ArticleLa Radiologia medica2020
Artificial intelligence: Who is responsible for the diagnosis?
Article in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 4 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
109 citing papers in PubMed, 4 syntheses or guidelines pooled it, 229 citations in OpenAlex.
- Physicians' required competencies in AI-assisted clinical settings: a systematic review.British medical bulletin · 2025Pooled it
- Clinical impact of AI in radiology department management: a systematic review.La Radiologia medica · 2024Pooled it
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Ethics of AI in Radiology: A Review of Ethical and Societal Implications.Frontiers in big data · 2022Pooled it
- Stakeholders' perspectives on an autonomous brain MRI: An interview study.PEC innovation · 2026Article
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Robot-assisted Computer-Implant surgery : A comprehensive review of system architecture, clinical accuracy, and emerging technologies.Journal of robotic surgery · 2026Review
- Systematic detection of predictive gene sets by semantics-based selection.BMC bioinformatics · 2026Article
- Artificial Intelligence in Non-Surgical Cosmetic Procedures: A Multi-Stakeholder Revolution.Aesthetic plastic surgery · 2026Review
- Artificial intelligence and photon-counting CT: the three phases of the software-hardware revolution in radiology.La Radiologia medica · 2026Article
- The Role of Artificial Intelligence in Shaping the Doctor-Patient Relationship: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Optimistic and ambivalent attitudes toward clinical artificial intelligence.Turkish journal of medical sciences · 2026Article
- Medical clinical minds meet artificial intelligence: Italian physicians' knowledge, attitudes, and concordance between Italian physicians and AI-generated diagnoses. A national cross-sectional study.Frontiers in digital health · 2026Article
- Evaluation of health professionals' perceptions on the use of artificial intelligence in radiology: a questionnaire-based study.BMC medical education · 2025Article
- A deep learning-based approach to enhance accuracy and feasibility of long-term high-resolution manometry examinations.Communications medicine · 2025Article
- How public involvement can improve the science of AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Artificial intelligence in medical imaging diagnosis: are we ready for its clinical implementation?Journal of medical imaging (Bellingham, Wash.) · 2025Article
- The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early-Stage (T1-T2/N0) Oral Cavity Carcinomas.Head & neck · 2025Article
- Legal Implication in Utilizing Automated Robots: A Written Informed Consent Form Proposal.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025Review
49 more citing papers are in PubMed but not listed here.
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 at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the paper is to find an answer to the question "Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?" When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional's responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional's ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots.
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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.