ReviewYearbook of medical informatics2024
Safety and Precision AI for a Modern Digital Health System.
Review in Yearbook of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) promises to revolutionize healthcare. Currently there is a proliferation of new AI applications that are being developed and beginning to be deployed across many areas in healthcare to streamline and make healthcare processes more efficient. In addition, AI has the potential to support personalized and customized precision healthcare by providing intelligent interaction with end users. However, to achieve the goal of precision AI issues and concerns related to the safety of AI, as with any new technology, must be addressed. In this article we first describe the link between AI and safety and then describe the relation of AI to the emerging study of technology-induced error. An overview of published safety issues that have been associated with introduction of AI are described and categorized. These include potential for error to arise from varied sources, including the data used to drive AI applications, and the design process of AI applications itself. In addition, lack of appropriate and rigorous testing and limited analysis of AI applications during procurement processes has also been reported. Recommendations for ensuring the safe adoption of AI technology in healthcare are discussed, focusing on the need for more rigorous testing and evaluation of AI applications, ranging from laboratory testing through to naturalistic evaluation. The application of such approaches will support safety and precision AI for a modern digital health system.
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.