ReviewDiagnostics (Basel, Switzerland)2024
AI in Radiology: Navigating Medical Responsibility.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Robot-assisted Computer-Implant surgery : A comprehensive review of system architecture, clinical accuracy, and emerging technologies.Journal of robotic surgery · 2026Review
- Review
- Radiology reporting in the age of artificial intelligence: implications for patient safety.BMJ quality & safety · 2026Article
- Temporal Reproducibility of Fracture Interpretation in Forensic Radiography: A Multispecialty Comparison of Physicians and Vision Language Models Including Fracture Subtype Description.Tomography (Ann Arbor, Mich.) · 2026Article
- Dosimetry inJournal of clinical medicine · 2026Review
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Potential of Artificial Bone Scintigraphy from Early-Phase Bone Scintigraphy Using Unpaired Image-to-Image Translation in Patients with Breast Cancer: A Single-Center Prospective Study.Tomography (Ann Arbor, Mich.) · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- From scanner to scalpel: adapting IDEAL for accountable AI in radiology and image-guided neurosurgery.Frontiers in radiology · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- Beyond the algorithm: embedding ethics for trustworthy AI in radiology and oncology.Frontiers in digital health · 2026Article
- Artificial Intelligence Applications in Interventional Radiology.Journal of personalized medicine · 2025Review
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- Implementation of AI in radiology: the perspective of referring physicians.Insights into imaging · 2025Article
- Emergency radiology: roadmap for radiology departments.Japanese journal of radiology · 2025Review
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Artificial Intelligence in Radiology: A Leadership Survey.Journal of the American College of Radiology : JACR · 2025Article
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- Legal Implication in Utilizing Automated Robots: A Written Informed Consent Form Proposal.The international journal of medical robotics + computer assisted surgery : MRCAS · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.
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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.