ArticleInternational journal of impotence research2025
Comparative analysis of the effectiveness of microsoft copilot artificial intelligence chatbot and google search in answering patient inquiries about infertility: evaluating readability, understandability, and actionability.
Article in International journal of impotence research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Evaluation of the applicability of AI chatbots in addressing health management issues for living kidney donors.International urology and nephrology · 2026Article
- Investigating the Readability and Quality of AI Systems to Trending Questions About Food Poisoning.Journal of food science · 2026Article
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Failure to achieve spontaneous pregnancy within 12 months despite unprotected intercourse is called infertility. The rapid development of digital health data has led more people to search for healthcare-related topics on the Internet. Many infertile individuals and couples use the Internet as their primary source for information on infertility diagnosis and treatment. However, it is important to assess the readability, understandability, and actionability of the information provided by these sources for patients. There is a gap in the literature addressing this aspect. This study aims to compare the readability, understandability, and actionability of responses generated by Microsoft Copilot (MC), an AI chatbot, and Google Search (GS), an internet search engine, for infertility-related queries. Prospectively a Google Trends analysis was conducted to identify the top 20 queries related to infertility in February, 2024. Then these queries were entered into GS and MC in May 2024. Answers from both platforms were recorded for further analysis. Outputs were assessed using automated readability tools, and readability scores were calculated. Understandability and actionability of answers were evaluated using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) tool. GS was found to have significantly higher Automated Readability Index (ARI) and Flesch-Kincaid Grade Level (FKGL) scores than MC (p = 0.044), while no significant differences were observed in the Flesch Reading Ease, Gunning Fog Index, Simplified Measure of Gobbledygook (SMOG), and Coleman-Liau Index scores. Both GS and MC outputs had readability scores above the 8th-grade level, indicating advanced reading levels. According to PEMAT-P, MC outperformed GS in terms of understandability (68.65 ± 11.99 vs. 54.50 ± 15.09, p = 0.001) and actionability (29.85 ± 17.8 vs. 1 ± 4.47, p = 0.000). MC provides more understandable and actionable responses to infertility related queries, that it might have great potential for patient education.
Indexed as
Identifiers
What 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.