Artificial intelligence and deep learning – Radiology's next frontier?

Artificial intelligence in radiology: the ecosystem essential to improving patient care

“…In addition to detecting abnormal imaging findings, AI algorithms can be used in the quick identification of negative studies. The concept of the "quick negative" study would be especially beneficial in disease screening programs or underserved countries without access to local medical expertise [27].…”

Missed Lung Cancers on Chest Radiograph: An Illustrative Review of Common Blind Spots on Chest Radiograph with Emphasis on Various Radiologic Presentations of Lung Cancers

“…Missed lung cancer on initial CXR could delay diagnosis and affect the patient's prognosis. the lesion detection and also the diagnosis (35,36). In this era, radiologists should be better positioned with extensive knowledge and expertise in image interpretation.…”

Simulation and Synthesis in Medical Imaging

“…As machine learning, artificial intelligence, computational medicine, etc. turn into buzzwords even among clinicians and market analysts [item 19) in the Appendix], [item 20) in the Appendix], and the threshold to access and (mis)use these technologies lowers, they become commodities [item 21) in the Appendix] [item 22) in the Appendix] with the potential risk of confusing reality with fiction. Well-designed community challenges 5 for performance assessment and cross-algorithmic benchmarking should keep us grounded in reality and grow their importance.…”