Academic radiology
-
Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed. ⋯ This workflow resulted in three-fold improved target lesion measurement concordance with oncology records, earlier detection and faster notification of incidental actionable findings to referring clinicians, and decreased exam interpretation times for clinical radiologists. These findings demonstrate potential roles for automation (such as AI) to improve report value, worklist prioritization, and patient care.
-
Natural language is ubiquitous in the workflow of medical imaging. Radiologists create and consume free text in their daily work, some of which can be amenable to enhancements through automatic processing. ⋯ The evolution of medical NLP including vectorization, word embedding, classification, as well as its use in automated speech recognition, are also explored. Finally, the article will discuss the role of machine learning and neural networks in the context of significant, if incremental, improvements in NLP.
-
Artificial intelligence in medicine has made dramatic progress in recent years. However, much of this progress is seemingly scattered, lacking a cohesive structure for the discerning observer. In this article, we will provide an up-to-date review of artificial intelligence in medicine, with a specific focus on its application to radiology, pathology, ophthalmology, and dermatology. We will discuss a range of selected papers that illustrate the potential uses of artificial intelligence in a technologically advanced future.
-
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep learning approaches. ⋯ Regularization, ground truth, and populations are discussed along with compute and data management principles. Advanced statistical machine learning techniques including bootstrapping, bagging, boosting, decision trees, random forest, XGboost, and support vector machines are reviewed along with relevant examples from the radiology literature.
-
As artificial intelligence (AI) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and AI developers with the best interests of the patients. Radiologists know that supporting research into AI and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. ⋯ This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of AI in radiology will be partially determined by radiologist and AI researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the AI transformation to the patients.