Evidence map›Paper›PMID 38464902›Full record

ReviewJournal of pain research2024

Reviewing the Potential Role of Artificial Intelligence in Delivering Personalized and Interactive Pain Medicine Education for Chronic Pain Patients.

Christopher L Robinson, Ryan S D'Souza, Cyrus Yazdi, Efemena M Diejomaoh, Michael E Schatman, Trent Emerick, Vwaire Orhurhu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pain research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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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

7 authors.

Christopher L Robinson *Department of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Ryan S D'Souza *Department of Anesthesiology and Perioperative Medicine, Mayo Clinic Rochester, Rochester, Minnesota, USA.ORCID 0000-0002-4601-9837
Cyrus YazdiDepartment of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Efemena M DiejomaohDepartment of Psychiatry & Behavioral Science, Meharry Medical College, Nashville, TN, USA.
Michael E Schatman *Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA.ORCID 0000-0002-1019-0413
Trent Emerick *Department of Anesthesiology and Perioperative Medicine, Chronic Pain Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.ORCID 0000-0002-3802-5946
Vwaire Orhurhu *University of Pittsburgh Medical Center, Susquehanna, Williamsport, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) in patient pain medicine education has the potential to revolutionize pain management. By harnessing the power of AI, patient education becomes more personalized, interactive, and supportive, empowering patients to understand their pain, make informed decisions, and actively participate in their pain management journey. AI tailors the educational content to individual patients' needs, providing personalized recommendations. It introduces interactive elements through chatbots and virtual assistants, enhancing engagement and motivation. AI-powered platforms improve accessibility by providing easy access to educational resources and adapting content to diverse patient populations. Future AI applications in pain management include explaining pain mechanisms, treatment options, predicting outcomes based on individualized patient-specific factors, and supporting monitoring and adherence. Though the literature on AI in pain medicine and its applications are scarce yet growing, we propose avenues where AI may be applied and review the potential applications of AI in pain management education. Additionally, we address ethical considerations, patient empowerment, and accessibility barriers.

Indexed as

chatbotsethicsmachine learningnatural language processingpatient education

Identifiers

PMID38464902
PMCPMC10924768

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.