Evidence map›Paper›PMID 40511324›Full record

ArticleInterventional pain medicine2025

Assessing ChatGPT responses to patient questions on epidural steroid injections: A comparative study of general vs specific queries.

Timothy Olivier, Zilin Ma, Ankit Patel, Weibin Shi, Mohammed Murtuza, Nicole E Hatchard, Xiaoyu Norman Pan, Thiru M Annaswamy

Abstract read
In one paragraph

Article in Interventional pain medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Timothy OlivierCenter for Integrative Pain Management, West Virginia University, USA.
Zilin MaIntelligent Interactive Systems Group, Harvard School of Engineering and Applied Sciences, USA.
Ankit PatelDepartment of Physical Medicine and Rehabilitation, UT Southwestern Medical Center, USA.
Weibin ShiDepartment of Physical Medicine and Rehabilitation, Penn State Health Milton S Hershey Medical Center, Penn State College of Medicine, Hershey, PA, USA.
Mohammed MurtuzaDepartment of Physical Medicine and Rehabilitation, UT Southwestern Medical Center, USA.
Nicole E HatchardDepartment of Physical Medicine and Rehabilitation, Penn State Health Milton S Hershey Medical Center, Penn State College of Medicine, Hershey, PA, USA.
Xiaoyu Norman PanDepartment of Physical Medicine and Rehabilitation, UT Southwestern Medical Center, USA.
Thiru M AnnaswamyDepartment of Physical Medicine and Rehabilitation, Penn State Health Milton S Hershey Medical Center, Penn State College of Medicine, Hershey, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is becoming more integrated into healthcare, with large language models (LLMs) like ChatGPT being widely used by patients to answer medical questions. Given the increasing reliance on AI for health-related information, it's important to evaluate how well these models perform in addressing common patient concerns, especially in procedural medicine. To date, no studies have specifically examined AI's role in addressing patient questions related to epidural steroid injections (ESIs), making this an important area for investigation. Objective: This study examines ChatGPT's ability to answer patient questions about epidural steroid injections (ESIs), focusing on response accuracy, readability, and overall usefulness. Our aim was to evaluate and compare the content, accuracy, and user-friendliness of AI-generated information on common peri-procedural questions and complications associated with ESIs, thereby extending the application of AI as a triage tool into pain management and interventional spine procedures. Methods: We formulated and compiled 29 common patient questions about ESIs and tested ChatGPT's responses in both general and specific formats. Two interventional pain specialists reviewed the AI-generated answers, assessing them for accuracy, clarity, empathy, and directness using a Likert scale. Readability scores were calculated using Flesch-Kincaid Reading Level and Flesch Reading Ease scales. Statistical analyses were performed to compare general versus specific responses. Results: General queries led to longer, more detailed responses, but readability was similar between general and specific formats. Subjective analysis showed that general responses were rated higher for accuracy, clarity, and responsiveness. However, neither format demonstrated strong empathy, and some general queries resulted in off-topic responses, underscoring the importance of precise wording when interacting with AI. Conclusion: ChatGPT can provide clear and largely accurate answers to patient questions about ESIs, with general prompts often producing more complete responses. However, AI-generated content still has limitations, particularly in conveying empathy and avoiding tangential information. These findings highlight the need for thoughtful prompt design and further research into how AI can be integrated into clinical workflows while ensuring accuracy and patient safety.

Identifiers

PMID40511324
PMCPMC12159221

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.