ArticleJournal of the American College of Radiology : JACR2025
Artificial Intelligence in Radiology: A Leadership Survey.
Article in Journal of the American College of Radiology : JACR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- Artificial intelligence and chatbots in general surgery: a survey among surgeons in Germany, Austria and Switzerland.Surgical endoscopy · 2026Article
- Radiologist burnout: AI's true black box.European radiology · 2026Review
- Selecting self-and-follower goal-aware leadership styles across sectors: a decision support approach.Frontiers in psychology · 2026Article
- The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025Article
- Article
- The application and challenges of ChatGPT in laboratory medicine.Advances in laboratory medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeSurveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments. MATERIALS AND
methodsWe conducted a web survey of Society of Chairs of Academic Radiology Departments members between October 5 and October 31, 2023, to solicit optimism or pessimism about AI, target use cases, planned implementation, and perceptions of their workforce. P values are provided only for descriptive purposes and have not been adjusted for multiple testing in this exploratory research.
resultsThe survey was sent to the 112 Society of Chairs of Academic Radiology Departments members and 43 responded (38%). Chairs were optimistic, with no statistical difference between views of AI in general versus generative AI. Chairs plan to implement AI to improve quality and efficiency (43 of 43, 100%), burnout (41 of 43, 95%), health care costs (22 of 43, 51%), and equity (27 of 43, 63%) and most likely will target the postprocessing (26 of 43, 60%), interpretation workflow (26 of 43, 60%), and image acquisition (18 of 43, 42%) steps in the imaging value chain. Chairs perceived that radiologists (36 of 43, 84%) and technologists (38 of 43, 88%) were not particularly worried about being displaced but saw trainees as slightly less confident (31 of 43, 72%). Free text responses revealed concerns about the cost of AI and emphasized trade-offs that needed to be balanced.
conclusionRadiology chairs are optimistic about AI and poised to tackle departmental challenges. Concerns about generative AI and workforce replacement are minimal.
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.