ReviewFrontiers in psychology2021
Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging.
Review in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses 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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare.F1000Research · 2021Pooled it
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- Perceptions of artificial intelligence for radiographic image accuracy: a cross-sectional survey of radiology personnel.Frontiers in medicine · 2026Article
- A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.Journal of evaluation in clinical practice · 2025Review
- CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.European radiology · 2025Observational
- Predicting the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment: an interpretable magnetic resonance imaging radiomics model.Quantitative imaging in medicine and surgery · 2025Article
- A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.Current oncology (Toronto, Ont.) · 2025Article
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus.Scientific reports · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Evaluation of Health Science Students' Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery.Journal of multidisciplinary healthcare · 2025Article
- Impact of an expert-derived, quick hands-on tool on classifying pulmonary hypertension in chest computed tomography: a study on inexperienced readers using RAPID-CT-PH.La Radiologia medica · 2024Observational
- AI in Radiology: Navigating Medical Responsibility.Diagnostics (Basel, Switzerland) · 2024Review
- Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context.Asian bioethics review · 2024Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology.International journal of general medicine · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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.