ReviewHealthcare technology letters
Systematic Review of Explainable Artificial Intelligence for Epileptic Seizure Onset Early Warning: Towards Responsible Artificial Intelligence.
Review in Healthcare technology letters. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A substantial amount of literature has been published on epileptic seizures. However, adequate evidence is still lacking to demonstrate that utilising explainable artificial intelligence for epileptic seizures can ensure an individual's safety. Furthermore, there is a need to define the fundamental challenges and opportunities present in the current state-of-the-art solutions and guide efforts towards responsible artificial intelligence. To identify fundamental challenges and opportunities in the existing state-of-the-art solutions available for explainable artificial intelligence-based epileptic seizure onset early warning: towards responsible artificial intelligence. The PRISMA checklist was utilised to develop this report. Papers were extracted from original articles and prior conference studies published in reputable databases such as PubMed, IEEE Xplore, ScienceDirect, Scopus and Google Scholar from January 2019 to 17 November 2024. Rayyan's online platform was used to identify duplicates, inclusions and exclusions of papers. This systematic review protocol was registered with the PROSPERO database. The included papers were assessed based on Microsoft's Responsible Artificial Intelligence template. The Responsible AI Impact Assessment Template, Principle 3 (transparency and explainability), determined a high-risk rating. A total of 26 studies are included based on the established inclusion and exclusion criteria. This study investigated 14.29% of responsible artificial intelligence principles applied in at least one paper with a high-risk rate. The results indicate that to transform researched solutions into practical applications, epileptic monitoring applications should be tested within the eight principles set by Microsoft. The black box explanation lacks insight into the deep internal features and operational methods, suggesting that further investigation is necessary. Systematic Review Registration ID: CRD42024544.
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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.