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Bae H, Park SY, Kim CE. A practical guide to implementing artificial intelligence in traditional East Asian medicine research. Integr Med Res 2024; 13:101067. [PMID: 39253696 PMCID: PMC11381867 DOI: 10.1016/j.imr.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/11/2024] Open
Abstract
In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sa-Yoon Park
- Department of Physiology, College of Korean Medicine, Wonkwang University, Iksan, Korea
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
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Duminuco A, Au Yeung J, Vaghela R, Virdee S, Woodley C, Asirvatham S, Curto‐Garcia N, Sriskandarajah P, O'Sullivan J, de Lavallade H, Radia D, Kordasti S, Palumbo G, Harrison C, Harrington P. Development of a natural language processing pipeline for assessment of cardiovascular risk in myeloproliferative neoplasms. Hemasphere 2024; 8:e143. [PMID: 39131900 PMCID: PMC11310405 DOI: 10.1002/hem3.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/18/2024] [Accepted: 07/16/2024] [Indexed: 08/13/2024] Open
Affiliation(s)
- Andrea Duminuco
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
- Haematology Unit with BMTA.O.U. Policlinico “G. Rodolico‐San Marco”CataniaItaly
| | | | - Raj Vaghela
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Sukhraj Virdee
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Claire Woodley
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Susan Asirvatham
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | | | | | | | | | - Deepti Radia
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
| | - Shahram Kordasti
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
- School of Cancer and Pharmaceutical Science, King's College LondonLondonUK
| | - Giuseppe Palumbo
- Haematology Unit with BMTA.O.U. Policlinico “G. Rodolico‐San Marco”CataniaItaly
| | - Claire Harrison
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
- School of Cancer and Pharmaceutical Science, King's College LondonLondonUK
| | - Patrick Harrington
- Department of HaematologyGuy's and St Thomas' NHS Foundation TrustLondonUK
- School of Cancer and Pharmaceutical Science, King's College LondonLondonUK
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Kurasawa H, Waki K, Seki T, Chiba A, Fujino A, Hayashi K, Nakahara E, Haga T, Noguchi T, Ohe K. Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development. JMIR AI 2024; 3:e56700. [PMID: 39024008 PMCID: PMC11294778 DOI: 10.2196/56700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/21/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors. OBJECTIVE We sought to develop a model that accurately predicts poor glycemic control among patients with T2D receiving usual care. METHODS Our machine learning model predicts poor glycemic control (HbA1c≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7787 patients with T2D seeing specialist physicians at the University of Tokyo Hospital. The training data include instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate, to that of LightGBM. RESULTS The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate (95% confidence limits) of the proposed model were 0.925 (95% CI 0.923-0.928), 0.864 (95% CI 0.852-0.875), and 0.864 (95% CI 0.86-0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM's performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control. CONCLUSIONS The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model's indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.
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Affiliation(s)
- Hisashi Kurasawa
- Nippon Telegraph and Telephone Corporation, Tokyo, Japan
- The University of Tokyo Hospital, Tokyo, Japan
| | - Kayo Waki
- The University of Tokyo Hospital, Tokyo, Japan
| | | | - Akihiro Chiba
- Nippon Telegraph and Telephone Corporation, Tokyo, Japan
- NTT DOCOMO, Inc, Tokyo, Japan
| | - Akinori Fujino
- Nippon Telegraph and Telephone Corporation, Tokyo, Japan
| | | | - Eri Nakahara
- Nippon Telegraph and Telephone Corporation, Tokyo, Japan
- The University of Tokyo Hospital, Tokyo, Japan
| | - Tsuneyuki Haga
- Nippon Telegraph and Telephone Corporation, Tokyo, Japan
- NTT-AT IPS Corporation, Kanagawa, Japan
| | - Takashi Noguchi
- National Center for Child Health and Development, Tokyo, Japan
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Riaz IB, Harmon S, Chen Z, Naqvi SAA, Cheng L. Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes. Am Soc Clin Oncol Educ Book 2024; 44:e438516. [PMID: 38935882 DOI: 10.1200/edbk_438516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.
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Affiliation(s)
- Irbaz Bin Riaz
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic, Phoenix, AZ
- Department of AI and Informatics, Mayo Clinic, Rochester, MN
| | - Stephanie Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Zhijun Chen
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | | | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, Lifespan Health, and the Legorreta Cancer Center at Brown University, Providence, RI
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Wang A, Wu Y, Ji X, Wang X, Hu J, Zhang F, Zhang Z, Pu D, Tang L, Ma S, Liu Q, Dong J, He K, Li K, Teng D, Li T. Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for Enhancement and Assessment. JMIR Res Protoc 2024; 13:e57001. [PMID: 38788208 PMCID: PMC11161706 DOI: 10.2196/57001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/10/2024] [Accepted: 04/10/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Spondyloarthritis (SpA), a chronic inflammatory disorder, predominantly impacts the sacroiliac joints and spine, significantly escalating the risk of disability. SpA's complexity, as evidenced by its diverse clinical presentations and symptoms that often mimic other diseases, presents substantial challenges in its accurate diagnosis and differentiation. This complexity becomes even more pronounced in nonspecialist health care environments due to limited resources, resulting in delayed referrals, increased misdiagnosis rates, and exacerbated disability outcomes for patients with SpA. The emergence of large language models (LLMs) in medical diagnostics introduces a revolutionary potential to overcome these diagnostic hurdles. Despite recent advancements in artificial intelligence and LLMs demonstrating effectiveness in diagnosing and treating various diseases, their application in SpA remains underdeveloped. Currently, there is a notable absence of SpA-specific LLMs and an established benchmark for assessing the performance of such models in this particular field. OBJECTIVE Our objective is to develop a foundational medical model, creating a comprehensive evaluation benchmark tailored to the essential medical knowledge of SpA and its unique diagnostic and treatment protocols. The model, post-pretraining, will be subject to further enhancement through supervised fine-tuning. It is projected to significantly aid physicians in SpA diagnosis and treatment, especially in settings with limited access to specialized care. Furthermore, this initiative is poised to promote early and accurate SpA detection at the primary care level, thereby diminishing the risks associated with delayed or incorrect diagnoses. METHODS A rigorous benchmark, comprising 222 meticulously formulated multiple-choice questions on SpA, will be established and developed. These questions will be extensively revised to ensure their suitability for accurately evaluating LLMs' performance in real-world diagnostic and therapeutic scenarios. Our methodology involves selecting and refining top foundational models using public data sets. The best-performing model in our benchmark will undergo further training. Subsequently, more than 80,000 real-world inpatient and outpatient cases from hospitals will enhance LLM training, incorporating techniques such as supervised fine-tuning and low-rank adaptation. We will rigorously assess the models' generated responses for accuracy and evaluate their reasoning processes using the metrics of fluency, relevance, completeness, and medical proficiency. RESULTS Development of the model is progressing, with significant enhancements anticipated by early 2024. The benchmark, along with the results of evaluations, is expected to be released in the second quarter of 2024. CONCLUSIONS Our trained model aims to capitalize on the capabilities of LLMs in analyzing complex clinical data, thereby enabling precise detection, diagnosis, and treatment of SpA. This innovation is anticipated to play a vital role in diminishing the disabilities arising from delayed or incorrect SpA diagnoses. By promoting this model across diverse health care settings, we anticipate a significant improvement in SpA management, culminating in enhanced patient outcomes and a reduced overall burden of the disease. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/57001.
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Affiliation(s)
- Anan Wang
- Department of Medical Innovation Research, Chinese PLA General Hospital, Beijing, China
| | - Yunong Wu
- Dataa Robotics Co, Ltd, Beijing, China
| | - Xiaojian Ji
- Department of Rheumatology and Immunology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiangyang Wang
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jiawen Hu
- Department of Rheumatology and Immunology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | | | | | - Dong Pu
- Dataa Robotics Co, Ltd, Beijing, China
| | - Lulu Tang
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Shikui Ma
- Dataa Robotics Co, Ltd, Beijing, China
| | - Qiang Liu
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jing Dong
- Department of Medical Innovation Research, Chinese PLA General Hospital, Beijing, China
| | - Kunlun He
- Department of Medical Innovation Research, Chinese PLA General Hospital, Beijing, China
| | - Kunpeng Li
- Department of Rheumatology and Immunology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Da Teng
- Beijing Institute of Petrochemical Technology, Beijing, China
| | - Tao Li
- Department of Medical Innovation Research, Chinese PLA General Hospital, Beijing, China
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Hofmann-Apitius M, Fröhlich H. Foresight-generative pretrained transformer for the prediction of patient timelines. Lancet Digit Health 2024; 6:e233-e234. [PMID: 38519150 DOI: 10.1016/s2589-7500(24)00045-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Affiliation(s)
- Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany.
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
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