期刊名：Diagnostics

期刊主页：https://www.mdpi.com/journal/diagnostics

医学诊断承载着守护生命的使命，而人工智能的迅猛发展正为这一领域带来前所未有的变革。AI不再只是辅助工具，它正在成为提升诊断精准度与效率的核心力量。本期我们为大家精选“人工智能赋能临床诊断”方向的高质量文献，让我们一同走进智能技术与医学深度融合的前沿，感受科技赋能健康的时代脉动。

1. Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?

诊断影像中增强现实技术的整合探索：神话还是现实？

https://www.mdpi.com/2075-4418/14/13/1333

Lastrucci, A.; Wandael, Y.; Barra, A.; Ricci, R.; Maccioni, G.; Pirrera, A.; Giansanti, D. Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality? Diagnostic 2024, 14, 1333.

2. From Data to Insights: How is AI Revolutionizing Small Bowel Endoscopy

从数据到洞察：人工智能如何革新小肠内镜检查

https://www.mdpi.com/2075-4418/14/3/291

Mota, J.; Almeida, M.J.; Mendes, F.; Martins, M.; Ribeiro, T.; Afonso, J.; Cardoso, P.; Cardoso, H.; Andrade, P.; Ferreira, J.; et al. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics 2024, 14, 291.

3. AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients

AI辅助X光骨折检测在住院医师培训中的应用评估：儿科与成人创伤患者研究

https://www.mdpi.com/2075-4418/14/6/596

Meetschen, M.; Salhöfer, L.; Beck, N.; Kroll, L.; Ziegenfuß, C.D.; Schaarschmidt, B.M.; Forsting, M.; Mizan, S.; Umutlu, L.; Hosch, R.; et al. AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients. Diagnostics 2024, 14, 596.

4. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

多通道CT与放射组学引导的CNN-ViT混合网络（RadCT-CNNViT）用于肺结节病诊断

https://www.mdpi.com/2075-4418/14/10/1049

Qiu, J.; Mitra, J.; Ghose, S.; Dumas, C.; Yang, J.; Sarachan, B.; Judson, M.A. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis. Diagnostics 2024, 14, 1049. 13.

5.Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

胰腺腺癌：影像学检查手段及人工智能在分析CT与MRI图像中的作用

https://www.mdpi.com/2075-4418/14/4/438

Anghel, C.; Grasu, M.C.; Anghel, D.A.; Rusu-Munteanu, G.-I.; Dumitru, R.L.; Lupescu, I.G. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics 2024, 14, 438.

6. Urine-Based Machine Learning Assay Detects Prostate Cancer

基于尿液的机器学习检测方法可识别前列腺癌

https://www.mdpi.com/2075-4418/16/7/993

Hausman, M.S.; Ambert, K.; Nagesetti, A.; Lim, F.B.H.; Swaminathan, M.; Cardwell, R.F.; Piloto, O. Urine-Based Machine Learning Assay Detects Prostate Cancer. Diagnostics 2026, 16, 993.

7. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

乳腺癌影像中的深度学习：2024年初的最新技术现状与进展

https://www.mdpi.com/2075-4418/14/8/848

Carriero, A.; Groenhoff, L.; Vologina, E.; Basile, P.; Albera, M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics 2024, 14, 848.

8. Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images

基于深度学习的超广角眼底图像视网膜疾病诊断

https://www.mdpi.com/2075-4418/14/1/105

Nguyen, T.D.; Le, D.-T.; Bum, J.; Kim, S.; Song, S.J.; Choo, H. Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images. Diagnostics 2024, 14, 105.

9. Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods

https://www.mdpi.com/2075-4418/14/22/2516

人工智能在运动医学诊断中的应用：损伤风险预测方法的综合综述

Musat, C.L.; Mereuta, C.; Nechita, A.; Tutunaru, D.; Voipan, A.E.; Voipan, D.; Mereuta, E.; Gurau, T.V.; Gur?u, G.; Nechita, L.C. Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods. Diagnostics 2024, 14, 2516.

10. Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation

用于医学图像分析与解读的深度学习人工神经网络技术的发展

https://www.mdpi.com/2075-4418/15/9/1072

Shobayo, O.; Saatchi, R. Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation. Diagnostics 2025, 15, 1072.

11. Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists

公开可用的大型语言模型在角膜疾病中的诊断性能：与人类专家的比较

https://www.mdpi.com/2075-4418/15/10/1221

Jiao, C.; Rosas, E.; Asadigandomani, H.; Delsoz, M.; Madadi, Y.; Raja, H.; Munir, W.M.; Tamm, B.; Mehravaran, S.; Djalilian, A.R.; et al. Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists. Diagnostics 2025, 15, 1221.

12. Artificial Intelligence Applications in Pediatric Craniofacial Surgery

人工智能在儿童颅颌面外科中的应用

https://www.mdpi.com/2075-4418/15/7/829

Harrison, L.M.; Edison, R.L.; Hallac, R.R. Artificial Intelligence Applications in Pediatric Craniofacial Surgery. Diagnostics 2025, 15, 829.

13. Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets

通过数据集融合构建更优深度学习模型：基于超数据集的皮肤癌分类案例研究

https://www.mdpi.com/2075-4418/15/3/352

Georgiadis, P.; Gkouvrikos, E.V.; Vrochidou, E.; Kalampokas, T.; Papakostas, G.A. Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets. Diagnostics 2025, 15, 352.

14. Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA

https://www.mdpi.com/2075-4418/15/14/1822

冠状动脉斑块特征分析的机器学习方法：基于OCT、IVUS与CCTA的多模态综述

Pinna, A.; Boi, A.; Mannelli, L.; Balestrieri, A.; Sanfilippo, R.; Suri, J.; Saba, L. Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA. Diagnostics 2025, 15, 1822.

15. Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning

利用自监督学习在小规模MRI数据集中检测与分割小型脑肿瘤的可行性研究

https://www.mdpi.com/2075-4418/15/3/249

Zhang, W.-J.; Chen, W.-T.; Liu, C.-H.; Chen, S.-W.; Lai, Y.-H.; You, S.D. Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning. Diagnostics 2025, 15, 249.

Diagnostics 期刊介绍

主编：Prof. Dr. Andreas Kjaer, University of Copenhagen, Denmark

Diagnostics (ISSN 2075-4418) 涵盖医学诊断各个方面的研究成果，刊载研究论文、综述及短讯，鼓励学者发表详细的实验和理论结果，包括但不限于医学影像、病理与分子诊断、传染病诊断、即时诊断技术与设备、光学诊断技术与设备、机器学习和人工智能诊断技术等。目前，期刊已被ESCI (Web of Science)、Scopus、PubMed等多个国际数据库收录。

期刊主页：https://www.mdpi.com/journal/diagnostics

投稿或咨询合作，请邮件联系期刊编辑部: diagnostics@mdpi.com