NEJM AI 2024 — papers
The works of NEJM AI published in 2024 that our library holds: 1 work, listed below with the most cited first; each has a page with our summary, its key points and its references.
Most cited papers
- Towards Generalist Biomedical AI ↗Tao Tu, Shekoofeh Azizi, Danny Driess et al.452 citations
- Almanac — Retrieval-Augmented Language Models for Clinical Medicine ↗Cyril Zakka, Rohan Shad, Akash R. Chaurasia et al.405 citations
- A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs ↗Sheng Zhang, Yanbo Xu, Naoto Usuyama et al.270 citations
- Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical AnalysisWeixin Liang, Yuhui Zhang, Hancheng Cao et al.243 citations · our summary
- Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying ↗Ali Soroush, Benjamin Scott Glicksberg, Eyal Zimlichman et al.136 citations
- GPT versus Resident Physicians — A Benchmark Based on Official Board Scores ↗Uriel Katz, Eran Cohen, Eliya K. Shachar et al.134 citations
- Retrieval-Augmented Generation–Enabled GPT-4 for Clinical Trial Screening ↗Ozan Ünlü, Jiyeon Shin, Charlotte J. Mailly et al.114 citations
- Comparative Evaluation of LLMs in Clinical Oncology ↗Nicholas Robert Rydzewski, Deepak Dinakaran, Shuang George Zhao et al.106 citations
- RAG in Health Care: A Novel Framework for Improving Communication and Decision-Making by Addressing LLM Limitations ↗Karen Ka Yan Ng, Izuki Matsuba, Peter Chengming Zhang103 citations
- Benchmarking Open-Source Large Language Models, GPT-4 and Claude 2 on Multiple-Choice Questions in Nephrology ↗Sean Wu, Michael Koo, Lesley Blum et al.97 citations
All works of the year (98)
- Towards Generalist Biomedical AI ↗Tao Tu, Shekoofeh Azizi, Danny Driess et al.452 citations
- Almanac — Retrieval-Augmented Language Models for Clinical Medicine ↗Cyril Zakka, Rohan Shad, Akash R. Chaurasia et al.405 citations
- A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs ↗Sheng Zhang, Yanbo Xu, Naoto Usuyama et al.270 citations
- Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical AnalysisWeixin Liang, Yuhui Zhang, Hancheng Cao et al.243 citations · our summary
- Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying ↗Ali Soroush, Benjamin Scott Glicksberg, Eyal Zimlichman et al.136 citations
- GPT versus Resident Physicians — A Benchmark Based on Official Board Scores ↗Uriel Katz, Eran Cohen, Eliya K. Shachar et al.134 citations
- Retrieval-Augmented Generation–Enabled GPT-4 for Clinical Trial Screening ↗Ozan Ünlü, Jiyeon Shin, Charlotte J. Mailly et al.114 citations
- Comparative Evaluation of LLMs in Clinical Oncology ↗Nicholas Robert Rydzewski, Deepak Dinakaran, Shuang George Zhao et al.106 citations
- RAG in Health Care: A Novel Framework for Improving Communication and Decision-Making by Addressing LLM Limitations ↗Karen Ka Yan Ng, Izuki Matsuba, Peter Chengming Zhang103 citations
- Benchmarking Open-Source Large Language Models, GPT-4 and Claude 2 on Multiple-Choice Questions in Nephrology ↗Sean Wu, Michael Koo, Lesley Blum et al.97 citations
- GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines ↗Dyke Ferber, Isabella C. Wiest, Georg Wölflein et al.90 citations
- Using ChatGPT to Facilitate Truly Informed Medical Consent ↗Fatima Nadeem Mirza, Oliver Young Tang, Ian David Connolly et al.88 citations
- Development and Validation of a Multimodal Multitask Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence ↗Jianing Qiu, Jian Wu, Hao Wei et al.79 citations
- Evaluation of Sepsis Prediction Models before Onset of Treatment ↗Fahad Kamran, Donna Tjandra, Andrew Heiler et al.73 citations
- FDA-Authorized AI/ML Tool for Sepsis Prediction: Development and Validation ↗Akhil Bhargava, Carlos G. Lopez-Espina, Lee Schmalz et al.72 citations
- A Call for Artificial Intelligence Implementation Science Centers to Evaluate Clinical Effectiveness ↗Chris Longhurst, Karandeep Singh, Aneesh Chopra et al.66 citations
- CORAL: Expert-Curated Oncology Reports to Advance Language Model Inference ↗Madhumita Sushil, Vanessa Elaine Kennedy, Divneet Mandair et al.61 citations
- To Do No Harm — and the Most Good — with AI in Health Care ↗Carey Beth Goldberg, Laura L. Adams, David Blumenthal et al.61 citations
- Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Study ↗Tsai‐Ling Liu, Timothy C. Hetherington, Ajay Dharod et al.60 citations
- Medical Ethics of Large Language Models in Medicine ↗Jasmine Chiat Ling Ong, Yin‐Hsi Chang, William Wasswa et al.58 citations
- Zero-Shot Clinical Trial Patient Matching with LLMs ↗Michael Wornow, Alejandro Lozano, Dev Dash et al.57 citations
- Large Language Models for More Efficient Reporting of Hospital Quality Measures ↗Aaron E. Boussina, Rishivardhan Krishnamoorthy, Kimberly Quintero et al.55 citations
- Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial ↗Chin S. Lin, Wei-Ting Liu, Chiao‐Hsiang Chang et al.54 citations
- The Regulation of Clinical Artificial Intelligence ↗David Blumenthal, Bakul Patel54 citations
- AI-MARRVEL — A Knowledge-Driven AI System for Diagnosing Mendelian Disorders ↗Dongxue Mao, Chaozhong Liu, Linhua Wang et al.50 citations
See all 98 papers in NEJM AI 2024 — papers →
Works with our summary in our library (1)
- Autonomous LLM-driven research from data to human-verifiable research papersNEJM AITal Ifargan, Lukas Hafner, Maor Kern, et al.2024 · 57 citations
Top topics
Language models & pretraining (1)
Fields
Arts and Humanities (1)Computer Science (1)
Top institutions
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