Evidence map›Paper›PMID 39199522›Full record

ArticleBrain sciences2024

Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective.

Thorsten Rudroff, Oona Rainio, Riku Klén

Abstract read
In one paragraph

Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

3 authors.

Thorsten RudroffTurku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland.ORCID 0000-0002-2057-7793
Oona RainioTurku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland.
Riku KlénTurku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland.ORCID 0000-0002-0982-8360

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long COVID (Coronavirus disease), affecting millions globally, presents unprecedented challenges to healthcare systems due to its complex, multifaceted nature and the lack of effective treatments. This perspective review explores the potential of artificial intelligence (AI)-guided transcranial direct current stimulation (tDCS) as an innovative approach to address the urgent need for effective Long COVID management. The authors examine how AI could optimize tDCS protocols, enhance clinical trial design, and facilitate personalized treatment for the heterogeneous manifestations of Long COVID. Key areas discussed include AI-driven personalization of tDCS parameters based on individual patient characteristics and real-time symptom fluctuations, the use of machine learning for patient stratification, and the development of more sensitive outcome measures in clinical trials. This perspective addresses ethical considerations surrounding data privacy, algorithmic bias, and equitable access to AI-enhanced treatments. It also explores challenges and opportunities for implementing AI-guided tDCS across diverse healthcare settings globally. Future research directions are outlined, including the need for large-scale validation studies and investigations of long-term efficacy and safety. The authors argue that while AI-guided tDCS shows promise for addressing the complex nature of Long COVID, significant technical, ethical, and practical challenges remain. They emphasize the importance of interdisciplinary collaboration, patient-centered approaches, and a commitment to global health equity in realizing the potential of this technology. This perspective article provides a roadmap for researchers, clinicians, and policymakers involved in developing and implementing AI-guided neuromodulation therapies for Long COVID and potentially other neurological and psychiatric conditions.

Indexed as

artificial intelligencebrain stimulationlong COVIDneuroimaging

Identifiers

PMID39199522
PMCPMC11353063

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.