Clinical Report: Evaluating the Effectiveness of DeepSeek-R1 and ChatGPT-4.0
Overview
This study compares the effectiveness of DeepSeek-R1 and ChatGPT-4.0 as informational resources for nursing care in gout. Findings indicate that while both models provide high-quality responses, ChatGPT-4.0 offers greater readability and coherence.
Background
The integration of large language models (LLMs) like DeepSeek-R1 and ChatGPT-4.0 in nursing care represents a significant advancement in utilizing artificial intelligence for clinical decision-making. Understanding their effectiveness is crucial for enhancing evidence-based management of conditions such as gout, which requires precise and accessible information for optimal patient care.
Data Highlights
Metric
DeepSeek-R1
ChatGPT-4.0
p-value
FKGL
13.04 ± 1.62
11.41 ± 1.74
0.013
FRE
40.50 ± 8.12
49.08 ± 8.90
0.010
mDISCERN Score
3.98 ± 0.70
4.30 ± 0.73
0.16
Average Publication Age (years)
3.57 ± 2.33
5.42 ± 2.34
<0.05
Key Findings
DeepSeek-R1 had a higher FKGL than ChatGPT-4.0, indicating lower readability.
ChatGPT-4.0 scored better on the mDISCERN quality score, though not statistically significant.
DeepSeek-R1 referenced 21 sources, while ChatGPT-4.0 referenced 23, with clinical guidelines being predominant in both.
DeepSeek-R1 provided more current citations but had invalid reference links.
Both models produced high-quality, professional responses for nursing care in gout.
Clinical Implications
Healthcare professionals should consider the readability and coherence of AI-generated content when utilizing LLMs for patient education and clinical decision support. While both models are valuable, ChatGPT-4.0 may be more suitable for direct patient communication due to its superior readability.
Conclusion
This comparative analysis highlights the strengths and weaknesses of DeepSeek-R1 and ChatGPT-4.0 as informational resources in nursing care for gout, emphasizing the importance of readability and quality in AI-generated responses.
