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Generative AI Guidelines in Korean Medical Journals: A Survey Using Human-AI Collaboration

Ahn, S.

2024-03-09 health informatics
10.1101/2024.03.08.24303960 medRxiv
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BackgroundGenerative artificial intelligence (GAI) tools, such as large language models, have the potential to revolutionize medical research and writing, but their use also raises important ethical and practical concerns. This study examines the prevalence and content of GAI guidelines among Korean medical journals to assess the current landscape and inform future policy development. MethodsTop 100 Korean medical journals by H-index were surveyed. Author guidelines were collected and screened by a human author and AI chatbot to identify GAI-related content. Key components of GAI policies were extracted and compared across journals. Journal characteristics associated with GAI guideline adoption were also analyzed. ResultsOnly 18% of the surveyed journals had GAI guidelines, which is much lower than previously reported international journals. However, adoption rates increased over time, reaching 57.1% in the first quarter of 2024. Higher-impact journals were more likely to have GAI guidelines. All journals with GAI guidelines required authors to declare GAI use, and 94.4% prohibited AI authorship. Key policy components included emphasizing human responsibility (72.2%), discouraging AI-generated content (44.4%), and exempting basic AI tools (38.9%). ConclusionWhile GAI guideline adoption among Korean medical journals is lower than global trends, there is a clear increase in implementation over time. The key components of these guidelines align with international standards, but greater standardization and collaboration are needed to ensure responsible and ethical use of GAI in medical research and writing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/24303960v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@20d506org.highwire.dtl.DTLVardef@181a810org.highwire.dtl.DTLVardef@140cee8org.highwire.dtl.DTLVardef@1cff266_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 18%
10.2%
2
Healthcare
16 papers in training set
Top 0.1%
8.5%
3
BMJ Health & Care Informatics
13 papers in training set
Top 0.1%
4.9%
4
Journal of the American Medical Informatics Association
61 papers in training set
Top 0.7%
4.0%
5
Journal of Medical Internet Research
85 papers in training set
Top 1%
3.7%
6
BMC Medical Informatics and Decision Making
39 papers in training set
Top 0.8%
3.6%
7
JAMIA Open
37 papers in training set
Top 0.4%
3.6%
8
JMIR Medical Informatics
17 papers in training set
Top 0.3%
3.6%
9
Scientific Reports
3102 papers in training set
Top 35%
3.6%
10
PLOS Digital Health
91 papers in training set
Top 0.9%
2.8%
11
Artificial Intelligence in Medicine
15 papers in training set
Top 0.2%
2.4%
50% of probability mass above
12
BMC Medical Education
20 papers in training set
Top 0.4%
2.1%
13
Computers in Biology and Medicine
120 papers in training set
Top 2%
2.1%
14
Biology Methods and Protocols
53 papers in training set
Top 0.6%
2.1%
15
Journal of Personalized Medicine
28 papers in training set
Top 0.2%
2.1%
16
International Journal of Medical Informatics
25 papers in training set
Top 0.7%
1.9%
17
BMJ Open
554 papers in training set
Top 8%
1.9%
18
Frontiers in Public Health
140 papers in training set
Top 5%
1.7%
19
BMC Medicine
163 papers in training set
Top 4%
1.7%
20
PeerJ
261 papers in training set
Top 7%
1.7%
21
Frontiers in Medicine
113 papers in training set
Top 4%
1.3%
22
JAMA
17 papers in training set
Top 0.1%
1.3%
23
Frontiers in Neurology
91 papers in training set
Top 3%
1.3%
24
BioMed Research International
25 papers in training set
Top 2%
1.2%
25
Heliyon
146 papers in training set
Top 3%
1.2%
26
JMIR Public Health and Surveillance
45 papers in training set
Top 3%
1.0%
27
Life
27 papers in training set
Top 0.2%
1.0%
28
International Journal of Environmental Research and Public Health
124 papers in training set
Top 6%
1.0%
29
The Lancet Digital Health
25 papers in training set
Top 0.8%
0.9%
30
Journal of Public Health
23 papers in training set
Top 0.9%
0.9%