Evidence map›Paper›PMID 40579558›Full record

ArticleEuropean radiology2026

Leadership in radiology in the era of technological advancements and artificial intelligence.

Barbara D Wichtmann, Daniel Paech, Oleg S Pianykh, Susie Y Huang, Steven E Seltzer, James Brink, Fiona M Fennessy

Abstract read
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Barbara D WichtmannDepartment of Radiology, Mass General Brigham, Boston, MA, USA. Barbara.Wichtmann@ukbonn.de.ORCID http://orcid.org/0000-0001-8020-0202
Daniel PaechDepartment of Radiology, Mass General Brigham, Boston, MA, USA.
Oleg S PianykhDepartment of Radiology, Mass General Brigham, Boston, MA, USA.
Susie Y HuangDepartment of Radiology, Mass General Brigham, Boston, MA, USA.
Steven E SeltzerDepartment of Radiology, Mass General Brigham, Boston, MA, USA.
James BrinkDepartment of Radiology, Mass General Brigham, Boston, MA, USA.
Fiona M FennessyDepartment of Radiology, Mass General Brigham, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiology has evolved from the pioneering days of X-ray imaging to a field rich in advanced technologies on the cusp of a transformative future driven by artificial intelligence (AI). As imaging workloads grow in volume and complexity, and economic as well as environmental pressures intensify, visionary leadership is needed to navigate the unprecedented challenges and opportunities ahead. Leveraging its strengths in automation, accuracy and objectivity, AI will profoundly impact all aspects of radiology practice-from workflow management, to imaging, diagnostics, reporting and data-driven analytics-freeing radiologists to focus on value-driven tasks that improve patient care. However, successful AI integration requires strong leadership and robust governance structures to oversee algorithm evaluation, deployment, and ongoing maintenance, steering the transition from static to continuous learning systems. The vision of a "diagnostic cockpit" that integrates multidimensional data for quantitative precision diagnoses depends on visionary leadership that fosters innovation and interdisciplinary collaboration. Through administrative automation, precision medicine, and predictive analytics, AI can enhance operational efficiency, reduce administrative burden, and optimize resource allocation, leading to substantial cost reductions. Leaders need to understand not only the technical aspects but also the complex human, administrative, and organizational challenges of AI's implementation. Establishing sound governance and organizational frameworks will be essential to ensure ethical compliance and appropriate oversight of AI algorithms. As radiology advances toward this AI-driven future, leaders must cultivate an environment where technology enhances rather than replaces human skills, upholding an unwavering commitment to human-centered care. Their vision will define radiology's pioneering role in AI-enabled healthcare transformation. KEY POINTS: Question Artificial intelligence (AI) will transform radiology, improving workflow efficiency, reducing administrative burden, and optimizing resource allocation to meet imaging workloads' increasing complexity and volume. Findings Strong leadership and governance ensure ethical deployment of AI, steering the transition from static to continuous learning systems while fostering interdisciplinary innovation and collaboration. Clinical relevance Visionary leaders must harness AI to enhance, rather than replace, the role of professionals in radiology, advancing human-centered care while pioneering healthcare transformation.

Indexed as

Artificial IntelligenceLeadershipRadiologyHumansArtificial IntelligenceGovernanceLeadershipRadiologyWorkflow

Identifiers

PMID40579558
PMCPMC12711943

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.