ArticleNature communications2024
A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
A Multicenter Randomized Controlled Trial of AI-Assisted Reading Compared With Standard Radiology Interpretation for Malignant Hepatic Lesions on Contrast-Enhanced CT: Diagnostic, Clinical and Workflow Outcomes
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice.Japanese journal of radiology · 2026Pooled it
- Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.Nature medicine · 2026Article
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Performance Comparison Between Two Versions of a Commercial Artificial Intelligence System for Chest Radiograph Interpretation: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based segmentation and detection of focal liver lesions on multi-sequence non-contrast MRI.Abdominal radiology (New York) · 2026Article
- Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- Federated orthogonal learning for detection of liver lesions from multi-phase contrast-enhanced CT images.NPJ digital medicine · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI.European radiology · 2026Article
- Evaluation of the performance of radiologists assisted by AI in detecting colorectal liver metastases on contrast-enhanced CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Deep learning in differentiating the colorectal cancer combined with hepatic enhancing nodules: liver metastases vs hemangiomas.Insights into imaging · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- A pretreatment multiphasic CT-based decision-support model for differentiating pediatric hepatoblastoma from focal nodular hyperplasia.Frontiers in oncology · 2026Article
- Model-Driven Deep Learning Enables Speckle-Free Holography for 3D Parallel Nanofabrication.Research (Washington, D.C.) · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- ChatGPT improves usability, effectiveness, scalability, interpretability and accessibility, in early diagnosis of metabolic dysfunction-associated fatty liver disease.BMC gastroenterology · 2025Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- A Multi-phase CT Dataset for Automated Differential Diagnosis of Liver Tumors.Scientific data · 2025Article
- A foundation model for rock thin-section images analysis.Communications engineering · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
42 authors at 20 institutions in 4 countries.
Funding
Abstract
Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.