Journal of medical artificial intelligence最新文献_第2页

Maintaining scientific integrity and high research standards against the backdrop of rising artificial intelligence use across fields 在各领域人工智能应用不断增加的背景下，保持科学诚信和高研究标准

Journal of medical artificial intelligence Pub Date : 2023-11-01 DOI: 10.21037/jmai-23-63

Fiona McGowan Martha Morrison, Nima Rezaei, Amanuel Godana Arero, Vasko Graklanov, Sevan Iritsyan, Mariya Ivanovska, Rangariari Makuku, Leander Penaso Marquez, Kseniia Minakova, Lindelwa Phakamile Mmema, Piotr Rzymski, Ganna Zavolodko

{"title":"Maintaining scientific integrity and high research standards against the backdrop of rising artificial intelligence use across fields","authors":"Fiona McGowan Martha Morrison, Nima Rezaei, Amanuel Godana Arero, Vasko Graklanov, Sevan Iritsyan, Mariya Ivanovska, Rangariari Makuku, Leander Penaso Marquez, Kseniia Minakova, Lindelwa Phakamile Mmema, Piotr Rzymski, Ganna Zavolodko","doi":"10.21037/jmai-23-63","DOIUrl":"https://doi.org/10.21037/jmai-23-63","url":null,"abstract":"Abstract: Artificial intelligence (AI) technologies have already played a revolutionary role in scientific research, from diagnostics to text-generative AI used in scientific writing. The use of AI in the scientific field needs transparent regulation, especially with a longstanding history of use—the first AI technologies in science were developed in the 1950s. Since then, AI has gone from being able to alter texts to producing them using billions of parameters to generate accurate and natural texts. However, scientific work requires high ethical and professional standards, and the rise of AI use in the field has led to many institutions and journals releasing statements and restrictions on its use. AI, being reliant on its users can exacerbate and increase existing biases in the field without being able to take accountability. AI responses can also often lack specificity and depth. However, it is important not to condemn the use of AI in scientific work as a whole. This article has partial use of an AI large language model (LLM), specifically Chatbot Generative Pre-Trained Transformer (ChatGPT), to demonstrate the theories with clear examples. Several recommendations on both a strategic and regulatory level have been formulated in this paper to enable the complementary use of AI alongside ethically-conducted scientific research or for educational purposes, where it shows great potential as a transformative force in interactive work. Policymakers should create wide-reaching, clear guidelines and legal frameworks for using AI to remove the burden of consideration from educators and senior researchers. Caution in the scientific community is advised, though further understanding and work to improve AI use is encouraged.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"101 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of machine learning algorithms used to catalog Google Appstore 机器学习算法用于谷歌应用商店目录的比较

Journal of medical artificial intelligence Pub Date : 2023-11-01 DOI: 10.21037/jmai-23-58

Priyadarshini Pattanaik, Dimple Nagpal

引用次数: 0

Applying large language model artificial intelligence for retina International Classification of Diseases (ICD) coding 应用大语言模型人工智能进行视网膜国际疾病分类编码

Journal of medical artificial intelligence Pub Date : 2023-10-01 DOI: 10.21037/jmai-23-106

Joshua Ong, Nikita Kedia, Sanjana Harihar, Sharat Chandra Vupparaboina, Sumit Randhir Singh, Ramesh Venkatesh, Kiran Vupparaboina, Sandeep Chandra Bollepalli, Jay Chhablani

引用次数: 1

ChatGPT and medicine: a potential threat to science or a step towards the future? ChatGPT和医学:对科学的潜在威胁还是迈向未来的一步?

Journal of medical artificial intelligence Pub Date : 2023-10-01 DOI: 10.21037/jmai-23-70

Lucas Lacerda de Souza, Felipe Paiva Fonseca, Manoela Domingues Martins, Oslei Paes de Almeida, Helder Antônio Rebelo Pontes, Fábio Luiz Coracin, Márcio Ajudarte Lopes, Syed Ali Khurram, Alan Roger Santos-Silva, Ahmed Hagag, Pablo Agustin Vargas

引用次数: 0

Implementing an artificial intelligence system to comprehensively manage people with glaucoma: a blueprint 实施人工智能系统对青光眼患者进行综合管理:蓝图

Journal of medical artificial intelligence Pub Date : 2023-10-01 DOI: 10.21037/jmai-23-91

Simon E. Skalicky, Robert N. Weinreb, Nahum Goldmann, Ricardo Augusto Paletta Guedes, Christophe Baudouin, Xiulan Zhang, Aukje van Gestel, Eytan Z. Blumenthal, Paul L. Kaufman, Robert Rothman, Ana Maria Vasquez, Paul Harasymowycz, Derek S. Welsbie, Ivan Goldberg

引用次数: 0

The impact of prompt engineering in large language model performance: a psychiatric example 提示工程对大型语言模型性能的影响:一个精神病学的例子

Journal of medical artificial intelligence Pub Date : 2023-10-01 DOI: 10.21037/jmai-23-71

Declan Grabb

引用次数: 0

Handling the predictive uncertainty of convolutional neural network in medical image analysis: a review 卷积神经网络在医学图像分析中的预测不确定性处理综述

Journal of medical artificial intelligence Pub Date : 2023-10-01 DOI: 10.21037/jmai-23-40

Yasanthi Malika Hirimutugoda, Thusari P. Silva, Nimalka M. Wagarachchi

{"title":"Handling the predictive uncertainty of convolutional neural network in medical image analysis: a review","authors":"Yasanthi Malika Hirimutugoda, Thusari P. Silva, Nimalka M. Wagarachchi","doi":"10.21037/jmai-23-40","DOIUrl":"https://doi.org/10.21037/jmai-23-40","url":null,"abstract":": Certainty is a significant part of disease detection, involving various kinds of imaging and machine learning (ML) methodologies. More precisely than other ML methods, a convolutional neural network (CNN) can classify images. As its parameters are deterministic, it cannot indicate the level of uncertainty in its predictions. Predictions made by predetermined CNNs may yield inaccurate findings, and there is no evaluation of confidence in these results. These outcomes may have harmful effects and lack trustworthiness. Uncertainty quantification (UQ) is critical to evaluating confidence in prediction. The noise, illumination, segmentation, and edge issues common to medical images also impact pre-trained CNN algorithms and lead to uncertain outcomes. This review aims to investigate the main inherent uncertainty issue in CNN and what form of UQ method can be applied with CNN to the task of medical image classification. This research proposes a novel approach by combining the superior properties of the Bayesian approach and fusion methods to reduce the uncertainty in CNN models. This study concludes that, despite a number of unresolved technical and scientific issues, various types of fusion approaches have improved the clinical validity for diagnosing and analytical purposes, and it is a field of study that has the capacity to grow significantly in the years to come.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136055906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using a self-attention architecture to automate valence categorization of French teenagers’ free descriptions of their family relationships: a proof of concept 使用自我关注架构自动对法国青少年对其家庭关系的自由描述进行价态分类:概念证明

Journal of medical artificial intelligence Pub Date : 2023-09-01 DOI: 10.21037/jmai-23-8

Mohammed Sedki, Nathan Vidal, Paul Roux, Caroline Barry, Mario Speranza, Bruno Falissard, Eric Brunet-Gouet

引用次数: 0

Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education 聊天生成预训练变压器在皮肤科特定问题上的表现及其在医学教育中的意义

Journal of medical artificial intelligence Pub Date : 2023-09-01 DOI: 10.21037/jmai-23-47

James Behrmann, Ellen M. Hong, Shannon Meledathu, Aliza Leiter, Michael Povelaitis, Mariela Mitre

引用次数: 0

Deep ensemble learning using a demographic machine learning risk stratifier for binary classification of skin lesions using dermatoscopic images 使用人口统计学机器学习风险分层器的深度集成学习，利用皮肤镜图像对皮肤病变进行二分类

Journal of medical artificial intelligence Pub Date : 2023-09-01 DOI: 10.21037/jmai-23-38

Ansh Roge, Patrick Ting, Andrew Chern, William Ting

引用次数: 0