Shin Yoojin, Lee Mingyu, Lee Yoonji, Kim Kyuri, Kim Taejung
College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea.
College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, Republic of Korea.
Life (Basel). 2025 Apr 16;15(4):654. doi: 10.3390/life15040654.
Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.
人工智能正在迅速改变医疗保健领域的质量保证，推动诊断、手术和患者护理方面的进步。本综述全面分析了人工智能在关键临床领域的整合情况，特别是卷积神经网络和循环神经网络，显著提高了诊断准确性、手术表现和病理评估水平。基于人工智能的方法已显示出优于传统方法的明显优势：卷积神经网络在扫描仪故障检测中的准确率达到91.56%，超过了人工检查；在人工智能辅助下，内镜病变检测的灵敏度从2.3%提高到了6.1%；胃癌浸润深度分类的准确率达到89.16%，比人类内镜医师高出17.25%。在病理学方面，人工智能在识别失焦区域时的准确率达到93.2%，在淋巴细胞定量分析中的F1分数为0.94，促进了更快、更可靠的诊断。同样，人工智能在手术流程识别方面的准确率超过81%，在技能评估分类方面的准确率超过95%。除了传统的诊断和手术支持外，人工智能驱动的可穿戴传感器、药物输送系统和生物集成设备正在通过优化生理监测、自动化护理方案和提高治疗精度来推进个性化治疗。尽管取得了这些成就，但在数据标准化、伦理治理和模型通用性等领域仍存在挑战。总体而言，这些发现强调了人工智能在多个参数上优于传统技术的潜力，强调了持续发展、严格的临床验证和跨学科合作的必要性，以充分实现其在精准医学和患者安全中的作用。
