Our patients have spoken: keep radiologists in the centre of AI imaging ecosystems

Abstract

In European Radiology, Ongena and colleagues [1] have developed a standardised questionnaire to evaluate the patient perspective on the implementation of artificial intelligence in radiology. In doing so, the authors have endeavoured to address an important blind spot in AI research, namely a need to assess the impact of new technologies in their social, cultural and political milieu [2]. Using exploratory factor analysis on patient feedback, the authors identified five variables reflecting patient concerns in radiology AI-(1) trust and accountability, (2) understanding of acquisition procedure and interpretation, (3) human communication, (4) efficiency and (5) being informed of AI utilisation for their radiological diagnosis. To paraphrase the results factoring scores for each variable, patients want accurate and fast results they can understand and believe, whilst being provided opportunities when being given their results to clarify their queries and doubts, as well as receive emotional support. These are fair expectations, and whilst not completely achievable at present, should remain aspirational for AI developers and radiologists alike. Ongena et al. [1] found that patients are moderately negative when it comes to their trust in AI taking over diagnostic interpretations tasks of the radiologist with regard to accuracy, communication and confidentiality. This bodes well for AI

Radiomics and Machine Learning in Oral Healthcare

“…Advances in diagnostic imaging modalities have increased in terms of complexity and volume of generated digital data. These factors led to the creation of a new approach to imaging diagnosis called radiomics . It consists of algorithms that decompose input images into basic features that may be used to classify or interpret the image, such as edges, gradients, shape, signal intensity, wavelength, and textures.…”

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”