Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 5.3. Future Perspectives in the Era of Large Language Models Here, we summarize six perspectives that can enhance the community challenges in biomedical text mining in the era of large language models. Comprehensive biomedical evaluation benchmark Comprehensive biomedical evaluation benchmark With the emergence of large language models such as ChatGPT and its successors, there is an urgent need for comprehensive evaluation of their capability in biomedical text mining or healthcare information processing. These capabilities should include medical information retrieval, medical language understanding, medical text generation, medical knowledge question answering, clinical decision reasoning, and more. To address this, evaluation benchmarks like PromptCBLUE [50, 59] , CMB [72] and MedBench [73] have been proposed. Handling multi-source and multimodal data Handling multi-source and multimodal data Biomedical text data encompasses a wide range of sources, including published literatures, electronic medical records, textbooks, and social media, etc. Additionally, Biomedical data exist in various modalities such as images, videos, and audio. Utilizing these diverse sources and multimodal data can provide more comprehensive information for diagnosis, treatment, and research [39]. Therefore, evaluating the model's ability to integrate multiple sources and process multimodal data is crucial in future community challenges. This may involve tasks such as cross-modal information fusion, cross-modal querying and retrieval. Leveraging domain-specific knowledge Leveraging domain-specific knowledge The field of biomedicine has a large number of software, tools, and knowledge bases. Integrating their functionalities into large language models can greatly enhance the models' capabilities. For example, the model can automatically invoke appropriate software and tools to parse, annotate, and analyze biomedical data [74]. Moreover, the model can extract relevant information from knowledge bases to generate more accurate responses and reduce erroneous information. Data privacy and security Data privacy and security The collection and sharing of biomedical data raise concerns about data privacy and security [75]. It is necessary to consider how to protect sensitive information while performing text mining and information processing using large language models. Future community challenges should include evaluation tasks related to data privacy and security to ensure responsible development and deployment of these models. Interpretability Interpretability Interpreting and understanding the predictions of models is crucial in clinical practice. Reliable explanations and evidence-based support are necessary for clinical decision-making. However, evaluating the interpretability of models represents a significant challenge. This may involve explaining the decision-making process of the model, providing reliable evidence and explanations for the predictions, and effectively interacting with clinical experts to ensure that model’s predictions are correctly understood or adjusted based on their knowledge and experience. Integration with translational applications Integration with translational applications Applying large language models to clinical practice is an important goal. Evaluating the model's ability to integrate with clinical translational applications is a key challenge. This may involve integrating the model into medical knowledge bases, hospital information systems, clinical decision support systems, or other clinical workflows. By doing so, the translation of research findings into practical healthcare solutions can be accelerated. Acknowledgements Acknowledgements We thank the community challenges for providing an open platform, the organizers of evaluation tasks for defining tasks and providing datasets, and the participants of the tasks for developing algorithms or systems. Author contributions Author contributions HZ and RW performed data collection, investigation, and analysis. HZ drafted the manuscript. RW revised the manuscript. JC, EW, JL and LT provided constructive suggestion and technique assistance; BS obtained the funding; ZL, BT and BS supervised the study. All authors have read and agreed to the published version of the manuscript. Competing interests Competing interests The authors declare no competing interests. Data availability Data availability No datasets were created in this study. The datasets in evaluation tasks of community challenges can be found in related websites or papers. 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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.
