology. As the technology rapidly evolves, AI is projected to become an integral assistant – and possibly even a partner – to radiologists in the coming years.
Enhancing Imaging Quality
AI algorithms can process medical images to reduce noise, artifacts, and other factors that can obscure findings. For example, AI systems can filter out ribs and clavicles that appear on a chest X-ray, providing a clearer view of the lungs. This level of image enhancement enables radiologists to more accurately interpret the images.
Supporting Quantitative Image Analysis
AI can extract quantitative data from medical images through tissue segmentation and pattern analysis. This allows for a very precise measurement of changes occurring over time, like tumour growth. By supporting more objective quantitative analysis, AI may reduce variability in image interpretation between different radiologists.
Aiding in Detection
Deep learning algorithms can be trained to highlight suspicious regions in medical images and draw radiologists’ attention to possible abnormalities. Numerous studies have found AI systems can match or even exceed radiologist performance for the detection of diseases like breast cancer, lung cancer, and diabetic retinopathy. AI detection assists radiologists in making timely diagnoses.
Triaging Medical Images
AI systems can analyze incoming medical images and categorize them based on urgency. Critical exams with apparent abnormalities are flagged for prioritized review. This application enables radiologists to optimize their worklists based on need. AI triaging will be key for managing the growing volume of imaging exams.
Generating Preliminary Reports
Natural language processing allows AI algorithms to extract findings from medical images and produce draft radiology reports. While human oversight is still required, this automation can accelerate the documentation process considerably. Systems can highlight the most critical findings for radiologists to include in final reports.
Improving Consistency
Medical imaging is susceptible to inter-reader variability, meaning different radiologists may interpret the same image differently. AI models can be trained to minimize variation and standardize disease detection. This promotes more accurate diagnoses and a consistent quality of care across patients and providers.
Predicting Patient Outcomes
Looking ahead, AI imaging analytics combined with clinical data may provide the capability to forecast the progression of diseases and response to different therapies. This would enable truly personalized medicine tailored to an individual’s predicted outcome.
While AI in radiology is still evolving, its transformative impact is already apparent and will grow substantially in the years ahead. But it is the symbiotic integration of radiologists and AI – combining human expertise and AI capabilities – that will ultimately allow this technology to improve patient care. Medical imaging AI has a bright but undoubtedly collaborative future supporting radiology practice.
Author
Dr. Maajid Mohi Ud Din Malik
Assistant Professor
Dr. D.Y. Patil School of Allied Health Sciences