ArticleFrontiers in neurology2017
The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading.
Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Medical students' knowledge, attitudes, and practices toward generative artificial intelligence in Egypt 2024: a Cross-Sectional study.BMC medical education · 2025Article
- The Promise of AI for Image-Driven Medicine: Qualitative Interview Study of Radiologists' and Pathologists' Perspectives.JMIR human factors · 2024Article
- Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.Neuroradiology · 2024Article
- Determining medical students' anxiety and readiness levels about artificial intelligence.Heliyon · 2024Article
- Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam.Heliyon · 2023Article
- Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students.BMC medical education · 2023Article
- OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data.Brain sciences · 2023Article
- Integration of Artificial Intelligence Into Sociotechnical Work Systems-Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review.JMIR research protocols · 2022Article
- Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.European radiology · 2022Article
- A review of the application of machine learning in molecular imaging.Annals of translational medicine · 2021Review
- Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study.BMC medical education · 2021Article
- Editorial: Machine Learning in Neuroimaging.Frontiers in neurology · 2021Article
- Standardization of imaging methods for machine learning in neuro-oncology.Neuro-oncology advances · 2020Article
- Neurological update: neuroimaging in dementia.Journal of neurology · 2020Review
- Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.European journal of hybrid imaging · 2020Review
- Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning.Radiology. Artificial intelligence · 2020Article
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.Nature reviews. Neurology · 2020Review
- Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?European journal of nuclear medicine and molecular imaging · 2019Review
- Artificial intelligence for precision education in radiology.The British journal of radiology · 2019Review
- Machine Learning in Orthopedics: A Literature Review.Frontiers in bioengineering and biotechnology · 2018Review
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
Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.
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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.