Evidence map›Paper›PMID 40402540›Full record

ArticleEpilepsia open2025

Ambient technology in epilepsy clinical practice.

Haania Kakwan, Justin F Rousseau, Lidia M Moura, Irfan S Sheikh

Abstract read
In one paragraph

Article in Epilepsia open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Haania KakwanDepartment of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID https://orcid.org/0000-0002-3880-6507
Justin F RousseauDivision of Biostatistics and Clinical Informatics, Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Lidia M MouraPopulation Health, Neurology Department, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-1191-1315
Irfan S SheikhPeter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance and assist providers with processes ranging from scheduling patients to providing preliminary interpretations of testing results, pending orders, and documenting encounters. Epileptologists could benefit from these technologies by utilizing ambient AI models, recent applications of which offer promising solutions for automating clinical documentation. While the potential benefits of using these tools are significant and include reduced physician burnout and improved patient experience, the deployment of these technologies also raises critical concerns, such as potential biases in model training and the risk of errors being inserted into the electronic health record (EHR), among other yet to be realized unintended consequences. The accuracy of clinical documentation is essential in epilepsy care, where detailed seizure histories and accurate medication records are critical to patient safety. Another concern may be paradoxically increased physician burnout as increased expectations of providers are created. This article examines the challenges, risks, and practical considerations in applying documentation tools that utilize ambient intelligence (AmI) for outpatient epilepsy clinic encounters, highlighting key examples from clinical practice and underscoring the importance of human oversight. Although AmI models may enhance clinical documentation efficiency as measured by time to close a note and reduced rates of burnout in providers, their role in clinical environments must be carefully regulated, with further studies needed to validate this claim, provide ongoing monitoring of performance, and establish safeguards for patient safety. Collaborative efforts among clinicians, clinical informatics professionals, AI developers, and regulatory bodies are pressingly needed to ensure the safe deployment of AmI into clinical care settings. PLAIN LANGUAGE SUMMARY: Ambient artificial intelligence technology takes advantage of sensors embedded in the environment to automate tasks without the need for human input. It has the potential to streamline numerous tasks within outpatient epilepsy clinics and reduce the workload of providers as well as improve patient care. This technology has already been brought to the market as a tool for clinical documentation. The current challenges and limitations associated with its implementation require careful human oversight, which we show with examples. Further research, regulations, and ongoing monitoring are necessary to ensure ambient artificial intelligence benefits both patients and healthcare providers while minimizing risks.

Indexed as

AI scribesambient intelligenceambient scribesartificial intelligenceburnoutclinical documentationelectronic health recordlarge language models

Identifiers

PMID40402540
PMCPMC13395045

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.