Top Papers: Machine Learning im Gesundheitswesen (2025)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2025 (von 5.663 insgesamt).

Machine Learning verändert das Gesundheitswesen grundlegend – von der Vorhersage von Krankheitsverläufen über die Optimierung von Behandlungspfaden bis hin zur Identifikation von Risikogruppen. Klinische Daten, Laborwerte und Bildgebungsdaten werden mit ML-Modellen ausgewertet, um Entscheidungen schneller und fundierter zu treffen. Diese Seite bündelt die relevantesten Studien und ihre Ergebnisse.

2023 2024 2025 2026

#	Paper	Zitationen
1	Toward expert-level medical question answering with large language models K. K. Singhal, Tao Tu, Juraj Gottweis et al. Nature Medicine	615
2	The TRIPOD-LLM reporting guideline for studies using large language models Jack Gallifant, Majid Afshar, Saleem Ameen et al. Nature Medicine	279
3	PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul et al. BMJ	252
4	Towards conversational diagnostic artificial intelligence Tao Tu, Mike Schaekermann, Anil Palepu et al. Nature	203
5	Current applications and challenges in large language models for patient care: a systematic review Felix Busch, Lena Hoffmann, Christopher Rueger et al. Communications Medicine	174
6	Towards accurate differential diagnosis with large language models Daniel McDuff, Mike Schaekermann, Tao Tu et al. Nature	141
7	Transforming Drug Therapy with Deep Learning: The Future of Personalized Medicine Altaf O. Mulani, Minal Deshmukh, Vaishali Jadhav et al. Drug Research	132
8	Benchmark evaluation of DeepSeek large language models in clinical decision-making Sarah Sandmann, Stefan Hegselmann, Michael Fujarski et al. Nature Medicine	131
9	An evaluation framework for clinical use of large language models in patient interaction tasks Shreya Johri, Jae‐Hwan Jeong, Benjamin A. Tran et al. Nature Medicine	131
10	The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions Razan Alkhanbouli, Hour Matar Abdulla Almadhaani, Farah Alhosani et al. BMC Medical Informatics and Decision Making	124
11	A generalist medical language model for disease diagnosis assistance Xiaohong Liu, Hao Liu, Guoxing Yang et al. Nature Medicine	124
12	🧜Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models Yue Zhang, Yafu Li, Leyang Cui et al. Computational Linguistics	124
13	A Review of Large Language Models in Medical Education, Clinical Decision Support, and Healthcare Administration Josip Vrdoljak, Zvonimir Boban, Marino Vilović et al. Healthcare	104
14	A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians Hirotaka Takita, Daijiro Kabata, Shannon L. Walston et al. npj Digital Medicine	101
15	Benchmarking large language models for biomedical natural language processing applications and recommendations Qingyu Chen, Yan Hu, Xueqing Peng et al. Nature Communications	101
16	A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics Kai He, Rui Mao, Qika Lin et al. Information Fusion	100
17	Retrieval augmented generation for large language models in healthcare: A systematic review Lameck Mbangula Amugongo, Pietro Mascheroni, Steven E. Brooks et al. PLOS Digital Health	99
18	Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications Pablo Cruz-Gonzalez, Anxun He, Eva K. M. Lam et al. Psychological Medicine	97
19	Large Language Models in Healthcare and Medical Applications: A Review Subhankar Maity, Manob Jyoti Saikia Bioengineering	94
20	Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human Daniel J. Mathew, Deborah Ebem, Anayo Chukwu Ikegwu et al. Neural Processing Letters	93
21	Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines Siru Liu, Allison B. McCoy, Adam Wright Journal of the American Medical Informatics Association	90
22	Core GRADE 1: overview of the Core GRADE approach Gordon Guyatt, Thomas Agoritsas, Romina Brignardello‐Petersen et al. BMJ	82
23	Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness Yu He Ke, Liyuan Jin, Kabilan Elangovan et al. npj Digital Medicine	79
24	The rise of agentic AI teammates in medicine James Zou, Eric J. Topol The Lancet	78
25	Next-generation agentic AI for transforming healthcare Nalan Karunanayake Informatics and Health	74
26	The integration of AI in nursing: addressing current applications, challenges, and future directions Qiuying Wei, Songcheng Pan, Xiaoyu Liu et al. Frontiers in Medicine	74
27	Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency Soumitra S. Bhuyan, Vidyoth Sateesh, Naya Meenkashi Mukul et al. Journal of Medical Systems	73
28	Large Language Models in Medicine: Applications, Challenges, and Future Directions Erlan Yu, Xuehong Chu, Wanwan Zhang et al. International Journal of Medical Sciences	73
29	Retrieval-augmented generation for generative artificial intelligence in health care Rui Yang, Yilin Ning, Emilia Keppo et al. npj Health Systems	73
30	Evaluating large language model workflows in clinical decision support for triage and referral and diagnosis Farieda Gaber, Maqsood Shaik, Fabio Allega et al. npj Digital Medicine	69
31	Artificial intelligence-assisted academic writing: recommendations for ethical use Adam Cheng, Aaron W. Calhoun, Gabriel Reedy Advances in Simulation	67
32	Large Language Models lack essential metacognition for reliable medical reasoning Maxime Griot, Coralie Hemptinne, Jean Vanderdonckt et al. Nature Communications	67
33	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	66
34	Delving into LLM-assisted writing in biomedical publications through excess vocabulary Dmitry Kobak, Rita González-Márquez, Emőke-Ágnes Horvát et al. Science Advances	65
35	Artificial intelligence in nursing: an integrative review of clinical and operational impacts Salwa Hassanein, Rabie Adel El Arab, Amany Abdrbo et al. Frontiers in Digital Health	64
36	MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot Xuejiao Zhao, Siyan Liu, Su-Yin Yang et al. —	62
37	Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals Paige Nong, Julia Adler‐Milstein, Nate C. Apathy et al. Health Affairs	61
38	Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings Rahul Haripriya, Nilay Khare, Manish Pandey Scientific Reports	60
39	Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare Nargish Parvin, Sang‐Woo Joo, Jae Hak Jung et al. Nanomaterials	59
40	Medical digital twins: enabling precision medicine and medical artificial intelligence Christoph Sadée, Stefano Testa, Thomas Barba et al. The Lancet Digital Health	59
41	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	56
42	Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota T. Rozera, Edoardo Pasolli, Nicola Segata et al. Gastroenterology	55
43	Application of large language models in medicine Fenglin Liu, Hongjian Zhou, 博司熊谷 et al. Nature Reviews Bioengineering	54
44	The Large Language Models on Biomedical Data Analysis: A Survey Wei Lan, Zhentao Tang, Mingyang Liu et al. IEEE Journal of Biomedical and Health Informatics	53
45	Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring Fang Li, Shengguo Wang, Zhi Gao et al. Frontiers in Medicine	51
46	Privacy preserving strategies for electronic health records in the era of large language models Jitendra Jonnagaddala, Zoie Shui-Yee Wong npj Digital Medicine	51
47	Evaluating and addressing demographic disparities in medical large language models: a systematic review Mahmud Omar, Vera Sorin, Reem Agbareia et al. International Journal for Equity in Health	51
48	Evolution of artificial intelligence in healthcare: a 30-year bibliometric study Yiweng Xie, Yuansheng Zhai, Guihua Lu Frontiers in Medicine	48
49	A comprehensive review on financial explainable AI Wei Jie Yeo, Wihan van der Heever, Rui Mao et al. Artificial Intelligence Review	48
50	Medical foundation large language models for comprehensive text analysis and beyond Qianqian Xie, Qingyu Chen, Aokun Chen et al. npj Digital Medicine	47

Top Papers: Machine Learning im Gesundheitswesen (2025)

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