Top Papers: Machine Learning im Gesundheitswesen (2024)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2024 (von 7.290 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.

2022 2023 2024 2025

#	Paper	Zitationen
1	Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation Anjanava Biswas, Wrick Talukdar International Journal of Innovative Science and Research Technology (IJISRT)	1.012
2	Adapted large language models can outperform medical experts in clinical text summarization Dave Van Veen, Cara Van Uden, Louis Blankemeier et al. Nature Medicine	591
3	A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo et al. Advanced Intelligent Systems	522
4	Detecting hallucinations in large language models using semantic entropy Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn et al. Nature	507
5	Explainability for Large Language Models: A Survey Haiyan Zhao, Hanjie Chen, Fan Yang et al. ACM Transactions on Intelligent Systems and Technology	490
6	Evaluation and mitigation of the limitations of large language models in clinical decision-making Paul Hager, Friederike Jungmann, Robbie Holland et al. Nature Medicine	472
7	Practical guide to <scp>SHAP</scp> analysis: Explaining supervised machine learning model predictions in drug development Ana Victoria Ponce Bobadilla, Vanessa Schmitt, Corinna S. Maier et al. Clinical and Translational Science	452
8	Evaluation of clinical prediction models (part 1): from development to external validation Gary S. Collins, Paula Dhiman, Jie Ma et al. BMJ	443
9	Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions Luca Longo, Mario Brčić, Federico Cabitza et al. Information Fusion	419
10	Enhancing mental health with Artificial Intelligence: Current trends and future prospects David B. Olawade, Ojima Z. Wada, Aderonke Odetayo et al. Journal of Medicine Surgery and Public Health	376
11	A review of Explainable Artificial Intelligence in healthcare Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Çifçi et al. Computers & Electrical Engineering	349
12	Towards Generalist Biomedical AI Tao Tu, Shekoofeh Azizi, Danny Driess et al. NEJM AI	328
13	A multimodal generative AI copilot for human pathology Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al. Nature	318
14	TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods BMJ	316
15	ChatGPT in healthcare: A taxonomy and systematic review Jianning Li, Amin Dada, Behrus Puladi et al. Computer Methods and Programs in Biomedicine	309
16	Almanac — Retrieval-Augmented Language Models for Clinical Medicine Cyril Zakka, Rohan Shad, Akash Chaurasia et al. NEJM AI	306
17	Large Language Models in Healthcare and Medical Domain: A Review Zabir Al Nazi, Wei Peng Informatics	297
18	The application of large language models in medicine: A scoping review Xiangbin Meng, Xiangyu Yan, Kuo Zhang et al. iScience	296
19	Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection Vimbi Viswan, Noushath Shaffi, Mufti Mahmud Brain Informatics	288
20	Prompt Engineering in Large Language Models Ggaliwango Marvin, Nakayiza Hellen, Daudi Jjingo et al. Algorithms for intelligent systems	287
21	Large Language Models in Medicine: The Potentials and Pitfalls Jesutofunmi A. Omiye, Haiwen Gui, Shawheen J. Rezaei et al. Annals of Internal Medicine	280
22	Generative artificial intelligence: a systematic review and applications Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar et al. Multimedia Tools and Applications	279
23	Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker et al. IEEE Access	266
24	The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs) Joschka Haltaufderheide, Robert Ranisch npj Digital Medicine	261
25	Improving large language models for clinical named entity recognition via prompt engineering Yan Hu, Qingyu Chen, Jingcheng Du et al. Journal of the American Medical Informatics Association	258
26	An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review Rosita Guido, Stefania Ferrisi, Danilo Lofaro et al. Information	257
27	Developing clinical prediction models: a step-by-step guide Orestis Efthimiou, Michael Seo, Konstantina Chalkou et al. BMJ	255
28	Ethical and regulatory challenges of large language models in medicine Jasmine Chiat Ling Ong, Yin‐Hsi Chang, William Wasswa et al. The Lancet Digital Health	243
29	PMC-LLaMA: toward building open-source language models for medicine Chaoyi Wu, Weixiong Lin, Xiaoman Zhang et al. Journal of the American Medical Informatics Association	242
30	Can large language models reason about medical questions? Valentin Liévin, Christoffer Hother, Andreas Geert Motzfeldt et al. Patterns	237
31	Evaluation of clinical prediction models (part 2): how to undertake an external validation study Richard D Riley, Lucinda Archer, Kym I E Snell et al. BMJ	232
32	A critical assessment of using ChatGPT for extracting structured data from clinical notes Jingwei Huang, Donghan M. Yang, Ruichen Rong et al. npj Digital Medicine	221
33	Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine Thomas Savage, Ashwin Nayak, Robert Gallo et al. npj Digital Medicine	209
34	Illusory generalizability of clinical prediction models Adam M. Chekroud, Matt Hawrilenko, Hieronimus Loho et al. Science	206
35	Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook Rawan AlSaad, Alaa Abd‐Alrazaq, Sabri Boughorbel et al. Journal of Medical Internet Research	205
36	Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service Sarina Aminizadeh, Arash Heidari, Mahshid Dehghan et al. Artificial Intelligence in Medicine	204
37	Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations Siva Sai, Aanchal Gaur, R Vijay Sai et al. IEEE Access	202
38	A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges Hussain A. Younis, Hussain A. Younis, Taiseer Abdalla Elfadil Eisa et al. Diagnostics	201
39	AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential Malek Elhaddad, Sara Hamam Cureus	198
40	Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao et al. IEEE Access	189
41	Artificial intelligence in positive mental health: a narrative review Anoushka Thakkar, Ankita Gupta, Avinash De Sousa Frontiers in Digital Health	189
42	BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains Yanis Labrak, Adrien Bazoge, Emmanuel Morin et al. —	185
43	The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives Marco Cascella, Federico Semeraro, Jonathan Montomoli et al. Journal of Medical Systems	185
44	A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis Xi Xu, Jianqiang Li, Zhichao Zhu et al. Bioengineering	181
45	Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks Sarah Sandmann, Sarah Riepenhausen, Lucas Plagwitz et al. Nature Communications	177
46	Matching patients to clinical trials with large language models Qiao Jin, Zifeng Wang, Charalampos S. Floudas et al. Nature Communications	176
47	Synthetic data generation methods in healthcare: A review on open-source tools and methods Vasileios C. Pezoulas, Dimitrios I. Zaridis, Eugenia Mylona et al. Computational and Structural Biotechnology Journal	173
48	Large language models in medical and healthcare fields: applications, advances, and challenges Dandan Wang, Shiqing Zhang Artificial Intelligence Review	170
49	Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework Simone Kresevic, Mauro Giuffrè, Miloš Ajčević et al. npj Digital Medicine	166
50	Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI Mahyar Abbasian, Elahe Khatibi, Iman Azimi et al. npj Digital Medicine	160

Top Papers: Machine Learning im Gesundheitswesen (2024)

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