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Exploring Body Donation Communication with Large Language Models: Accuracy, Readability, and Ethical Considerations

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Title: Exploring Body Donation Communication with Large Language Models: Accuracy, Readability, and Ethical Considerations
Language: English
Authors: Fulya Temizsoy Korkmaz (ORCID 0000-0001-7048-036X), Fatma Ok (ORCID 0000-0002-7777-5459), Burak Karip (ORCID 0000-0002-6757-4960), Papatya Keles (ORCID 0000-0002-0222-5081)
Source: Anatomical Sciences Education. 2025 18(11):1238-1249.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Donors, Human Body, Instructional Materials, Artificial Intelligence, Accuracy, Readability, Ethics
DOI: 10.1002/ase.70120
ISSN: 1935-9772
1935-9780
Abstract: Educational materials advocating whole-body donation must be accurate, easy to read, and transparent, as one potential solution to the fact that the supply of donations is not keeping pace with educational demand, thereby disrupting anatomy education programs. The use of AI technologies to supplement communications with prospective donors and next of kin deserves investigation to determine whether LLM-based approaches meet the common requirements for effective communication. This study contributes to the limited literature on LLM-supported communications by presenting a comparative quantitative benchmark and an adaptable evaluation framework. Five LLMs (ChatGPT-4o, Grok3.0, Claude4Sonnet, Gemini2.5 Flash, DeepSeekR1) were used to generate responses to six frequently asked questions about body donation in Turkish. Four anatomists evaluated accuracy, quality, readability, and vocabulary diversity. Differences between models were statistically analyzed. The two top-performing models, ChatGPT-4o and Grok3.0, achieved mean quality scores of 21.7 ± 2.8 and 21.0 ± 5.1 on a 25-point checklist, and 4.58 ± 0.88 and 4.25 ± 1.03 on a 5-point global quality scale, significantly outperforming the remaining three systems (p < 0.037). Both maintained a below-secondary-school level on two validated readability indices (scores [greater than or equal to]67.8 and [greater than or equal to]40.2). LLM-produced body donation materials (e.g., informational texts and FAQs) may help promote the importance of whole-body donations by providing accessible and reliable information, potentially streamlining the creation of first drafts and reducing staff workload. Given the sensitivity of donation decisions, ethical transparency, cultural sensitivity, and continuous human oversight are essential safeguards. Therefore, LLM use for such purposes should be governed by clear governance frameworks, regular expert audits, and publicly disclosed quality metrics.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1488671
Database: ERIC
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  Data: Exploring Body Donation Communication with Large Language Models: Accuracy, Readability, and Ethical Considerations
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  Data: Wiley. Available from: John Wiley &amp; Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 12
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Donors%22&quot;&gt;Donors&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Human+Body%22&quot;&gt;Human Body&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Instructional+Materials%22&quot;&gt;Instructional Materials&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Artificial+Intelligence%22&quot;&gt;Artificial Intelligence&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Accuracy%22&quot;&gt;Accuracy&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Readability%22&quot;&gt;Readability&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Ethics%22&quot;&gt;Ethics&lt;/searchLink&gt;
– Name: DOI
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  Data: 10.1002/ase.70120
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  Data: 1935-9772&lt;br /&gt;1935-9780
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Educational materials advocating whole-body donation must be accurate, easy to read, and transparent, as one potential solution to the fact that the supply of donations is not keeping pace with educational demand, thereby disrupting anatomy education programs. The use of AI technologies to supplement communications with prospective donors and next of kin deserves investigation to determine whether LLM-based approaches meet the common requirements for effective communication. This study contributes to the limited literature on LLM-supported communications by presenting a comparative quantitative benchmark and an adaptable evaluation framework. Five LLMs (ChatGPT-4o, Grok3.0, Claude4Sonnet, Gemini2.5 Flash, DeepSeekR1) were used to generate responses to six frequently asked questions about body donation in Turkish. Four anatomists evaluated accuracy, quality, readability, and vocabulary diversity. Differences between models were statistically analyzed. The two top-performing models, ChatGPT-4o and Grok3.0, achieved mean quality scores of 21.7 &#177; 2.8 and 21.0 &#177; 5.1 on a 25-point checklist, and 4.58 &#177; 0.88 and 4.25 &#177; 1.03 on a 5-point global quality scale, significantly outperforming the remaining three systems (p &lt; 0.037). Both maintained a below-secondary-school level on two validated readability indices (scores [greater than or equal to]67.8 and [greater than or equal to]40.2). LLM-produced body donation materials (e.g., informational texts and FAQs) may help promote the importance of whole-body donations by providing accessible and reliable information, potentially streamlining the creation of first drafts and reducing staff workload. Given the sensitivity of donation decisions, ethical transparency, cultural sensitivity, and continuous human oversight are essential safeguards. Therefore, LLM use for such purposes should be governed by clear governance frameworks, regular expert audits, and publicly disclosed quality metrics.
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