ReviewMedical oncology (Northwood, London, England)2020
Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists.
Review in Medical oncology (Northwood, London, England), 2020. 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
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in preoperative planning for Total Hip Arthroplasty: a systematic review.Frontiers in artificial intelligence · 2024Pooled it
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- The accuracy of artificial intelligence in 3D preoperative planning for total hip arthroplasty: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2026Review
- Legal Implication in Utilizing Automated Robots: A Written Informed Consent Form Proposal.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025Review
- Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.Journal of medical systems · 2024Article
- Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology.Diagnostics (Basel, Switzerland) · 2023Review
- AI in breast screening mammography: breast screening readers' perspectives.Insights into imaging · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- [Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future?]Der Urologe. Ausg. A · 2022Review
- Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.Exploration of targeted anti-tumor therapy · 2022Review
- Integrating artificial intelligence into radiology practice: undergraduate students' perspective.European journal of nuclear medicine and molecular imaging · 2021Article
- Challenges of Implementing Artificial Intelligence in Interventional Radiology.Seminars in interventional radiology · 2021Review
- In search of a Goldilocks zone for credible AI.Scientific reports · 2021Article
- Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.La Radiologia medica · 2021Review
- Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study.Journal of clinical medicine · 2021Article
- Challenges and opportunities of digital health in a post-COVID19 world.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2021Review
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. Despite the overwhelmingly positive uptake of the technology, warnings have been published about the potential dangers of AI. Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.
Indexed as
Identifiers
32246300What 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.