Algarni Ali
Department of Informatics and Computer Systems, King Khalid University, 62527, Abha City, Saudi Arabia.
Sci Rep. 2025 Jul 2;15(1):22727. doi: 10.1038/s41598-025-01518-w.
The rapid integration of artificial intelligence (AI) into healthcare has enhanced diagnostic accuracy; however, patient engagement and satisfaction remain significant challenges that hinder the widespread acceptance and effectiveness of AI-driven clinical tools. This study introduces CareAssist-GPT, a novel AI-assisted diagnostic model designed to improve both diagnostic accuracy and the patient experience through real-time, understandable, and empathetic communication. CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. Model performance was evaluated in terms of accuracy, precision, recall, specificity, and response time, alongside patient satisfaction through a structured user feedback survey. CareAssist-GPT achieved a diagnostic accuracy of 95.8%, improving by 2.4% over conventional models. It reported high precision (94.3%), recall (93.8%), and specificity (92.7%), with an AUC-ROC of 0.97. The system responded within 500 ms-23.1% faster than existing tools-and achieved a patient satisfaction score of 9.3 out of 10, demonstrating its real-time usability and communicative effectiveness. CareAssist-GPT significantly enhances the diagnostic process by improving accuracy and fostering patient trust through transparent, real-time explanations. These findings position it as a promising patient-centered AI solution capable of transforming healthcare delivery by bridging the gap between advanced diagnostics and human-centered communication.
人工智能（AI）在医疗保健领域的迅速整合提高了诊断准确性；然而，患者参与度和满意度仍然是重大挑战，阻碍了人工智能驱动的临床工具的广泛接受和有效性。本研究介绍了CareAssist-GPT，这是一种新型的人工智能辅助诊断模型，旨在通过实时、易懂且有同理心的沟通来提高诊断准确性和患者体验。CareAssist-GPT使用深度学习在统一的预测框架内结合了高分辨率X光图像、实时生理生命体征和临床记录。使用卷积神经网络（CNN）、门控循环单元（GRU）和基于Transformer的自然语言处理模块进行特征提取。通过结构化用户反馈调查，从准确性、精确性、召回率、特异性和响应时间以及患者满意度方面评估模型性能。CareAssist-GPT的诊断准确率达到95.8%，比传统模型提高了2.4%。它的精确率（94.3%）、召回率（93.8%）和特异性（92.7%）都很高，曲线下面积（AUC-ROC）为0.97。该系统的响应时间在500毫秒以内，比现有工具快23.1%，患者满意度得分为9.3分（满分10分），证明了其实时可用性和沟通有效性。CareAssist-GPT通过提高准确性并通过透明的实时解释增强患者信任，显著改善了诊断过程。这些发现使其成为一个有前途的以患者为中心的人工智能解决方案，能够通过弥合先进诊断与以人为本的沟通之间的差距来改变医疗服务。
