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...
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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 |
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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
< 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 & Public Health ; Nephrology ; Prostate cancer ; Prostatic Neoplasms - diagnosis ; Prostatic Neoplasms - prevention & 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
< 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 & Public Health</subject><subject>Nephrology</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Prostatic Neoplasms - prevention & 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 & Public Health</topic><topic>Nephrology</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Prostatic Neoplasms - prevention & 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 & Calcified Tissue Abstracts</collection><collection>ProQuest Health & 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
< 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> |
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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 |
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