AI-Powered Language Models for Enhanced Healthcare Conversations and Clinical Knowledge Extraction

The integration of artificial intelligence (AI) in healthcare offers promising avenues for enhancing diagnostic accuracy and patient engagement. However, AI models must be evaluated for their effectiveness in healthcare-specific tasks. This study assesses the performance of three AI models: BERT, GPT-3, and ClinicalBERT in the simulated healthcare environments, focusing on their conversational AI capabilities and medical knowledge extraction. We conducted a comparative evaluation using simulated patient interactions. ClinicalBERT was trained on clinical data from the MIMIC-III database, while BERT and GPT-3 utilized generalized language processing. Five medical experts assessed model performance across four metrics: Accuracy, Relevance, Coherence, and Medical Appropriateness. ClinicalBERT, with its specialized training, significantly outperformed BERT and GPT-3 across multiple metrics, including accuracy and F1 scores. For example, ClinicalBERT achieved an F1 score of 0.82, indicating its superior ability to interpret complex medical dialogues and extract relevant information. The study concludes by emphasizing the necessity of tailored training for AI models in healthcare. ClinicalBERT’s performance suggests domain-specific AI can enhance clinical outcomes. A hybrid approach combining generalist and specialist AI capabilities may further optimize healthcare communication. Future research should explore these models and address ethical and practical considerations in the clinical AI deployment.