Evidence map›Paper›PMID 41396542›Full record

ReviewCurrent pain and headache reports2025

Artificial Intelligence-Assisted Scribing for Chronic Pain Care: A Narrative Review.

Connor Yost, Oranicha Jumreornvong, Jamal J Hasoon, Qing Zhao Ruan, Samuel P Ang, Peter Palumbo, Giuliano Lo Bianco, R Jason Yong, Gabe Zetter, Paul J Christo and 5 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current pain and headache reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Connor YostDepartment of Internal Medicine, Crieghton Medical School, Phoenix, AZ, USA. connordavidyost@gmail.com.
Oranicha JumreornvongDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jamal J HasoonDepartment of Anesthesia, Critical Care, and Pain Medicine, McGovern Medical School, UTHealth, Houston, TX, USA.
Qing Zhao RuanWarren Alpert Medical School, Brown University, Providence, RI, USA.
Samuel P AngDepartment of Anesthesiology, Perioperative and Pain Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
Peter PalumboGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Giuliano Lo BiancoAnesthesiology and Pain Department, Fondazione Istituto G. Giglio Cefalù, Palermo, Italy.
R Jason YongDepartment of Anesthesiology, Perioperative and Pain Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
Gabe ZetterXaia, Los Angeles, CA, USA.
Paul J ChristoDivision of Pain Medicine, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Alan D KayeDepartment of Anesthesiology, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA.
Henry J HilgerUniversity of Notre Dame, Notre Dame, IN, USA.
Matthew ChungDepartment of Pain Medicine, Division of Anesthesia, Critical Care and Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Belinda DuszynskiPolicy and Practice, International Pain and Spine Intervention Society, Hinsdale, IL, USA.
Christopher L RobinsonDivision of Pain Medicine, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ChristopherRobinsonMDPhD@outlook.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewMedical documentation is a significant pillar of healthcare, enabling quality care, legal protection, billing and reimbursement, and collaboration between physicians. However, it is time-consuming and resource-intensive, posing a significant burden on chronic pain physicians, leading to outcomes such as increased work stress, diminished patient-physician quality time, and burnout. A range of ambient artificial intelligence (AI)-powered scribes have been developed to document based on listening to the patient-doctor interactions. These scribes utilize voice recognition and data processing capabilities to summarize and interpret patient-physician conversations, freeing physicians' time and resources and refocusing their efforts on core aspects of patient care. RECENT

findingsStudies indicate that ambient AI scribes can score significantly higher than traditional notetaking on critical metrics such as completeness, conciseness, and accuracy. Additionally, they have demonstrated the potential to reduce time spent on documentation and cognitive workload while enhancing the quality of the consultation experience. Common challenges include errors, patient autonomy and privacy risks, and broader ethical implications.

conclusionWhile still in its early stages, AI-assisted documentation presents a possible solution to persistent issues in healthcare, such as administrative overload, physician burnout, and documentation-related errors. However, realizing its full potential requires addressing concerns with accuracy, privacy, and trust. The successful implementation of AI in clinical documentation will depend on striking a balance between innovation and safeguards, ensuring that such systems support patient care without compromising ethical standards or clinical safety within pain management, including the broader field of medicine.

Indexed as

Artificial IntelligenceChronic PainDocumentationPain ManagementHumansPhysician-Patient RelationsArtificial intelligenceChronic painMedical documentationScribing

Identifiers

PMID41396542

What OpenQuestion holds

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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.