Medical Imaging Based Disease Diagnosis using AI is about how AI technologies have found their way into medical imaging methods and are utilized for disease diagnosis. Human experts may find it challenging to analyze extensive data from traditional medical imaging techniques such as X-rays, CT scans, and MRIs. Machine learning algorithms closely linked to AI help interpret complex datasets by detecting disease patterns, anomalies, and subtle features. The above approach will tremendously improve the diagnosis process by making it faster and more accurate. AI algorithms can quickly analyze a large number of medical images, hence helping radiologists and clinicians make early disease diagnoses, predict patient outcomes, and customize treatment plans. In addition to making the diagnostic process shorter, adopting AI in medical imaging also improves strategies for treating patients with high accuracy. Analyzing medical images is integral to many AI techniques, such as deep learning and computer vision. An AI system that employs deep learning algorithms for analyzing these images can learn autonomously from large data sets, and in so doing, its accuracy in detecting patterns related to various diseases continues to increase. This adaptability makes them effective tools for identifying anomalies in medical images. However, other challenges yet to be addressed include extensive labeled datasets, interpretability of AI-generated diagnoses, and regulatory considerations necessary for the responsible use of AI in medical imaging. As the field progresses through research and development endeavors, the interaction between AI capabilities and medical knowledge could lead to crucial avenues for disease diagnosis that would benefit patients.

The peer-reviewed articles will showcase potentially high-impact research topics or directions. The objective of this issue is to identify topics that are likely to result in a noteworthy impact on the engineering industry in the next 25 to 50 years.

Topics of interest include, but are not limited to: