Evidence map›Paper›PMID 39729119›Full record

ArticleWorld journal of urology2024

Physician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians.

Eric J Robinson, Chunyuan Qiu, Stuart Sands, Mohammad Khan, Shivang Vora, Kenichiro Oshima, Khang Nguyen, L Andrew DiFronzo, David Rhew, Mark I Feng

Abstract readComparative Study
In one paragraph

Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Comparative analysis of artificial intelligence platforms in generating Post-Operative instructions for endoscopic transnasal skull base surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  6. What patients want from healthcare chatbots: insights from a mixed-methods study.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  7. Article
  8. [AI-enabled clinical decision support systems: challenges and opportunities].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
    Review
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Eric J RobinsonDepartment of Urology, Los Angeles Medical Center, Kaiser Permanente, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-6334-3540
Chunyuan QiuDepartment of Anesthesiology, Baldwin Park Medical Center, Kaiser Permanente, Baldwin Park, CA, USA.
Stuart SandsKaiser Permanente, Pleasanton, CA, USA.
Mohammad KhanMicrosoft Health & Life Sciences, Irvine, CA, USA.
Shivang VoraMicrosoft Health & Life Sciences, Dallas, TX, USA.
Kenichiro OshimaKaiser Permanente, Oakland, CA, USA.
Khang NguyenDepartment of Family Medicine, Kaiser Permanente, Pasadena, CA, USA.
L Andrew DiFronzoKaiser Permanente, Pasadena, CA, USA.
David RhewMicrosoft Health & Life Sciences, Redmond, WA, USA.
Mark I FengDepartment of Urology, Baldwin Park Medical Center, Kaiser Permanente, 1011 Baldwin Park Blvd., Baldwin Park, CA, 91706, USA. mark.i.feng@kp.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the accuracy, comprehensiveness, empathetic tone, and patient preference for AI and urologist responses to patient messages concerning common BPH questions across phases of care.

methodsCross-sectional study evaluating responses to 20 BPH-related questions generated by 2 AI chatbots and 4 urologists in a simulated clinical messaging environment without direct patient interaction. Accuracy, completeness, and empathetic tone of responses assessed by experts using Likert scales, and preferences and perceptions of authorship (chatbot vs. human) rated by non-medical evaluators.

resultsFive non-medical volunteers independently evaluated, ranked, and inferred the source for 120 responses (n = 600 total). For volunteer evaluations, the mean (SD) score of chatbots, 3.0 (1.4) (moderately empathetic) was significantly higher than urologists, 2.1 (1.1) (slightly empathetic) (p < 0.001); mean (SD) and preference ranking for chatbots, 2.6 (1.6), was significantly higher than urologist ranking, 3.9 (1.6) (p < 0.001). Two subject matter experts (SMEs) independently evaluated 120 responses each (answers to 20 questions from 4 urologist and 2 chatbots, n = 240 total). For SME evaluations, mean (SD) accuracy score for chatbots was 4.5 (1.1) (nearly all correct) and not significantly different than urologists, 4.6 (1.2). The mean (SD) completeness score for chatbots was 2.4 (0.8) (comprehensive), significantly higher than urologists, 1.6 (0.6) (adequate) (p < 0.001).

conclusionAnswers to patient BPH messages generated by chatbots were evaluated by experts as equally accurate and more complete than urologist answers. Non-medical volunteers preferred chatbot-generated messages and considered them more empathetic compared to answers generated by urologists.

Indexed as

Patient PreferenceUrologyAdultArtificial IntelligenceCross-Sectional StudiesFemaleHumansMaleMiddle AgedPhysician-Patient RelationsProstatic HyperplasiaUrologistsArtificial intelligence (AI)Benign prostatic hyperplasia (BPH)Care experienceChatbotChatGPTLarge language models (LLMs)Patient communicationPatient messagesPhysician experienceSandbox

Identifiers

PMID39729119
PMCPMC11680670

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.