Nishida Naoshi
Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 377-2 Ohno-Higashi, Osakasayama 589-8511, Japan.
Bioengineering (Basel). 2024 Dec 9;11(12):1243. doi: 10.3390/bioengineering11121243.
Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable in healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses that of human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features that are imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to make more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal plans for treatment, making it an essential component in the provision of the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to advance personalized diagnostics and treatments and contribute to overall improvements in healthcare quality.
肝脏疾病会显著影响预期寿命,这使得早期诊断和治疗干预成为医疗保健中的关键挑战。影像诊断在肝脏疾病的诊断和管理中发挥着至关重要的作用。近年来,人工智能(AI)在医学影像分析中的应用在医疗保健领域已变得不可或缺。基于大量医学图像数据集进行训练的AI,有时已展现出超越人类专家的诊断准确性。预计AI辅助影像诊断将对诊断质量的标准化做出重大贡献。此外,AI有潜力识别出人类难以察觉的图像特征,从而在临床决策中发挥重要作用。这种能力使医生能够做出更准确的诊断并制定有效的治疗策略,最终改善患者的治疗效果。此外,AI有望成为个性化医疗中的强大工具。通过将个体患者的影像数据与临床信息相结合,AI可以提出最佳治疗方案,使其成为为每位患者提供最合适护理的重要组成部分。当前的报告突出了AI在肝脏疾病管理中的优势。随着AI技术不断发展,预计它将推动个性化诊断和治疗,并为整体医疗质量的提升做出贡献。