Evidence mapPaperPMID 37169486Full record

ArticleRegional anesthesia and pain medicine2023

Daring discourse: artificial intelligence in pain medicine, opportunities and challenges.

Meredith C B Adams, Ariana M Nelson, Samer Narouze

Abstract read
In one paragraph

Article in Regional anesthesia and pain medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee.Pain practice : the official journal of World Institute of Pain · 2025
    Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
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.

Meredith C B AdamsDepartments of Anesthesiology, Biomedical Informatics, Physiology & Pharmacology, and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.ORCID 0000-0002-3969-4279
Ariana M NelsonDepartment of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California, USA arianamn@hs.uci.edu.ORCID 0000-0003-1575-1635
Samer NarouzeWestern Reserve Hospital, Cuyahoga Falls, Ohio, USA.

Funding

Wake Forest IMPOWR Dissemination Education and Coordination Center (IDEA-CC)R24DA055306 · NIDA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ADAMS, MEREDITH C. B. · 2021 to 2023
$3.3M
Identifying opioid response phenotypes in low back pain electronic health dataK08EB022631 · NIBIB · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ADAMS, MEREDITH C. B. · 2017 to 2020
$737k
NIBIB NIH HHS K08 EB022631NIDA NIH HHS R24 DA055306
6 · The paper itself

Abstract

Artificial intelligence (AI) tools are currently expanding their influence within healthcare. For pain clinics, unfettered introduction of AI may cause concern in both patients and healthcare teams. Much of the concern stems from the lack of community standards and understanding of how the tools and algorithms function. Data literacy and understanding can be challenging even for experienced healthcare providers as these topics are not incorporated into standard clinical education pathways. Another reasonable concern involves the potential for encoding bias in healthcare screening and treatment using faulty algorithms. And yet, the massive volume of data generated by healthcare encounters is increasingly challenging for healthcare teams to navigate and will require an intervention to make the medical record manageable in the future. AI approaches that lighten the workload and support clinical decision-making may provide a solution to the ever-increasing menial tasks involved in clinical care. The potential for pain providers to have higher-quality connections with their patients and manage multiple complex data sources might balance the understandable concerns around data quality and decision-making that accompany introduction of AI. As a specialty, pain medicine will need to establish thoughtful and intentionally integrated AI tools to help clinicians navigate the changing landscape of patient care.

Indexed as

AlgorithmsArtificial IntelligenceClinical Decision-MakingDelivery of Health CareHumansPainCHRONIC PAINDiagnostic Techniques and ProceduresEconomicsTECHNOLOGYTreatment Outcome

Identifiers

PMID37169486
PMCPMC10525018

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.