Does AI actually catch more missed diagnoses than radiologists?
Yes, and the strongest evidence comes from a large 2023 study of over 3,300 patients suspected of having a pulmonary embolism (a blood clot in the lung). The AI algorithm missed 23 cases, while the attending radiologists missed 60—meaning AI would have prevented nearly three times as many missed diagnoses [1]. The AI's sensitivity (its ability to correctly identify patients who actually have the condition) was 96.8% versus 91.6% for radiologists, a statistically significant difference [1]. This study is the largest and most direct head-to-head comparison among the papers here, and it shows that AI-assisted reporting could substantially reduce the number of missed positive findings in daily practice.
Does AI work equally well for every type of scan or condition?
No, AI's accuracy depends heavily on the imaging modality and the specific abnormality. In dental radiology, one study found that AI detected periapical lesions (infections around tooth roots) with 77.8% sensitivity on cone-beam CT (CBCT) scans, but only 33.3% sensitivity on standard panoramic X-rays [3]. This means AI is much better at spotting these lesions on 3D scans than on 2D X-rays. Another dental study showed AI achieved perfect scores (100%) for detecting whether a root canal filling was present, but only moderate performance (84.1% accuracy) for judging whether the filling was adequate [2]. So while AI is a powerful tool, its reliability varies by task, and it should not be assumed to work equally well across all applications.
Is AI meant to replace radiologists, or just help them?
The evidence points strongly to AI as a support tool, not a replacement. In the pulmonary embolism study, the authors explicitly state that 'highest diagnostic accuracy can likely be achieved by radiologists supported by AI' [1]. The AI detected more cases than radiologists alone, but it also had false positives (2 vs. 9 for radiologists), meaning it flagged some normal scans as abnormal [1]. A 2023 review of AI for chest X-rays in emergency settings notes that AI can improve reporting accuracy and speed, but emphasizes that it is meant to assist radiologists, not replace their clinical judgment [5]. Similarly, a 2023 narrative review on AI in orthodontic cephalometric analysis concludes that AI facilitates landmark identification and supports less experienced clinicians, but its effectiveness varies by algorithm [6]. The consensus across these studies is that AI reduces errors when used alongside a radiologist, but it is not yet reliable enough to work entirely on its own.
About These Sources
This answer is built on 6 peer-reviewed studies — published from 2023 to 2025, 3 from 2024 or later, 5 in Q1 journals, collectively cited 176 times — selected as the most relevant from 9 studies that passed quality screening, drawn from 81 papers retrieved from a database of over 500 million.
Sources used in this answer
Retrospective batch analysis to evaluate the diagnostic accuracy of a clinically deployed AI algorithm for the detection of acute pulmonary embolism on CTPA
In a retrospective study of 3,316 patients, an AI algorithm for detecting pulmonary embolism on CT scans had significantly higher sensitivity (96.8% vs. 91.6%) and specificity (99.9% vs. 99.7%) than attending radiologists, missing 23 cases vs. 60 missed by radiologists [1].
Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images—Assessment of the Diagnostic Accuracy of AI
In a study of 55 patients using CBCT, the AI platform Diagnocat achieved perfect scores (100%) for detecting the presence of root canal fillings, but moderate accuracy (84.1%) for judging adequate obturation, with lower performance for detecting voids (F1 score 76.2%) [2].
Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy
In a study of 49 patients (1,223 teeth), AI detected periapical lesions with 77.8% sensitivity on CBCT but only 33.3% sensitivity on panoramic X-rays, though specificity was above 98% for both [3].
AI-Powered Medical Imaging: Enhancing Diagnostic Accuracy in Radiology
A hybrid deep learning model combining Vision Transformers with LSTM achieved 96.33% accuracy in classifying chest X-rays for COVID-19, pneumonia, and normal cases, outperforming ViT-GRU (95.51%) and ViT-RNN (93%) [5].
Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
A review of AI applications for chest X-rays in emergency radiology highlights that AI can improve detection of pneumothorax, pneumonia, heart failure, and pleural effusion, but is intended to assist radiologists, not replace them [8].
Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review
A narrative review of AI in cephalometric analysis found that most AI algorithms for automated landmark positioning on lateral cephalometric radiographs have relatively high accuracy, but effectiveness varies by algorithm and application type [9].