Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.
Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院）；NAVER CLOVA AI Lab（NAVER CLOVA AI实验室）
We propose Medical Entity Definition-based Sentence Embedding (MED-SE), a novel unsupervised contrastive learning framework designed for clinical texts, which exploits the definitions of medical entities. To this end, we conduct an extensive analysis of multiple sentence embedding techniques in clinical semantic textual similarity (STS) settings. In the entity-centric setting that we have designed, MED-SE achieves significantly better performance, while the existing unsupervised methods including SimCSE show degraded performance. Our experiments elucidate the inherent discrepancies between the…
Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis
Penn State University（宾夕法尼亚州立大学）；Mississippi State University（密西西比州立大学）；Northwest Missouri State University（西北密苏里州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Colorado State University（科罗拉多州立大学）
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm（法国国家健康与医学研究院）；Pixyl Research and Development Laboratory（Pixyl研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associate…
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
College of Computer Science, Zhejiang University（浙江大学计算机科学与技术学院）；School of Medicine, Zhejiang University（浙江大学医学院）；Department of Biomedical Informatics, Harvard University（哈佛大学生物医学信息学系）
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
The Hong Kong Polytechnic University（香港理工大学）；Hong Kong University（香港大学）；South China University of Technology（华南理工大学）；University of Toronto（多伦多大学）；Peking Union Medical College Hospital（北京地坛医院）；West China Hospital, Sichuan University（四川大学华西医院）
Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes
Department of Computer Science（计算机科学系）；Emory University（埃默里大学）；College of Public Health（公共卫生学院）；Temple University（temple大学）；School of Information（信息学院）；Florida State University（佛罗里达州立大学）；Hubert Department of Global Health（全球健康部）；Nell Hodgson Woodruff School of Nursing（Nell Hodgson Woodruff护理学院）
University of Trier（特里尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University Hospital Münster（明斯特大学医院）；Heinrich Heine University（海因里希·海涅大学）
Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another cha…
With a neural sequence generation model, this study aims to develop a method of writing the patient clinical texts given a brief medical history. As a proof-of-a-concept, we have demonstrated that it can be workable to use medical concept embedding in clinical text generation. Our model was based on the Sequence-to-Sequence architecture and trained with a large set of de-identified clinical text data. The quantitative result shows that our concept embedding method decreased the perplexity of the baseline architecture. Also, we discuss the analyzed results from a human evaluation performed by m…
The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Hunan University（湖南大学）；ByteDance（字节跳动）；Duke University（杜克大学）；Westlake University（西湖大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Macau（澳门大学）；The Ohio State University（俄亥俄州立大学）
Elsevier, Health and Commercial Markets（爱思唯尔，健康与商业市场部）
Clinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.
Artificial Intelligence (AI) and Machine-Learning (ML) models have been increasingly used in medical products, such as medical device software. General considerations on the statistical aspects for the evaluation of AI/ML-enabled medical diagnostic devices are discussed in this paper. We also provide relevant academic references and note good practices in addressing various statistical challenges in the clinical validation of AI/ML-enabled medical devices in the context of their intended use.
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）；School of Natural and Computing Science, University of Aberdeen, UK（阿伯丁大学自然与计算科学学院）
King Mongkut’s University of Technology North Bangkok（曼谷北部金蒙固科技大学）；Suan Sunandha Rajabhat University（素安塔尼皇家大学）
This paper discusses the knowledge integration of clinical information extracted from distributed medical ontology in order to ameliorate a machine learning-based multi-label coding assignment system. The proposed approach is implemented using a decision tree based cascade hierarchical technique on the university hospital data for patients with Coronary Heart Disease (CHD). The preliminary results obtained show a satisfactory finding.
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties
Objective: Effective collaboration between machines and clinicians requires flexible data structures to represent medical processes and clinical practice guidelines. Such a data structure could enable effective turn-taking between human and automated components of a complex treatment, accurate on-line monitoring of clinical treatments (for example to detect medical errors), or automated treatment systems (such as future medical robots) whose overall treatment plan is understandable and auditable by human experts. Materials and Methods: Behavior trees (BTs) emerged from video game development a…
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
The University of New South Wales（新南威尔士大学）；University of Göttingen（哥廷根大学）；Macquarie University（麦考瑞大学）；Beijing Intelligent Decision Medical Technology Co. Ltd（北京智决医疗科技有限公司）
Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do not take into consideration expert domain knowledge. In this work, we introduced UmlsBERT, a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. More specifically, the augmentation on UmlsBERT with the Unified Medical Language System (UMLS) Metathesaurus was perfor…
Simon Fraser University（西蒙弗雷泽大学）；The University of British Columbia（不列颠哥伦比亚大学）
Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. Applying XAI in clinical settings requires proper evaluation criteria to ensure the explanation technique is both technically sound and clinically useful, but specific support is lacking to achieve this goal. To bridge the research gap, we propose the Clinical XAI Guidelines that consist of five criteria a clinical XAI needs to be optimized for. The guidelines recommend choosing an explanation form based on Guideline 1 (G1)…
Medical Reasoning with Large Language Models: A Survey and MR-Bench
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）；National University of Singapore（新加坡国立大学）
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
College of Computer Science and Artificial Intelligence, Fudan University（计算机科学与人工智能学院，复旦大学）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；Shanghai AI Laboratory（上海人工智能实验室）；Northwestern University（西北大学）
Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University of Toronto（多伦多大学）；Peter Munk Cardiac Centre, University Health Network（大学健康网络彼得·蒙克心脏中心）；McGill University（麦吉尔大学）；University Health Network（大学健康网络）
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations …
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction, and reasoning with remarkable flexibility and efficiency. These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, mon…
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University（清华大学临床医学院北京清华长庚医院肝胆胰中心）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；College of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）
Nanjing Medical University（南京医科大学）；the First Affiliated Hospital（第一附属医院）
Event extraction is an important work of medical text processing. According to the complex characteristics of medical text annotation, we use the end-to-end event extraction model to enhance the output formatting information of events. Through pre training and fine-tuning, we can extract the attributes of the four dimensions of medical text: anatomical position, subject word, description word and occurrence state. On the test set, the accuracy rate was 0.4511, the recall rate was 0.3928, and the F1 value was 0.42. The method of this model is simple, and it has won the second place in the task …
