ArticleWorld journal of urology2024
Artificial intelligence versus human touch: can artificial intelligence accurately generate a literature review on laser technologies?
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 1 paper.
What it found
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
purposeTo compare the accuracy of open-source Artificial Intelligence (AI) Large Language Models (LLM) against human authors to generate a systematic review (SR) on the new pulsed-Thulium:YAG (p-Tm:YAG) laser.
methodsFive manuscripts were compared. The Human-SR on p-Tm:YAG (considered to be the "ground truth") was written by independent certified endourologists with expertise in lasers, accepted in a peer-review pubmed-indexed journal (but not yet available online, and therefore not accessible to the LLMs). The query to the AI LLMs was: "write a systematic review on pulsed-Thulium:YAG laser for lithotripsy" which was submitted to four LLMs (ChatGPT3.5/Vercel/Claude/Mistral-7b). The LLM-SR were uniformed and Human-SR reformatted to fit the general output appearance, to ensure blindness. Nine participants with various levels of endourological expertise (three Clinical Nurse Specialist nurses, Urology Trainees and Consultants) objectively assessed the accuracy of the five SRs using a bespoke 10 "checkpoint" proforma. A subjective assessment was recorded using a composite score including quality (0-10), clarity (0-10) and overall manuscript rank (1-5).
resultsThe Human-SR was objectively and subjectively more accurate than LLM-SRs (96 ± 7% and 86.8 ± 8.2% respectively; p < 0.001). The LLM-SRs did not significantly differ but ChatGPT3.5 presented greater subjective and objective accuracy scores (62.4 ± 15% and 29 ± 28% respectively; p > 0.05). Quality and clarity assessments were significantly impacted by SR type but not the expertise level (p < 0.001 and > 0.05, respectively).
conclusionsLLM generated data on highly technical topics present a lower accuracy than Key Opinion Leaders. LLMs, especially ChatGPT3.5, with human supervision could improve our practice.
Indexed as
Identifiers
39466443What OpenQuestion holds
Registered trials
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.