ReviewEuropean radiology2023
Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).
Review in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.European journal of radiology · 2026Article
- Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.European radiology · 2026Article
- Impact of AI-Triaged Worklists and AI-Assisted Report Generation on Radiology Turnaround Times: Prospective Real-World Study.Journal of medical Internet research · 2026Article
- Progressive pulmonary fibrosis.European radiology · 2026Review
- Perceptions of artificial intelligence among pulmonologists.ERJ open research · 2026Article
- Deformable lung models for anatomical lung resections: The introduction of simulated reality for imaging guidance.JTCVS techniques · 2026Article
- Artificial Intelligence for Detecting Aortic Arch Calcification on Chest Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Emerging technologies in thoracic surgery: artificial intelligence, robotic-assisted surgery, and telesurgery.Frontiers in surgery · 2026Review
- Comparison of the efficacy of spiral CT combined with advanced multiplanar reconstruction in detecting the progression of pneumoconiosis.Frontiers in medicine · 2026Article
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions.Scientific reports · 2025Article
- Choosing the right artificial intelligence solutions for your radiology department: key factors to considerDiagnostic and interventional radiology (Ankara, Turkey) · 2024Review
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.IEEE journal of biomedical and health informatics · 2024Article
- Innovations in Chest Imaging: How Can Patients Benefit?Diagnostics (Basel, Switzerland) · 2024Article
- A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.Respiratory research · 2023Article
- Deep learning for diagnosis of malign pleural effusion on computed tomography images.Clinics (Sao Paulo, Brazil) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.
Indexed as
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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.