ReviewJapanese journal of radiology2019
The day when computers read between lines.
Review in Japanese journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam.Heliyon · 2023Article
- Analysis of "visible in retrospect" to monitor false-negative findings in radiological reports.Japanese journal of radiology · 2023Article
- Computed tomography angiography lightbulb sign: Characteristic enhancement pattern on neck computed tomography angiography in differentiating paraganglioma from schwannoma of the carotid space.The neuroradiology journal · 2020Article
- Celebrating the beginning of international journal collaboration.Japanese journal of radiology · 2020Article
- 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing notion that artificial general intelligence (AGI) will replace some of the work done by trained professionals, including physicians. This idea, however, seems to have logical leap; herein, we discuss three problems that are significant barriers to this. First, the ground truth is difficult to provide in the majority of medical conditions. Second, the electronic medical record (EMR) only covers a portion of the information that is crucial for patient care. This makes the data in the EMR a suboptimum material for creation of AGI. Third, there are decision-making processes that cannot be captured in a way that computers can digest; portions of our thoughts, perceptions, intuitions, and inspirations cannot be translated into numbers or words.
Indexed as
Identifiers
30911986What 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.