<?xml version="1.0" encoding="utf-8"?>
			<journal>
			<title>The Archives of Bone and Joint Surgery</title>
			<title_fa></title_fa>
			<short_title>ABJS</short_title>
			<subject>Medical Sciences</subject>
			<web_url>https://abjs.mums.ac.ir/</web_url>
			<journal_hbi_system_id>0</journal_hbi_system_id>
			<journal_hbi_system_user></journal_hbi_system_user>
			<journal_id_issn>2345-4644</journal_id_issn>
			<journal_id_issn_online>2345-461X</journal_id_issn_online>
			<journal_id_pii></journal_id_pii>
			<journal_id_doi></journal_id_doi>
			<journal_id_iranmedex></journal_id_iranmedex>
			<journal_id_magiran></journal_id_magiran>
			<journal_id_sid></journal_id_sid>
			<journal_id_nlai></journal_id_nlai>
			<journal_id_science></journal_id_science>
			<language>en</language>
			<pubdate>
				<type>jalali</type>
				<year>0</year>
				<month>0</month>
				<day>1</day>
			</pubdate>
			<pubdate>
				<type>gregorian</type>
				<year>2025</year>
				<month>4</month>
				<day>1</day>
			</pubdate>
			<volume>13</volume>
			<number>4</number>
			<publish_type>online</publish_type>
			<publish_edition>1</publish_edition>
			<article_type>fulltext</article_type>
			<articleset><article>
				<language>en</language>
				<article_id_issn></article_id_issn>
				<article_id_issn_online></article_id_issn_online>
				<article_id_pubmed></article_id_pubmed>
				<article_id_pii></article_id_pii>
				<article_id_doi></article_id_doi>
				<article_id_iranmedex></article_id_iranmedex>
				<article_id_magiran></article_id_magiran>
				<article_id_sid></article_id_sid>
				<title_fa></title_fa>
				<title>From Algorithms to Academia: An Endeavor to Benchmark AI-Generated Scientific Papers against Human Standards</title>
				<subject_fa></subject_fa>
				<subject></subject>
				<content_type_fa></content_type_fa>
				<content_type>RESEARCH PAPER</content_type>
				<abstract_fa><![CDATA[]]></abstract_fa>
				<abstract><![CDATA[Objectives: The aim of this study is to quantitatively investigate the accuracy of text generated by AI large language models while comparing their readability and likelihood of being accepted to a scientific compared to human-authored papers on the same topics.Methods: The study consisted of two papers written by ChatGPT, two papers written by Assistant by scite, and two papers written by humans. A total of six independent reviewers were blinded to the authorship of each paper and assigned a grade to each subsection on a scale of 1 to 4. Additionally, each reviewer was asked to guess if the paper was written by a human or AI and explain their reasoning. The study authors also graded each AI-generated paper based on factual accuracy of the claims and citations.Results: The human-written calcaneus fracture paper received the highest score of a 3.70/4, followed by Assistantwritten calcaneus fracture paper (3.02/4), human-written ankle osteoarthritis paper (2.98/4), ChatGPT calcaneus fracture (2.89/4), ChatGPT Ankle Osteoarthritis (2.87/4), and Assistant Ankle Osteoarthritis (2.78/4). The human calcaneus fracture paper received a statistically significant higher rating than the ChatGPT calcaneus fracture paper (P = 0.028) and the Assistant calcaneus fracture paper (P = 0.043). The ChatGPT osteoarthritis review showed 100% factual accuracy, the ChatGPT calcaneus fracture review was 97.46% factually accurate, the Assistant calcaneus fracture was 95.56% accurate, and the Assistant ankle osteoarthritis was 94.98% accurate. Regarding citations, the ChatGPT ankle osteoarthritis paper was 90% accurate, the ChatGPT calcaneus fracture was 69.23% accurate, the Assistant ankle osteoarthritis was 35.14% accurate, and the Assistant calcaneus fracture was 39.68% accurate. Conclusion: Through this paper we emphasize that while AI holds the promise of enhancing knowledge sharing, it must be used responsibly and in conjunction with comprehensive fact-checking procedures to maintain the integrity of the scientific discourse. Level of evidence: III]]></abstract>
				<keyword_fa></keyword_fa>
				<keyword>Artificial intelligence, ChatGPT, Large Language Models, Natural Language Processing, Prompt Engineering</keyword>
				<start_page>212</start_page>
				<end_page>222</end_page>
				<web_url>https://abjs.mums.ac.ir/article_25053.html</web_url>
			<author_list><author>
				<first_name>Jackson</first_name>
				<middle_name></middle_name>
				<last_name>Woodrow</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>jwoodrow@mgh.harvard.edu</email>
				<code>109859</code>
				<coreauthor>Yes</coreauthor>
				<affiliation>Foot &amp; Ankle Research and Innovation Lab (FARIL), 
Department of Orthopaedic Surgery, Massachusetts General 
Hospital, Harvard Medical School, Boston, MA, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Nour</first_name>
				<middle_name></middle_name>
				<last_name>Nassour</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>nnassour@mgh.harvard.edu</email>
				<code>109860</code>
				<coreauthor>No</coreauthor>
				<affiliation>Foot &amp; Ankle Research and Innovation Lab (FARIL), 
Department of Orthopaedic Surgery, Massachusetts General 
Hospital, Harvard Medical School, Boston, MA, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>John</first_name>
				<middle_name>Y.</middle_name>
				<last_name>Kwon</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>jkwon@mgb.org</email>
				<code>109861</code>
				<coreauthor>No</coreauthor>
				<affiliation>Foot &amp; Ankle Research and Innovation Lab (FARIL), 
Department of Orthopaedic Surgery, Massachusetts General 
Hospital, Harvard Medical School, Boston, MA, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Soheil</first_name>
				<middle_name></middle_name>
				<last_name>Ashkani-Esfahani</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>sashkaniesfahani@mgh.harvard.edu</email>
				<code>109862</code>
				<coreauthor>No</coreauthor>
				<affiliation>Foot &amp; Ankle Research and Innovation Lab (FARIL), 
Department of Orthopaedic Surgery, Massachusetts General 
Hospital, Harvard Medical School, Boston, MA, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author><author>
				<first_name>Mitchel</first_name>
				<middle_name></middle_name>
				<last_name>Harris</last_name>
				<suffix></suffix>
				<first_name_fa></first_name_fa>
				<middle_name_fa></middle_name_fa>
				<last_name_fa></last_name_fa>
				<suffix_fa></suffix_fa>
				<email>mbharris@mgh.harvard.edu</email>
				<code>109863</code>
				<coreauthor>No</coreauthor>
				<affiliation>Foot &amp; Ankle Research and Innovation Lab (FARIL), 
Department of Orthopaedic Surgery, Massachusetts General 
Hospital, Harvard Medical School, Boston, MA, USA</affiliation>
				<affiliation_fa></affiliation_fa>
				 </author></author_list>
				</article>
			</articleset>
			</journal>