Next-Generation Diagnostic Radiology: Deep Learning, Explainable Artificial Intelligence, Radiomics, and Multimodal Imaging for Improved Clinical Decision-Making

Abstract

Traditional radiological workflows face mounting challenges, including escalating diagnostic volumes, intra- and inter-observer variability, and the complexity of extracting clinically actionable insights from high-dimensional imaging data.  Explainable Artificial Intelligence (XAI), Multimodal Imaging and Radiomics are driving the evolution of diagnostic radiology. These technologies present novel possibilities to enhance the interpretation of medical images and the assistance for clinical decision making. Deep learning can help detect, classify, segment and predict abnormalities in X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET). XAI techniques can increase transparency by highlighting the areas or patterns in the images that are important for the AI to make its predictions. This could help healthcare professionals assess and apply the AI's results accurately. The next step in image analysis is radiomics, which transforms medical images into quantitative features for diagnosing, predicting or monitoring treatment efficacy and response. Recently, multimodal AI has opened up the possibility to combine information from various imaging modalities, which also includes information from radiomics, clinical, lab and genomic data, paving the way to more personalized patient evaluation. Despite these advances, key questions on the challenges remain such as small and not standardized datasets, algorithmic bias, poor reproducibility, lack of external validation, interoperability, privacy concerns, and uncertainty about clinical implementation. Standardized datasets, multicenter validation, clinically relevant explainability, ethical AI development, and prospective evaluation should thus be future focal points of ongoing research. In summary, the adoption of AI-powered.imaging technologies can enhance diagnostic precision, streamline workflows, and drive personalized patient care, all while preserving radiologists' crucial role in clinical decision-making.