Intelligence-based medicine最新文献_第5页

Nanotechnology and machine learning: a promising confluence for the advancement of precision medicine 纳米技术和机器学习：精密医学进步的有希望的融合

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100267

Shuaibu Saidu Musa , Adamu Muhammad Ibrahim , Muhammad Yasir Alhassan , Abubakar Hafs Musa , Abdulrahman Garba Jibo , Auwal Rabiu Auwal , Olalekan John Okesanya , Zhinya Kawa Othman , Muhammad Sadiq Abubakar , Mohamed Mustaf Ahmed , Carina Joane V. Barroso , Abraham Fessehaye Sium , Manuel B. Garcia , James Brian Flores , Adamu Safiyanu Maikifi , M.B.N. Kouwenhoven , Don Eliseo Lucero-Prisno

The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.

纳米技术中的分子尺度工程与机器学习（ML）分析的融合正在重塑精准医学领域。纳米颗粒可以实现超灵敏的诊断、靶向药物和基因传递以及高分辨率成像，而ML模型可以挖掘大量的多模态数据集来优化纳米颗粒设计，提高预测准确性，并实时个性化治疗。最近的突破包括：ml引导的脂质、聚合物和无机载体跨越生物屏障的配方；人工智能增强的纳米传感器可以从呼吸、汗液或血液中发现早期疾病；纳米治疗剂可以同时追踪和治疗肿瘤。对检索增强生成和监督学习管道的比较研究揭示了纳米器件工程在不同数据环境中的独特优势。进一步关注可解释的人工智能工具，如SHAP、LIME、Grad-CAM和集成梯度，强调了它们在提高纳米临床决策的透明度、信任和可解释性方面的作用。采用结构化的叙事回顾方法，综合ML模型的关键性能，增强分析的清晰度。新兴的可生物降解纳米材料、自主微纳米机器人和混合芯片实验室系统承诺更快地做出护理点决策，但也提出了关于数据完整性、可解释性、可扩展性、监管、伦理和公平获取的紧迫问题。解决这些障碍需要健全的数据标准、隐私保护、跨学科研发网络和灵活的审批途径，才能将实验成果转化为患者的临床益处。这篇综述综合了纳米技术和机器学习在精准医学领域交叉的现状、关键挑战和未来方向。

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引用次数: 0

PU-MLP: A PU-learning based method for polypharmacy side-effects detection based on multi-layer perceptron and feature extraction techniques PU-MLP：一种基于pu学习的基于多层感知器和特征提取技术的多药副作用检测方法

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100265

Abedin Keshavarz, Amir Lakizadeh

Polypharmacy, or the concurrent use of multiple medications, increases the risk of adverse effects due to drug interactions. As polypharmacy becomes more prevalent, forecasting these interactions is essential in the pharmaceutical field. Due to the limitations of clinical trials in detecting rare side effects associated with polypharmacy, computational methods are being developed to model these adverse effects. This study introduces a method named PU-MLP, based on a Multi-Layer Perceptron, to predict side effects from drug combinations. This research utilizes advanced machine learning techniques to explore the connections between medications and their adverse effects. The approach consists of three key stages: first, it creates an optimal representation of each drug using a combination of a random forest classifier, Graph Neural Networks (GNNs), and dimensionality reduction techniques. Second, it employs Positive Unlabeled learning to address data uncertainty. Finally, a Multi-Layer Perceptron model is utilized to predict polypharmacy side effects. Performance evaluation using 5-fold cross-validation shows that the proposed method surpasses other approaches, achieving impressive scores of 0.99, 0.99, and 0.98 in AUPR, AUC, and F1 measures, respectively.

多种用药，或同时使用多种药物，由于药物相互作用，增加了不良反应的风险。随着多药制药变得越来越普遍，预测这些相互作用在制药领域是必不可少的。由于临床试验在检测与多种药物相关的罕见副作用方面的局限性，正在开发计算方法来模拟这些副作用。本研究提出了一种基于多层感知机的PU-MLP方法来预测药物组合的副作用。这项研究利用先进的机器学习技术来探索药物及其副作用之间的联系。该方法包括三个关键阶段：首先，它使用随机森林分类器、图神经网络（gnn）和降维技术的组合创建每种药物的最佳表示。其次，它采用正无标签学习来解决数据的不确定性。最后，利用多层感知器模型对多药副作用进行预测。使用5倍交叉验证的性能评估表明，所提出的方法优于其他方法，在AUPR、AUC和F1指标上分别取得了令人印象深刻的0.99、0.99和0.98的分数。

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引用次数: 0

LCSNet: Lightweight Caries Segmentation Network for the segmentation of dental caries using smartphone photographs LCSNet：轻量级的龋齿分割网络，用于使用智能手机照片分割龋齿

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100304

Radha R.C. , B.S. Raghavendra , Rishabh Kumar Hota , K.R. Vijayalakshmi , Seema Patil , A.V. Narasimhadhan

Dental caries is one of the major dental issues that is common among many individuals. It leads to tooth loss and affects the tooth root, creating a need to automatically detect dental caries to reduce treatment costs and prevent its consequences. The Lightweight Caries Segmentation Network (LCSNet) proposed in this study detects the location of dental caries by applying pixel-wise segmentation to dental photographs taken with various Android phones. LCSNet utilizes a Dual Multiscale Residual (DMR) block in both the encoder and decoder, adapts transfer learning through a pre-trained InceptionV3 model at the bottleneck layer, and incorporates a Squeeze and Excitation block in the skip connection, effectively extracting spatial information even from images where 95 % of the background and only 5 % represent the area of interest. A new dataset was developed by gathering oral photographs of dental caries from two hospitals, with advanced augmentation techniques applied. The LCSNet architecture demonstrated an accuracy of 97.36 %, precision of 73.1 %, recall of 70.2 %, an F1-Score of 71.14 %, and an Intersection-over-Union (IoU) of 56.8 %. Expert dentists confirmed that the LCSNet model proposed in this in vivo study accurately segments the position and texture of dental caries. Both qualitative and quantitative performance analyses, along with comparative analyses of efficiency and computational requirements, were conducted with other deep learning models. The proposed model outperforms existing deep learning models and shows significant potential for integration into a smartphone application-based oral disease detection system, potentially replacing some conventional clinically adapted methods.

龋齿是许多人常见的主要牙齿问题之一。它会导致牙齿脱落并影响牙根，因此需要自动检测龋齿，以减少治疗费用并预防其后果。本研究提出的轻量级龋齿分割网络（LCSNet）通过对各种Android手机拍摄的牙齿照片进行逐像素分割来检测龋齿的位置。LCSNet在编码器和解码器中都使用了双多尺度残差（DMR）块，通过瓶颈层预训练的InceptionV3模型适应迁移学习，并在跳过连接中结合了挤压和激励块，即使从95%的背景和只有5%代表感兴趣区域的图像中也能有效地提取空间信息。通过收集来自两家医院的龋齿口腔照片，并应用先进的增强技术，开发了一个新的数据集。LCSNet体系结构的准确率为97.36%，准确率为73.1%，召回率为70.2%，F1-Score为71.14%，IoU为56.8%。专家牙医证实，在体内研究中提出的LCSNet模型准确地分割了蛀牙的位置和质地。定性和定量的性能分析，以及效率和计算需求的比较分析，都与其他深度学习模型进行了比较。所提出的模型优于现有的深度学习模型，并显示出集成到基于智能手机应用程序的口腔疾病检测系统的巨大潜力，有可能取代一些传统的临床适应方法。

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引用次数: 0

Uncertainty-aware hybrid optimization for robust cardiovascular disease detection: A clinical translation framework 不确定性感知混合优化稳健心血管疾病检测：临床翻译框架

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100302

Tamanna Jena , Rahul Suryodai , Desidi Narsimha Reddy , Kambala Vijaya Kumar , Elangovan Muniyandy , N.V. Phani Sai Kumar

Background

Cardiovascular disease causes 17.9 million deaths annually, yet current AI systems achieve ∼82 % accuracy without uncertainty quantification—limiting clinical utility where prediction confidence directly guides life-saving treatment decisions.

Objective

We developed an uncertainty-aware hybrid optimization framework for robust CVD detection that provides clinicians with both risk predictions and confidence intervals, enabling personalized decision-making under real-world clinical conditions.

Methods

Our clinical translation framework integrates multiple complementary AI models (Gaussian processes, gradient-boosted trees, Transformers) through uncertainty-guided optimization. Key clinical innovations include: (1) real-time uncertainty calibration responding to data quality variations, (2) dynamic model weighting adapting to individual patient characteristics, and (3) interpretable confidence intervals supporting clinical decision protocols.

Results

Clinical validation on 12,458 CVD patients from MIMIC-III and UK Biobank demonstrated clinically significant improvements: +1.4 % AUC (0.853 vs 0.839, p < 0.01) translating to 50 additional correct diagnoses per 10,000 patients, +1.5 % balanced accuracy, and 20 % better uncertainty calibration. The framework maintained robust performance (>80 % AUC) under realistic clinical noise while providing reliable confidence intervals across all risk levels.

Clinical translation

This framework delivers immediate clinical utility through real-time inference (<2s), FHIR-compliant EHR integration, and physician-validated uncertainty interpretation. Implementation prevents an estimated 50 missed diagnoses and 23 unnecessary procedures per 10,000 patients screened annually.

Conclusions

Our uncertainty-aware framework represents the first clinically ready AI system providing both accurate CVD risk assessment and trustworthy confidence measures, directly addressing physician adoption barriers and supporting personalized cardiovascular care.

背景：心血管疾病每年导致1790万人死亡，但目前的人工智能系统在没有不确定性量化的情况下达到了82%的准确率，这限制了临床实用性，预测置信度直接指导挽救生命的治疗决策。目的：我们开发了一个不确定性感知的混合优化框架，用于稳健的心血管疾病检测，为临床医生提供风险预测和置信区间，从而在现实临床条件下实现个性化决策。方法通过不确定性导向优化，sour临床翻译框架集成了多个互补的人工智能模型（高斯过程、梯度增强树、变形金刚）。关键的临床创新包括：(1)响应数据质量变化的实时不确定度校准，(2)适应个体患者特征的动态模型加权，以及(3)支持临床决策方案的可解释置信区间。结果：来自MIMIC-III和UK Biobank的12,458例CVD患者的临床验证显示出临床显着改善：+ 1.4%的AUC （0.853 vs 0.839, p < 0.01）转化为每10,000例患者额外50例正确诊断，+ 1.5%的平衡准确性和20%的不确定度校准。该框架在真实的临床噪声下保持稳健的性能（80% AUC），同时在所有风险水平上提供可靠的置信区间。临床翻译该框架通过实时推理（<2s）、符合fhir的EHR集成和医生验证的不确定性解释，提供即时的临床效用。每年每1万名接受筛查的患者中，估计有50例漏诊和23例不必要的手术得到预防。结论我们的不确定性感知框架代表了第一个临床就绪的人工智能系统，提供准确的心血管疾病风险评估和可信赖的信心措施，直接解决医生采用障碍并支持个性化心血管护理。

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引用次数: 0

Speech biomarkers predict amyloid status in cognitively unimpaired adults 语言生物标志物预测认知功能正常的成年人的淀粉样蛋白状态

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100306

Peru Gabirondo , María García-Martínez , Ana Pozueta-Cantudo , Patricia Laura Maran , Patricia Dias , Tomas Rojo , Javier Jiménez-Raboso , Carmen Lage , Francisco Martínez-Dubarbie , Sara López-García , Marta Fernández-Matarrubia , Andrea Corrales-Pardo , María Bravo , Juan Irure-Ventura , Marcos López-Hoyos , Pascual Sánchez-Juan , Carla Zaldua , Eloy Rodríguez-Rodríguez

引用次数: 0

Enhancing emotion recognition through multi-modal data fusion and graph neural networks 通过多模态数据融合和图神经网络增强情绪识别

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100291

Kasthuri Devarajan , Suresh Ponnan , Sundresan Perumal

In this paper, a novel emotion detection system is proposed based on Graph Neural Network (GNN) architecture, which is used to integrate and learn from multiple data sets (EEG, face expression, physiological signals). The proposed GNN is able to learn about interactions between multiple modalities, so as to extract a single picture of emotion categorization. This model is very good and gets 91.25 % accuracy, 91.26 % precision, 91.25 % recall and 91.25 % F1-score. Moreover, the proposed GNN is a sensible trade-off between speed and precision, with a calculation time of 163 ms. The Proposed GNN is better, primarily due to its ability to represent complex relations between multi-modal inputs, thereby improving its real-time emotional state recognition and classification performance. The proposed GNN demonstrates its suitability for powerful emotion detection by outperforming all models in classification precision and multi-modal data fusion, surpassing traditional models such as SVM, KNN, CCA, CNN, and RNN. The Proposed GNN consistently proves to be the most accurate and robust solution, having been the most dominant technique in emotion detection, despite CNN and RNN achieving slightly better results.

本文提出了一种基于图神经网络（GNN）架构的情绪检测系统，该系统用于对多个数据集（EEG、面部表情、生理信号）进行整合和学习。提出的GNN能够学习多个模态之间的相互作用，从而提取出单一的情绪分类图像。该模型的准确率为91.25%，精密度为91.26%，召回率为91.25%，f1分数为91.25%。此外，所提出的GNN在速度和精度之间进行了合理的权衡，计算时间为163 ms。提出的GNN更好，主要是因为它能够表示多模态输入之间的复杂关系，从而提高了其实时情绪状态识别和分类性能。本文提出的GNN在分类精度和多模态数据融合方面优于所有模型，超越了SVM、KNN、CCA、CNN和RNN等传统模型，证明了它适合于强大的情感检测。尽管CNN和RNN取得了稍好的结果，但所提出的GNN始终被证明是最准确和鲁棒的解决方案，是情绪检测中最主要的技术。

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引用次数: 0

Clinical-ready CNN framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency 用于肺癌分类的临床就绪CNN框架：提高计算效率的医疗部署系统优化

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100292

G. Inbasakaran, J. Anitha Ruth

Purpose

This study develops a computationally efficient Convolutional Neural Network (CNN) for lung cancer classification in Computed Tomography (CT) images, addressing the critical need for accurate diagnostic tools deployable in resource-constrained clinical settings.

Methods

Using the IQ-OTH/NCCD dataset (1190 CT images: normal, benign, and malignant classes from 110 patients), we implemented systematic architecture optimization with strategic data augmentation to address class imbalance and limited dataset challenges. Patient-level data splitting prevented leakage, ensuring valid performance metrics. The model was evaluated using 5-fold cross-validation and compared against established architectures using McNemar's test for statistical significance.

Results

The optimized CNN achieved 94 % classification accuracy with only 4.2 million parameters and 18 ms inference time. Performance significantly exceeded AlexNet (85 %), VGG-16 (88 %), ResNet-50 (90 %), InceptionV3 (87 %), and DenseNet (86 %) with p < 0.05. Malignant case detection showed excellent clinical metrics (precision: 0.96, recall: 0.95, F1-score: 0.95), critical for minimizing false negatives. Ablation studies revealed data augmentation contributed 6.6 % accuracy improvement, with rotation and translation proving most effective. The model operates 4.3 × faster than ResNet-50 while using 6 × fewer parameters, enabling deployment on standard clinical workstations with 4–8 GB GPU memory.

Conclusions

Carefully optimized CNN architectures can achieve superior diagnostic performance while meeting computational constraints of real-world medical settings. Our approach demonstrates that systematic optimization strategies effectively balance accuracy with clinical deployment feasibility, providing a practical framework for implementing AI-assisted lung cancer detection in resource-limited healthcare environments. The model's high sensitivity for malignant cases positions it as a valuable clinical decision support tool.

目的：本研究开发了一种计算效率高的卷积神经网络（CNN），用于计算机断层扫描（CT）图像中的肺癌分类，解决了在资源有限的临床环境中部署准确诊断工具的关键需求。方法利用iqoth /NCCD数据集（来自110例患者的1190张CT图像：正常、良性和恶性分类），通过战略性数据增强实现系统架构优化，以解决分类不平衡和数据集有限的挑战。患者级数据分割防止了泄漏，确保了有效的性能指标。该模型使用5倍交叉验证进行评估，并使用McNemar的统计显著性检验与已建立的架构进行比较。结果优化后的CNN只需要420万个参数和18 ms的推理时间，分类准确率达到94%。性能显著高于AlexNet（85%）、VGG-16（88%）、ResNet-50（90%）、InceptionV3（87%）和DenseNet (86%), p < 0.05。恶性病例的检出表现出优异的临床指标（准确率：0.96，召回率：0.95，f1评分：0.95），这对于减少假阴性至关重要。消融研究显示，数据增强提高了6.6%的准确性，旋转和平移被证明是最有效的。该模型的运行速度比ResNet-50快4.3倍，同时使用的参数减少了6倍，可在具有4-8 GB GPU内存的标准临床工作站上部署。结论经过精心优化的CNN架构在满足现实医疗环境计算约束的情况下，能够取得优异的诊断性能。我们的方法表明，系统优化策略有效地平衡了准确性和临床部署的可行性，为在资源有限的医疗环境中实施人工智能辅助肺癌检测提供了一个实用的框架。该模型对恶性病例的高敏感性使其成为一种有价值的临床决策支持工具。

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引用次数: 0

A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM 基于反应预测的药物推荐系统：利用LightGBM整合药物SMILES的基因表达和K-mer碎片化

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100206

Sajid Naveed , Mujtaba Husnain

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects.

医学专家和医生检查胶质母细胞瘤（GBM）癌症患者的基因表达异常，以确定药物反应。本研究的主要目的是建立一个基于机器学习（ML）的模型，以改善癌症药物治疗的结果，从而节省医生的时间和精力。我们的目标是开发一种药物反应推荐系统，利用癌细胞系的基因表达数据，以半最大抑制浓度（IC50）来预测抗癌药物的反应。来自GBM癌症患者的遗传数据被用作系统的输入，以预测和推荐多种抗癌药物对特定癌症样本的反应。在这项研究中，我们利用K-mer分子碎片以一种新颖的方式处理药物SMILES，这使我们能够建立一个提供药物反应的胜任模型。我们使用光梯度增强机（Light Gradient Boosting Machine, LightGBM）回归算法和癌症药物敏感性基因组学（Genomics of Drug Sensitivity of Cancer， GDSC）数据来构建这个推荐系统。结果表明，对GBM数据的预测IC50值均落在实际值的范围内。替莫唑胺和卡莫司汀两种药物的预测均方误差（MSE）分别为0.10和0.11，未见样品的预测均方误差为0.41。这些建议的反应然后由专家医生验证，他们确认对这些药物的反应非常接近实际反应。这些建议也有效地减缓这些肿瘤的生长，并通过监测药物效果来改善患者的生活质量。

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引用次数: 0

Arkangel AI: A conversational agent for real-time, evidence-based medical question-answering Arkangel AI：实时、循证医学问答的对话代理

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100274

Maria Camila Villa, Natalia Castano-Villegas, Isabella Llano, Julian Martinez, Maria Fernanda Guevara, Jose Zea, Laura Velásquez

Introduction

Large Language Models (LLMs) have been trained and tested on several medical question-answering (QA) datasets built from medical licensing exams and natural interactions between doctors and patients to fine-tune them for specific health-related tasks.

Objective

We aimed to develop LLM-powered Conversational Agents (CAs) equipped to produce fast, accurate, and real-time responses to medical queries in different clinical and scientific scenarios. This paper presents Arkangel AI, our first conversational agent and research assistant.

Methods

The model is based on a system containing five LLMs; each is classified within a specific workflow with pre-defined instructions to produce the best search strategy and provide evidence-based answers. We assessed accuracy, intra/inter-class variability, and Cohen's Kappa using the question-answer (QA) dataset MedQA. Additionally, we used the PubMedQA dataset and assessed both databases using the RAGAS framework, including Context, Response Relevance, and Faithfulness. Traditional statistical analysis was performed with hypothesis tests and 95 % IC.

Results

Accuracy for MedQA (n: 1273) was 90.26 % and Cohen's kappa was 87 %, surpassing current SoTAs for other LLMs (GPT-4o, MedPaLM2). The model retrieved 80 % of the expected articles and provided relevant answers in 82 % of PubMedQA.

Conclusion

Arkangel AI showed proficient retrieval and reasoning abilities and unbiased responses. Evenly distributed medical QA datasets to train improved LLMs and external validation for the model with real-world physicians in clinical scenarios are needed. Clinical decision-making remains in the hands of trained healthcare professionals.

大型语言模型（llm）已经在几个医学问答（QA）数据集上进行了培训和测试，这些数据集来自医疗许可考试和医生和患者之间的自然互动，以微调它们以适应特定的健康相关任务。我们的目标是开发基于llm的会话代理（CAs），以便在不同的临床和科学场景中对医疗查询产生快速、准确和实时的响应。本文介绍了Arkangel AI，我们的第一个会话代理和研究助理。方法基于一个包含5个llm的系统建立模型；每个都在特定的工作流中进行分类，并带有预定义的指令，以产生最佳搜索策略并提供基于证据的答案。我们使用问答（QA）数据集MedQA评估准确性、类内/类间变异性和Cohen Kappa。此外，我们使用PubMedQA数据集，并使用RAGAS框架评估两个数据库，包括上下文、响应相关性和可信度。结果MedQA （n: 1273）的准确率为90.26%,Cohen’s kappa为87%，超过了目前其他LLMs （gpt - 40、MedPaLM2）的SoTAs。该模型检索了80%的预期文章，并在82%的PubMedQA中提供了相关答案。结论arkangel人工智能具有良好的检索推理能力和无偏性反应。需要均匀分布的医疗QA数据集来训练改进的llm，并在临床场景中与现实世界的医生一起对模型进行外部验证。临床决策仍然掌握在训练有素的医疗保健专业人员手中。

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引用次数: 0

Design and implementation of a low-cost malaria diagnostic system based on convolutional neural network 基于卷积神经网络的低成本疟疾诊断系统的设计与实现

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100272

Ekobo Akoa Brice , Ndoumbe Jean , Mohamadou Madina

This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the CNN (convolutional neural network). The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not; its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. The system thus created allows malaria to be diagnosed at low cost from blood smear images. The use of CNNs in this project has the advantage of automatically extracting features from blood smear images and classifying them efficiently. The major advantage of the proposed system is its portability and lower cost. The performance of the proposed algorithm was evaluated on a publicly available malaria data set.

这项工作的重点是设计和实现一个智能系统，可以从血液涂片图像诊断疟疾。该系统以图像格式获取数据，并提供即时和自动的诊断，将患者的病情结果输出到屏幕上。实现该系统的方法是基于CNN（卷积神经网络）。后者具有作为特征提取器和图像分类器的特异性。由此获得的软件部分在一个电子设备中实现，该设备作为一个工具包安装在我们的护理中。建立这样一个系统具有无数的优势，例如实验室技术人员在诊断过程中是否快速；它的可移植性将使它在任何需要的地方都能使用。从人体工程学和功能的角度来看，该系统对大规模疟疾地方病的诊断具有实际影响。CNN在一个大型的血液涂片数据集上进行训练，能够以高灵敏度和特异性准确地对感染和未感染的样本进行分类。在对几个样本进行测试后，该系统的平均灵敏度达到89.50%，平均精度达到89%，这改善了疟疾诊断的决策。这样创建的系统可以通过血液涂片图像以低成本诊断疟疾。在本课题中使用cnn具有从血液涂片图像中自动提取特征并进行高效分类的优点。该系统的主要优点是可移植性和低成本。在一个公开可用的疟疾数据集上评估了所提出算法的性能。

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