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Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study
3
Zitationen
6
Autoren
2024
Jahr
Abstract
BACKGROUND: Patients often struggle with determining which outpatient specialist to consult based on their symptoms. Natural language processing models in health care offer the potential to assist patients in making these decisions before visiting a hospital. OBJECTIVE: This study aimed to evaluate the performance of ChatGPT in recommending medical specialties for medical questions. METHODS: -score. RESULTS: -score of 0.587. CONCLUSIONS: Although ChatGPT did not surpass the fine-tuned KM-BERT model in recommending the correct medical specialties, it showcased notable advantages as a conversational artificial intelligence model. By providing detailed, contextually appropriate explanations, ChatGPT has the potential to significantly enhance patient comprehension of medical information, thereby improving the medical referral process.
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