Performance of DeepSeek V3.2 and ChatGPT 5.1 in Musculoskeletal Triage and Differential Diagnosis of Outpatients With Low Back Pain: Multidimensional Comparative Study - Report - MDSpire
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Performance of DeepSeek V3.2 and ChatGPT 5.1 in Musculoskeletal Triage and Differential Diagnosis of Outpatients With Low Back Pain: Multidimensional Comparative Study
Clinical Report: Evaluation of DeepSeek V3.2 and ChatGPT 5.1 in Triage of LBP
Overview
This study evaluates the diagnostic capabilities of two AI chatbots, DeepSeek V3.2 and ChatGPT 5.1, in the triage and differential diagnosis of outpatients with low back pain (LBP).
Background
Musculoskeletal disorders (MSDs) are prevalent and contribute significantly to healthcare burdens, with a notable increase in incidence over the past two decades. Effective triage and diagnosis of conditions like low back pain are essential for optimizing patient care and resource allocation. The integration of artificial intelligence, particularly large language models, is being explored for enhancing diagnostic accuracy in outpatient settings.
Data Highlights
No numerical data or trial data was provided in the source material.
Key Findings
DeepSeek V3.2 and ChatGPT 5.1 were assessed for their ability to classify MSDs based on patient complaints.
The study utilized standardized questionnaires derived from real outpatient records for evaluation.
Both AI models were evaluated for their performance in the clinical diagnosis and triage of low back pain.
The complexity of MSDs necessitates advanced diagnostic tools.
LLMs like DeepSeek and ChatGPT are being investigated for their roles in the medical field.
Clinical Implications
AI chatbots may support outpatient physicians in the triage and diagnosis of low back pain.
Conclusion
The study evaluates AI chatbots in the triage and diagnostic processes for low back pain, addressing challenges posed by musculoskeletal disorders.
