ArticleEuropean radiology experimental2024
The Picasso's skepticism on computer science and the dawn of generative AI: questions after the answers to keep "machines-in-the-loop".
Article in European radiology experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.European radiology · 2026Trial
- Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.European radiology · 2026Article
- Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation.Abdominal radiology (New York) · 2026Review
- Artificial intelligence as medical device in radiology in 2025: the regulatory scenario in the EU, USA, and China.European radiology · 2026Review
- How green are large language models for radiology report labelling? Comparing human, rule-based and hybrid workflows.Insights into imaging · 2026Article
- Safe for kids? AI medical devices in radiology overlook paediatric suitability.European radiology · 2026Review
- Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports.European radiology experimental · 2026Observational
- What should AI explain to you? Visions of older adults regarding explainable AI.The Gerontologist · 2026Article
- Keeping AI in medicine and radiology within the framework of scientific method: measuring to close the epistemic gap.Insights into imaging · 2025Article
- RadCLARE: an automated clinical language engine for detecting semantic errors in radiology reports.European radiology experimental · 2025Article
- The Role of Synthetic Data and Generative AI in Breast Imaging: Promise, Pitfalls, and Pathways Forward.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- A preliminary investigation into the potential, pitfalls, and limitations of large language models for mammography interpretation.Discover oncology · 2025Article
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?European radiology experimental · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology.Insights into imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a "machines-in-the-loop" approach.
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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.