Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Peri-implantitis is a major biological complication that compromises the longevity of dental implants, and accurate radiographic assessment is essential for early detection and intervention. Artificial intelligence (AI) has shown promising performance in dental imaging; however, the evidence supporting its diagnostic utility for peri-implant disease on two-dimensional radiographs remains fragmente...
The rapid proliferation of artificial intelligence (AI) applications in neuroradiology can lead to heterogeneous study design and reporting that impede reproducibility and clinical translation. To support authors submitting AI imaging research to AJNR, we introduce six study-type-specific reporting checklists for studies focused on: (1) Classification and Clinical Outcome Prediction, (2) Lesion De...
BACKGROUND/OBJECTIVES: Artificial intelligence (AI) is leading to a significant paradigm shift in medical imaging and diagnostic sciences. In particul...
Artificial intelligence and automated pattern recognition, in particular, have been described as the next frontier in musculoskeletal imaging. However...
BACKGROUND: Acanthamoeba keratitis (AK) is a severe, sight-threatening infectious disease with a rising global incidence, linked to the increasing use...
Musculoskeletal radiology has transitioned to "musculoskeletal imaging and intervention," encompassing both traditional diagnostic roles and specializ...
Artificial Intelligence (AI) is rapidly transforming cancer care by enabling healthcare teams to make more accurate diagnoses, predict responses to th...
BackgroundIntraoperative consultation using frozen sections has been crucial for guiding surgical decisions, but has often been limited by the time an...
Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Acade...
Traditional metrics such as precision, recall, mean Average Precision (mAP), and F-score are widely used to evaluate object detection models. However,...
Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, movi...
BACKGROUND: Timely diagnosis of impaired systolic function and left ventricular hypertrophy (LVH) remains a clinical challenge. Routine electrocardiog...
INTRODUCTION: Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL), is a rare EBV-associated malignancy characterized by destructive tumors i...
BACKGROUND: Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary ...
Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for...
OBJECTIVE: To determine whether a novel diagnostic platform which pairs high-throughput imaging cytometry with Artificial Intelligence (AI) assisted i...
RATIONALE AND OBJECTIVES: Radiation dose reduction in pediatric chest radiography is a clinical priority due to increased radiosensitivity and cumulat...
RATIONALE AND OBJECTIVES: Timely radiology access is essential for accurate diagnosis, treatment planning, and efficient care delivery. U.S. academic ...
PURPOSE: Machine learning in medical imaging (MIML) is critical to computer-aided diagnostics. However, data heterogeneity-variation in medical data a...
OBJECTIVES: To compare conventional speech recognition (CSR) and a general-purpose large language model (LLM) in radiology reports, focusing on genera...