Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model

Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were f...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International urology and nephrology 2024-08, Vol.56 (8), p.2589-2595
Hauptverfasser: Chiarelli, Giuseppe, Stephens, Alex, Finati, Marco, Cirulli, Giuseppe Ottone, Beatrici, Edoardo, Filipas, Dejan K., Arora, Sohrab, Tinsley, Shane, Bhandari, Mahendra, Carrieri, Giuseppe, Trinh, Quoc-Dien, Briganti, Alberto, Montorsi, Francesco, Lughezzani, Giovanni, Buffi, Nicolò, Rogers, Craig, Abdollah, Firas
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 2595
container_issue 8
container_start_page 2589
container_title International urology and nephrology
container_volume 56
creator Chiarelli, Giuseppe
Stephens, Alex
Finati, Marco
Cirulli, Giuseppe Ottone
Beatrici, Edoardo
Filipas, Dejan K.
Arora, Sohrab
Tinsley, Shane
Bhandari, Mahendra
Carrieri, Giuseppe
Trinh, Quoc-Dien
Briganti, Alberto
Montorsi, Francesco
Lughezzani, Giovanni
Buffi, Nicolò
Rogers, Craig
Abdollah, Firas
description Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. Results In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p  
doi_str_mv 10.1007/s11255-024-04009-5
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3031133856</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3031133856</sourcerecordid><originalsourceid>FETCH-LOGICAL-c326t-6e4531c9073f1fbbac180589c0c10ee9dd9fbd0ddfff46a1b99c84ffc3a915eb3</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS0EoqXwAiyQJTZsAuM4TuJlVZUfqRIbWFuOPY5cJc6tnRTdN-CxmXDLj1iwsceab46P5jD2UsBbAdC9K0LUSlVQNxU0ALpSj9i5UJ2satU3j_-qz9izUm6BmB7gKTuTvWob6PQ5-37p8W6z7siXwA95KatdkTubHGZ64z2mNS6J2-R5cRkxxTTyjG6ZZ0ze7s2yD95Hj54PRyK5zWsM0UU78ZhWnKY4IglWh-UbZqImm0ekM42bpWJePE7P2ZNgp4IvHu4L9vX99Zerj9XN5w-fri5vKifrdq1abJQUTkMngwjDYJ3oQfXagROAqL3XYfDgfQihaa0YtHZ9E4KTVguFg7xgb0665Pluw7KaORZHHm3CZStGghRC7gsi9PU_6O2y5UTuiOpl1zYkSVR9ohxtr2QM5pDjbPPRCDB7TuaUk6GczM-czD706kF6G2b0v0d-BUOAPAGFWmnE_Ofv_8j-AJuqoY8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3083764915</pqid></control><display><type>article</type><title>Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Chiarelli, Giuseppe ; Stephens, Alex ; Finati, Marco ; Cirulli, Giuseppe Ottone ; Beatrici, Edoardo ; Filipas, Dejan K. ; Arora, Sohrab ; Tinsley, Shane ; Bhandari, Mahendra ; Carrieri, Giuseppe ; Trinh, Quoc-Dien ; Briganti, Alberto ; Montorsi, Francesco ; Lughezzani, Giovanni ; Buffi, Nicolò ; Rogers, Craig ; Abdollah, Firas</creator><creatorcontrib>Chiarelli, Giuseppe ; Stephens, Alex ; Finati, Marco ; Cirulli, Giuseppe Ottone ; Beatrici, Edoardo ; Filipas, Dejan K. ; Arora, Sohrab ; Tinsley, Shane ; Bhandari, Mahendra ; Carrieri, Giuseppe ; Trinh, Quoc-Dien ; Briganti, Alberto ; Montorsi, Francesco ; Lughezzani, Giovanni ; Buffi, Nicolò ; Rogers, Craig ; Abdollah, Firas</creatorcontrib><description>Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. Results In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p  &lt; 0.05). Conclusions GPT-4 displayed superior accuracy, clarity, conciseness, and readability than GPT-3.5. Though prompts influenced the quality response in both GPTs, their impact was significant only for clarity.</description><identifier>ISSN: 1573-2584</identifier><identifier>ISSN: 0301-1623</identifier><identifier>EISSN: 1573-2584</identifier><identifier>DOI: 10.1007/s11255-024-04009-5</identifier><identifier>PMID: 38564079</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Accuracy ; Artificial Intelligence ; Early Detection of Cancer - methods ; Humans ; Language ; Male ; Medicine ; Medicine &amp; Public Health ; Nephrology ; Prostate cancer ; Prostatic Neoplasms - diagnosis ; Prostatic Neoplasms - prevention &amp; control ; Readability ; Urology ; Urology - Original Paper</subject><ispartof>International urology and nephrology, 2024-08, Vol.56 (8), p.2589-2595</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s), under exclusive licence to Springer Nature B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-6e4531c9073f1fbbac180589c0c10ee9dd9fbd0ddfff46a1b99c84ffc3a915eb3</cites><orcidid>0000-0003-1298-8231</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11255-024-04009-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11255-024-04009-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38564079$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chiarelli, Giuseppe</creatorcontrib><creatorcontrib>Stephens, Alex</creatorcontrib><creatorcontrib>Finati, Marco</creatorcontrib><creatorcontrib>Cirulli, Giuseppe Ottone</creatorcontrib><creatorcontrib>Beatrici, Edoardo</creatorcontrib><creatorcontrib>Filipas, Dejan K.</creatorcontrib><creatorcontrib>Arora, Sohrab</creatorcontrib><creatorcontrib>Tinsley, Shane</creatorcontrib><creatorcontrib>Bhandari, Mahendra</creatorcontrib><creatorcontrib>Carrieri, Giuseppe</creatorcontrib><creatorcontrib>Trinh, Quoc-Dien</creatorcontrib><creatorcontrib>Briganti, Alberto</creatorcontrib><creatorcontrib>Montorsi, Francesco</creatorcontrib><creatorcontrib>Lughezzani, Giovanni</creatorcontrib><creatorcontrib>Buffi, Nicolò</creatorcontrib><creatorcontrib>Rogers, Craig</creatorcontrib><creatorcontrib>Abdollah, Firas</creatorcontrib><title>Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model</title><title>International urology and nephrology</title><addtitle>Int Urol Nephrol</addtitle><addtitle>Int Urol Nephrol</addtitle><description>Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. Results In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p  &lt; 0.05). Conclusions GPT-4 displayed superior accuracy, clarity, conciseness, and readability than GPT-3.5. Though prompts influenced the quality response in both GPTs, their impact was significant only for clarity.</description><subject>Accuracy</subject><subject>Artificial Intelligence</subject><subject>Early Detection of Cancer - methods</subject><subject>Humans</subject><subject>Language</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine &amp; Public Health</subject><subject>Nephrology</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Prostatic Neoplasms - prevention &amp; control</subject><subject>Readability</subject><subject>Urology</subject><subject>Urology - Original Paper</subject><issn>1573-2584</issn><issn>0301-1623</issn><issn>1573-2584</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1TAQhS0EoqXwAiyQJTZsAuM4TuJlVZUfqRIbWFuOPY5cJc6tnRTdN-CxmXDLj1iwsceab46P5jD2UsBbAdC9K0LUSlVQNxU0ALpSj9i5UJ2satU3j_-qz9izUm6BmB7gKTuTvWob6PQ5-37p8W6z7siXwA95KatdkTubHGZ64z2mNS6J2-R5cRkxxTTyjG6ZZ0ze7s2yD95Hj54PRyK5zWsM0UU78ZhWnKY4IglWh-UbZqImm0ekM42bpWJePE7P2ZNgp4IvHu4L9vX99Zerj9XN5w-fri5vKifrdq1abJQUTkMngwjDYJ3oQfXagROAqL3XYfDgfQihaa0YtHZ9E4KTVguFg7xgb0665Pluw7KaORZHHm3CZStGghRC7gsi9PU_6O2y5UTuiOpl1zYkSVR9ohxtr2QM5pDjbPPRCDB7TuaUk6GczM-czD706kF6G2b0v0d-BUOAPAGFWmnE_Ofv_8j-AJuqoY8</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Chiarelli, Giuseppe</creator><creator>Stephens, Alex</creator><creator>Finati, Marco</creator><creator>Cirulli, Giuseppe Ottone</creator><creator>Beatrici, Edoardo</creator><creator>Filipas, Dejan K.</creator><creator>Arora, Sohrab</creator><creator>Tinsley, Shane</creator><creator>Bhandari, Mahendra</creator><creator>Carrieri, Giuseppe</creator><creator>Trinh, Quoc-Dien</creator><creator>Briganti, Alberto</creator><creator>Montorsi, Francesco</creator><creator>Lughezzani, Giovanni</creator><creator>Buffi, Nicolò</creator><creator>Rogers, Craig</creator><creator>Abdollah, Firas</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1298-8231</orcidid></search><sort><creationdate>20240801</creationdate><title>Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model</title><author>Chiarelli, Giuseppe ; Stephens, Alex ; Finati, Marco ; Cirulli, Giuseppe Ottone ; Beatrici, Edoardo ; Filipas, Dejan K. ; Arora, Sohrab ; Tinsley, Shane ; Bhandari, Mahendra ; Carrieri, Giuseppe ; Trinh, Quoc-Dien ; Briganti, Alberto ; Montorsi, Francesco ; Lughezzani, Giovanni ; Buffi, Nicolò ; Rogers, Craig ; Abdollah, Firas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-6e4531c9073f1fbbac180589c0c10ee9dd9fbd0ddfff46a1b99c84ffc3a915eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Artificial Intelligence</topic><topic>Early Detection of Cancer - methods</topic><topic>Humans</topic><topic>Language</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine &amp; Public Health</topic><topic>Nephrology</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Prostatic Neoplasms - prevention &amp; control</topic><topic>Readability</topic><topic>Urology</topic><topic>Urology - Original Paper</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chiarelli, Giuseppe</creatorcontrib><creatorcontrib>Stephens, Alex</creatorcontrib><creatorcontrib>Finati, Marco</creatorcontrib><creatorcontrib>Cirulli, Giuseppe Ottone</creatorcontrib><creatorcontrib>Beatrici, Edoardo</creatorcontrib><creatorcontrib>Filipas, Dejan K.</creatorcontrib><creatorcontrib>Arora, Sohrab</creatorcontrib><creatorcontrib>Tinsley, Shane</creatorcontrib><creatorcontrib>Bhandari, Mahendra</creatorcontrib><creatorcontrib>Carrieri, Giuseppe</creatorcontrib><creatorcontrib>Trinh, Quoc-Dien</creatorcontrib><creatorcontrib>Briganti, Alberto</creatorcontrib><creatorcontrib>Montorsi, Francesco</creatorcontrib><creatorcontrib>Lughezzani, Giovanni</creatorcontrib><creatorcontrib>Buffi, Nicolò</creatorcontrib><creatorcontrib>Rogers, Craig</creatorcontrib><creatorcontrib>Abdollah, Firas</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>International urology and nephrology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chiarelli, Giuseppe</au><au>Stephens, Alex</au><au>Finati, Marco</au><au>Cirulli, Giuseppe Ottone</au><au>Beatrici, Edoardo</au><au>Filipas, Dejan K.</au><au>Arora, Sohrab</au><au>Tinsley, Shane</au><au>Bhandari, Mahendra</au><au>Carrieri, Giuseppe</au><au>Trinh, Quoc-Dien</au><au>Briganti, Alberto</au><au>Montorsi, Francesco</au><au>Lughezzani, Giovanni</au><au>Buffi, Nicolò</au><au>Rogers, Craig</au><au>Abdollah, Firas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model</atitle><jtitle>International urology and nephrology</jtitle><stitle>Int Urol Nephrol</stitle><addtitle>Int Urol Nephrol</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>56</volume><issue>8</issue><spage>2589</spage><epage>2595</epage><pages>2589-2595</pages><issn>1573-2584</issn><issn>0301-1623</issn><eissn>1573-2584</eissn><abstract>Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. Results In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p  &lt; 0.05). Conclusions GPT-4 displayed superior accuracy, clarity, conciseness, and readability than GPT-3.5. Though prompts influenced the quality response in both GPTs, their impact was significant only for clarity.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>38564079</pmid><doi>10.1007/s11255-024-04009-5</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-1298-8231</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 1573-2584
ispartof International urology and nephrology, 2024-08, Vol.56 (8), p.2589-2595
issn 1573-2584
0301-1623
1573-2584
language eng
recordid cdi_proquest_miscellaneous_3031133856
source MEDLINE; SpringerLink Journals - AutoHoldings
subjects Accuracy
Artificial Intelligence
Early Detection of Cancer - methods
Humans
Language
Male
Medicine
Medicine & Public Health
Nephrology
Prostate cancer
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - prevention & control
Readability
Urology
Urology - Original Paper
title Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T19%3A59%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Adequacy%20of%20prostate%20cancer%20prevention%20and%20screening%20recommendations%20provided%20by%20an%20artificial%20intelligence-powered%20large%20language%20model&rft.jtitle=International%20urology%20and%20nephrology&rft.au=Chiarelli,%20Giuseppe&rft.date=2024-08-01&rft.volume=56&rft.issue=8&rft.spage=2589&rft.epage=2595&rft.pages=2589-2595&rft.issn=1573-2584&rft.eissn=1573-2584&rft_id=info:doi/10.1007/s11255-024-04009-5&rft_dat=%3Cproquest_cross%3E3031133856%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3083764915&rft_id=info:pmid/38564079&rfr_iscdi=true