Top Papers: KI in der Krebserkennung (2025)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2025 (von 6.810 insgesamt).

Krebs frühzeitig zu erkennen kann Leben retten – und genau hier setzt KI an. Deep-Learning-Modelle erreichen inzwischen bei bestimmten Tumorarten eine Erkennungsgenauigkeit, die mit der erfahrener Pathologen vergleichbar ist. Die Forschung umfasst Hautkrebs-Screening, Brustkrebs-Mammographie, Lungennoduli-Erkennung und vieles mehr. Hier finden Sie die einflussreichsten und neuesten Studien zu diesem Thema.

2023 2024 2025 2026

#	Paper	Zitationen
1	Medical SAM adapter: Adapting segment anything model for medical image segmentation Junde Wu, Ziyue Wang, Mingxuan Hong et al. Medical Image Analysis	265
2	Spirit In the Shell: A Mathematically Plausible Pathway from Emotions to Metacognition in Artificial Intelligence Systems Ogaro, Ronald Kisaka Zenodo (CERN European Organization for Nuclear Research)	139
3	Nationwide real-world implementation of AI for cancer detection in population-based mammography screening Nora Eisemann, Stefan Bunk, Trasias Mukama et al. Nature Medicine	134
4	The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry Lakshman P. Samaranayake, Nozimjon Tuygunov, Falk Schwendicke et al. International Dental Journal	132
5	A vision–language foundation model for precision oncology Jinxi Xiang, Xiyue Wang, Xiaoming Zhang et al. Nature	127
6	Deep Convolutional Neural Networks in Medical Image Analysis: A Review Ibomoiye Domor Mienye, Theo G. Swart, George Obaido et al. Information	119
7	Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine Matthew G. Hanna, Liron Pantanowitz, Rajesh Dash et al. Modern Pathology	119
8	Cellpose-SAM: superhuman generalization for cellular segmentation Marius Pachitariu, Michael Rariden, Carsen Stringer bioRxiv (Cold Spring Harbor Laboratory)	117
9	The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand et al. Health Science Reports	116
10	Current AI technologies in cancer diagnostics and treatment Ashutosh Tiwari, Soumya Nandan Mishra, Tsung‐Rong Kuo Molecular Cancer	108
11	UltraLight VM-UNet: Parallel Vision Mamba significantly reduces parameters for skin lesion segmentation Renkai Wu, Yinghao Liu, Guochen Ning et al. Patterns	106
12	A robust deep learning framework for multiclass skin cancer classification Burhanettin Özdemir, İshak Paçal Scientific Reports	93
13	Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study Veronica Hernström, Viktoria Josefsson, Hanna Sartor et al. The Lancet Digital Health	92
14	AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis Esther Ugo Alum Discover Oncology	85
15	DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier Abdulrahman Temsah, Khalid Alhasan, Ibraheem Altamimi et al. Cureus	84
16	Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein et al. Nature Cancer	82
17	U-Net V2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation Yaopeng Peng, Danny Z. Chen, Milan Sonka —	72
18	DCSSGA-UNet: Biomedical image segmentation with DenseNet channel spatial and Semantic Guidance Attention Tahir Hussain, Hayaru Shouno, Mazin Abed Mohammed et al. Knowledge-Based Systems	69
19	Multimodal generative AI for medical image interpretation Vishwanatha M. Rao, Michael Hla, Michael Moor et al. Nature	67
20	EFFResNet-ViT: A Fusion-Based Convolutional and Vision Transformer Model for Explainable Medical Image Classification Tahir Hussain, Hayaru Shouno, Arshad Hussain et al. IEEE Access	65
21	SAM-Med3D: Towards General-Purpose Segmentation Models for Volumetric Medical Images Haoyu Wang, Sizheng Guo, Ye Jin et al. Lecture notes in computer science	65
22	Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab et al. Computers in Biology and Medicine	64
23	Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings Rahul Haripriya, Nilay Khare, Manish Pandey Scientific Reports	58
24	Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods Yuxiao Gao, Yang Jiang, Yanhong Peng et al. Tomography	58
25	A systematic review of intermediate fusion in multimodal deep learning for biomedical applications Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso et al. Image and Vision Computing	57
26	The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions Prasanna Sakthi Aravazhi, Padmini Gunasekaran, N. Benjamin et al. Disease-a-Month	55
27	Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations Sarah A. Mess, Alison Mackey, David Yarowsky Plastic & Reconstructive Surgery Global Open	54
28	Breast cancer classification based on hybrid CNN with LSTM model Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey et al. Scientific Reports	53
29	A clinical benchmark of public self-supervised pathology foundation models Gabriele Campanella, Shengjia Chen, Manbir Singh et al. Nature Communications	53
30	A multimodal vision foundation model for clinical dermatology Siyuan Yan, Zhen Yu, Clare Primiero et al. Nature Medicine	52
31	Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications Daan Schouten, Giulia Nicoletti, Bas Dille et al. Medical Image Analysis	52
32	A novel hybrid ConvNeXt-based approach for enhanced skin lesion classification İbrahim Aruk, İshak Paçal, Ahmet Nusret Toprak Expert Systems with Applications	51
33	Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues Muhammad Ali, Viviana Benfante, Ghazal Basirinia et al. Journal of Imaging	50
34	The generative era of medical AI L John Fahrner, Emma Chen, Eric J. Topol et al. Cell	50
35	A dual-branch network for ultrasound image segmentation Zhiqin Zhu, Zimeng Zhang, Guanqiu Qi et al. Biomedical Signal Processing and Control	50
36	Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study Yun‐Woo Chang, Jung Kyu Ryu, Jin Kyung An et al. Nature Communications	49
37	Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model Haifan Gong, Luoyao Kang, Yitao Wang et al. —	49
38	A visual–omics foundation model to bridge histopathology with spatial transcriptomics Weiqing Chen, Pengzhi Zhang, Tu Tran et al. Nature Methods	48
39	Hybrid deep learning model for automated colorectal cancer detection using local and global feature extraction İshak Paçal, Omneya Attallah Knowledge-Based Systems	48
40	Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery Eren Öğüt Clinics and Practice	48
41	Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha et al. Scientific Reports	48
42	Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models Shokofeh Anari, Soroush Sadeghi, Ghazaal Sheikhi et al. Scientific Reports	47
43	Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data Chaoyi Wu, Xiaoman Zhang, Ya Zhang et al. Nature Communications	46
44	InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis İshak Paçal, Omneya Attallah Biomedical Signal Processing and Control	46
45	AI model using clinical images for genomic prediction and tailored treatment in patients with cancer Song‐Bin Guo, Xiuyu Cai, Meng Yuan et al. The Lancet Oncology	46
46	Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation Shenhai Zheng, Xin Ye, Chaohui Yang et al. IEEE Transactions on Medical Imaging	46
47	Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives Antonio Marra, Stefania Morganti, Fresia Pareja et al. Annals of Oncology	44
48	An amalgamation of deep neural networks optimized with Salp swarm algorithm for cervical cancer detection Omair Bilal, Sohaib Asif, Ming Zhao et al. Computers & Electrical Engineering	44
49	An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification Madallah Alruwaili, Mahmood Mohamed Diagnostics	44
50	Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis Chaoyou Fu, Yuhan Dai, Yongdong Luo et al. —	43

Top Papers: KI in der Krebserkennung (2025)

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