{"total":35,"rows":[{"id":"2310.05694","title":"A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics","created":"2023-10-09","cited_by_count":163,"area":null},{"id":"2406.12036","title":"MedCalc-Bench: Evaluating Large Language Models for Medical Calculations","created":"2024-06-17","cited_by_count":70,"area":null},{"id":"2505.23802","title":"MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks","created":"2025-05-26","cited_by_count":64,"area":null},{"id":"2406.07835","title":"SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature","created":"2024-06-10","cited_by_count":41,"area":null},{"id":"2311.11608","title":"Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks","created":"2023-11-20","cited_by_count":36,"area":null},{"id":"2303.12961","title":"The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs","created":"2023-03-22","cited_by_count":29,"area":null},{"id":"2311.12410","title":"nach0: Multimodal Natural and Chemical Languages Foundation Model","created":"2023-11-21","cited_by_count":26,"area":null},{"id":"2311.00287","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","created":"2023-11-01","cited_by_count":20,"area":null},{"id":"2410.21348","title":"Large Language Model Benchmarks in Medical Tasks","created":"2024-10-28","cited_by_count":17,"area":null},{"id":"2405.01249","title":"Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices","created":"2024-05-02","cited_by_count":14,"area":null},{"id":"2510.15710","title":"UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis","created":"2025-10-17","cited_by_count":12,"area":null},{"id":"2210.11913","title":"NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities","created":"2022-10-21","cited_by_count":10,"area":null},{"id":"2211.14838","title":"PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets","created":"2022-11-27","cited_by_count":10,"area":null},{"id":"2402.12372","title":"HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2310.10920","title":"NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain","created":"2023-10-17","cited_by_count":7,"area":null},{"id":"2408.01460","title":"LocalValueBench: A Collaboratively Built and Extensible Benchmark for Evaluating Localized Value Alignment and Ethical Safety in Large Language Models","created":"2024-07-27","cited_by_count":7,"area":null},{"id":"2408.14568","title":"Improving Clinical Note Generation from Complex Doctor-Patient Conversation","created":"2024-08-26","cited_by_count":7,"area":null},{"id":"2310.00160","title":"Self-Specialization: Uncovering Latent Expertise within Large Language Models","created":"2023-09-29","cited_by_count":5,"area":null},{"id":"2310.08885","title":"InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems","created":"2023-10-13","cited_by_count":5,"area":null},{"id":"2304.01344","title":"End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies","created":"2023-04-03","cited_by_count":4,"area":null},{"id":"2307.13962","title":"Understanding Deep Neural Networks via Linear Separability of Hidden Layers","created":"2023-07-26","cited_by_count":4,"area":null},{"id":"2503.20846","title":"Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2310.11275","title":"xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization","created":"2023-10-17","cited_by_count":3,"area":null},{"id":"2311.12537","title":"Oasis: Data Curation and Assessment System for Pretraining of Large Language Models","created":"2023-11-21","cited_by_count":3,"area":null},{"id":"2510.24551","title":"Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives","created":"2025-10-28","cited_by_count":3,"area":null},{"id":"2303.15846","title":"Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes","created":"2023-03-28","cited_by_count":2,"area":null},{"id":"2311.12474","title":"CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews","created":"2023-11-21","cited_by_count":2,"area":null},{"id":"2505.00814","title":"Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction","created":"2025-05-01","cited_by_count":2,"area":null},{"id":"2211.07713","title":"How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2309.01669","title":"Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2309.01812","title":"Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2310.20440","title":"Integrating curation into scientific publishing to train AI models","created":"2023-10-31","cited_by_count":1,"area":null},{"id":"2503.15924","title":"Towards Automatic Continual Learning: A Self-Adaptive Framework for Continual Instruction Tuning","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2506.02451","title":"Weak Supervision for Real World Graphs","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2606.03250","title":"The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP","created":"2026-06-02","cited_by_count":0,"area":null}],"next_cursor":null}