Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the ~3-10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and ~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
The University of Manchester（曼彻斯特大学）；CRUK Manchester Institute（英国癌症研究中心曼彻斯特研究所）；Idiap Research Institute（Idiap研究所）
Artificial Intelligence in Healthcare: Lost In Translation?
QUEST-Center for Transforming Biomedical Research, Berlin Institute of Health（柏林卫生研究所QUEST转化生物医学研究中心）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；CLAIM - Charité Lab for AI in Medicine（夏里特医学人工智能实验室（CLAIM））；School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学计算、工程与建筑环境学院计算与数字技术系）
Impact of clinical decision support systems (cdss) on clinical outcomes and healthcare delivery in low- and middle-income countries: protocol for a systematic review and meta-analysis
ICMR National Institute for Research in Digital Health & Data Science（印度国家数字健康与数据科学研究所）；ICMR Headquarters（印度国家医学研究委员会总部）；ICMR-RMRC Bhubaneshwar（印度国家医学研究委员会比哈尔邦研究中心）
Institute of Logic, Language, and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Pacmed（Pacmed公司）；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
College of Health Sciences, Bayero University Kano（贝耶罗大学卡诺分校健康科学学院）；Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）；African Institute for Research Advancement & Innovation（非洲研究促进与创新研究所）；Medical Artificial Intelligence Laboratory (MAI Lab)（医学人工智能实验室）；Aminu Kano Teaching Hospital（阿米努·卡诺教学医院）；Nnamdi Azikiwe University Teaching Hospital（纳姆迪·阿齐基韦大学教学医院）；Lagos State Teaching Hospital（拉各斯州立教学医院）
Beyond Random Missingness: Clinically Rethinking for Healthcare Time Series Imputation
King’s College London（伦敦国王学院）；PyPOTS Research（PyPOTS研究中心）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology（香港科技大学）；University College London（伦敦大学学院）
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
Dr. B. C. Roy Engineering College（B.C.罗伊工程学院）；Bengal College of Engg. and Tech.（孟加拉工程技术学院）；National Institute of Technology（印度国家理工学院）；University of North Texas（北得克萨斯大学）
本文提出 Easydiagnos 框架，通过融合遗传算法、可解释 AI 和排列组合技术的 AFE 算法优化医疗特征选择，并在多个数据集上验证其可提升临床决策的准确性与可解释性。
Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings
Centre for Clinical Intelligence & Safety（临床智能与安全中心）；Tomorrow University of Applied Sciences（明天应用科学大学）
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Carnegie Mellon University（卡内基梅隆大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；GenBio AI（基因生物人工智能公司）；Intel（英特尔公司）
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Hong Kong University of Science and Technology（香港科技大学）；Massachusetts Institute of Technology（麻省理工学院）；Adobe Research（Adobe研究院）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
Madrigal Pharmaceuticals Inc（马德里加尔制药公司）；Boston University School of Medicine（波士顿大学医学院）；AbbVie Inc.（艾伯维公司）；St. Elizabeth Healthcare（圣伊丽莎白医疗中心）；Thermo Fisher Scientific（赛默飞世尔科技）
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
Department of Computer Science, Stevens Institute of Technology（斯蒂文斯理工学院计算机科学系）；Weill Cornell Medicine Graduate School of Medical Sciences, Cornell University（韦尔医学院医学研究生院，康奈尔大学）
Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
Peking University（北京大学）；Peking University Cancer Hospital and Institute（北京大学肿瘤医院暨北京市肿瘤防治研究所）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用技术国家工程实验室）；Peking University Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
University of Southern California（南加州大学）；Tsinghua University（清华大学）
深度学习模型（又称深度神经网络）已经彻底改变了许多领域，包括计算机视觉、自然语言处理、语音识别，并且越来越多地用于临床医疗应用。然而，很少有工作存在基准的深度学习模型的性能相对于最先进的机器学习模型和预后评分系统在公共可用的医疗保健数据集。在本文中，我们使用深度学习模型、集成机器学习模型（超级学习算法）、SAPS II和SOFA评分，展示了几种临床预测任务的基准测试结果，如死亡率预测、住院时间预测和ICD-9代码组预测。我们使用了重症监护医学信息市场III（MIMIC-III）（v1.4）公开数据集，其中包括2001年至2012年在Beth Israel Deaconess医疗中心入住ICU的所有患者，用于基准任务。我们的研究结果表明，深度学习模型始终优于所有其他方法，特别是当“原始”临床时间序列数据用作模型的输入特征时。
Review of multimodal machine learning approaches in healthcare
Oxford Internet Institute, University of Oxford（牛津大学互联网研究所）；Institute for People-Centred AI, University of Surrey（萨里大学以人为本人工智能研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院惠康/英国工程与物理科学研究委员会介入与外科科学中心）
