標(biāo)題: Titlebook: Artificial Intelligence in Medicine; Niklas Lidstr?mer,Hutan Ashrafian Living reference work 20200th edition Deep Medicine.Machine Learni [打印本頁] 作者: interminable 時(shí)間: 2025-3-21 16:53
書目名稱Artificial Intelligence in Medicine影響因子(影響力)
書目名稱Artificial Intelligence in Medicine影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence in Medicine被引頻次
書目名稱Artificial Intelligence in Medicine被引頻次學(xué)科排名
書目名稱Artificial Intelligence in Medicine年度引用
書目名稱Artificial Intelligence in Medicine年度引用學(xué)科排名
書目名稱Artificial Intelligence in Medicine讀者反饋
書目名稱Artificial Intelligence in Medicine讀者反饋學(xué)科排名
作者: 金桌活畫面 時(shí)間: 2025-3-21 23:58 作者: 神圣在玷污 時(shí)間: 2025-3-22 01:50
Introduction to Artificial Intelligence in Medicine,cuss the power of contextual processing, study insights from the human visual system, and study in some detail how different deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.作者: 多產(chǎn)魚 時(shí)間: 2025-3-22 06:59
E. C. H. Meng,J. E. Taylor,S. B. Brush on crucial tasks, artificial intelligence algorithms were introduced..An expansive demand of AI applications in varying fields led to the development of specifically designed ad hoc algorithms with the role of better estimating (by learning) solutions to the problems..The boost of AI in healthcare 作者: 使服水土 時(shí)間: 2025-3-22 11:11
H. Perales,R. S. B. Brush,C. O. Qualsetical purposes have brought the necessity to introduce artificial intelligence algorithms to healthcare..The first artificial intelligence applications in the medical field were to be seen in the introduction of Electronic Health Records followed by the development of Learning Health Systems and Clin作者: 訓(xùn)誡 時(shí)間: 2025-3-22 13:38
https://doi.org/10.1007/978-981-16-3432-1cuss the power of contextual processing, study insights from the human visual system, and study in some detail how different deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.作者: progestin 時(shí)間: 2025-3-22 18:37
https://doi.org/10.1007/978-981-16-3432-1 to other domains.?This chapter presents an overview of the vast field of decision-making in healthcare and proposes a functional classification of decision tasks. We aim to equip the reader with a working knowledge of methods for clinical decision-making, including logic-based, learning-based, and 作者: 信任 時(shí)間: 2025-3-22 22:22 作者: 被告 時(shí)間: 2025-3-23 02:16
https://doi.org/10.1007/978-3-030-16361-7inning and will continue to impact other areas such as medical education. The multifaceted yet socrato-didactic methods of education need to evolve to cater for the twenty-first-century medical educator and trainee. Advances in machine learning and artificial intelligence are paving the way to new d作者: 英寸 時(shí)間: 2025-3-23 09:33 作者: Spartan 時(shí)間: 2025-3-23 11:41 作者: 過度 時(shí)間: 2025-3-23 15:02 作者: 有常識(shí) 時(shí)間: 2025-3-23 18:06
Ika Darnhofer,David Gibbon,Benoit Dedieuf AI, at least in the fields of oncology and infectious disease, evolutionary theory must be brought to bear. In oncology, AI is uniquely suited to analyze the complex latticework of correlations among the many genomic and environmental influences that constitute cancer risk. It also makes possible 作者: 泥瓦匠 時(shí)間: 2025-3-23 23:17
Stéphane Bellon,Jean-Louis Hemptinnees. EBM is therefore a systematic approach to decision-making that integrates these three inputs. It involves evidence production (design and conducting of clinical studies), synthesis (collecting, appraising, and combining data to answer clinical questions), implementation (e.g., through clinical p作者: Optometrist 時(shí)間: 2025-3-24 06:06 作者: terazosin 時(shí)間: 2025-3-24 09:28 作者: 放氣 時(shí)間: 2025-3-24 10:48 作者: GULF 時(shí)間: 2025-3-24 15:55
https://doi.org/10.1007/978-1-349-11615-7raditional Chinese medicine (TCM) now has been recognized as one of the main approaches of AM. As a clinical and evidence-driven discipline with long histories, AM is also heavily relied on in the utilization of big healthcare and therapeutic data for improving the capability of diagnosis and treatm作者: 斜坡 時(shí)間: 2025-3-24 21:59 作者: 非秘密 時(shí)間: 2025-3-25 02:19 作者: 單調(diào)性 時(shí)間: 2025-3-25 06:31
,Erratum to: Sydney’s ‘Invisible’ Farmers,move into the realm of complex diseases, this approach fails to provide the insight needed to explain disease pathogenesis. Network medicine is a new paradigm that applies network science, artificial intelligence (in particular, machine learning and graph mining), and systems biology approaches to s作者: 空氣 時(shí)間: 2025-3-25 10:03
,Erratum to: Sydney’s ‘Invisible’ Farmers,s the continuum of host defense, immune homeostasis/regulation, immune genetics, and laboratory immunology we are now seeing the emergence of artificial intelligence and data science approaches. These computational tools are being leveraged to analyze the inherently large datasets of relevance to cl作者: Dignant 時(shí)間: 2025-3-25 15:37
Niklas Lidstr?mer,Hutan AshrafianCovers challenges and problems faced through the use of artificial intelligence in medicine.Reviews the literature to give readers a scientific foundation.Contains original images and illustrations to作者: etiquette 時(shí)間: 2025-3-25 18:41
http://image.papertrans.cn/b/image/162484.jpg作者: Acetaminophen 時(shí)間: 2025-3-25 20:23
https://doi.org/10.1007/978-3-030-58080-3Deep Medicine; Machine Learning; Individualized Medicine; Medical Informatics; Personalisation of Eviden作者: GLUT 時(shí)間: 2025-3-26 02:27 作者: 親密 時(shí)間: 2025-3-26 07:09
Basic Concepts of Artificial Intelligence: Primed for Clinicians, on crucial tasks, artificial intelligence algorithms were introduced..An expansive demand of AI applications in varying fields led to the development of specifically designed ad hoc algorithms with the role of better estimating (by learning) solutions to the problems..The boost of AI in healthcare 作者: Nerve-Block 時(shí)間: 2025-3-26 12:25 作者: consolidate 時(shí)間: 2025-3-26 12:57
Introduction to Artificial Intelligence in Medicine,cuss the power of contextual processing, study insights from the human visual system, and study in some detail how different deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.作者: Insufficient 時(shí)間: 2025-3-26 18:12 作者: NEEDY 時(shí)間: 2025-3-26 22:20
Artificial Intelligence for Medical Diagnosis,ances in Artificial Intelligence have seen the emergence of diagnostic algorithms that perform as well as clinicians and can be applied at scale in clinical practice. This chapter presents a broad picture of the foundations, history, and the current state of AI in medical diagnosis. We provide an ov作者: 開始從未 時(shí)間: 2025-3-27 01:44
AIM in Medical Education,inning and will continue to impact other areas such as medical education. The multifaceted yet socrato-didactic methods of education need to evolve to cater for the twenty-first-century medical educator and trainee. Advances in machine learning and artificial intelligence are paving the way to new d作者: 犬儒主義者 時(shí)間: 2025-3-27 07:05
AIM in Medical Informatics,, and pathological data. Making a fruitful use of such data is not straightforward, as they are usually stored in electronic health records (EHRs) that need to be properly handled and processed in order to successfully perform medical diagnosis. In recent years, machine learning and deep learning te作者: 細(xì)絲 時(shí)間: 2025-3-27 10:04
Reporting Standards and Quality Assessment Tools in Artificial Intelligence Centered Healthcare Research waste,” and represents a significant moral hazard. In order to combat this issue, there has been a shift towards the use of reporting standards and quality assessment tools, a move that has been endorsed by major biomedical journals as well as other key stakeholders. These instruments help [.]作者: 柳樹；枯黃 時(shí)間: 2025-3-27 16:13
,AIM and the Patient’s Perspective,riences. The public have major concerns about data sharing and privacy and the ability or otherwise of the technology to act independently of doctors and human oversight. Regulations are complex and poorly understood by the public, and media attention focused on recent breaches of trust and security作者: anachronistic 時(shí)間: 2025-3-27 18:52
AIM and Evolutionary Theory,f AI, at least in the fields of oncology and infectious disease, evolutionary theory must be brought to bear. In oncology, AI is uniquely suited to analyze the complex latticework of correlations among the many genomic and environmental influences that constitute cancer risk. It also makes possible 作者: 草本植物 時(shí)間: 2025-3-28 01:37
Artificial Intelligence in Evidence-Based Medicine,es. EBM is therefore a systematic approach to decision-making that integrates these three inputs. It involves evidence production (design and conducting of clinical studies), synthesis (collecting, appraising, and combining data to answer clinical questions), implementation (e.g., through clinical p作者: 進(jìn)步 時(shí)間: 2025-3-28 02:37 作者: 令人苦惱 時(shí)間: 2025-3-28 09:07 作者: 大方不好 時(shí)間: 2025-3-28 11:56
The New Frontiers of AI in Medicine,n to draw the potential future applications of AI that will change how care is delivered irrevocably. Techniques including machine learning, natural language processing, and computer vision will be applied to enable earlier diagnosis, give patient control, and create entirely new categories of diagn作者: Fluctuate 時(shí)間: 2025-3-28 17:26
AIM in Alternative Medicine,raditional Chinese medicine (TCM) now has been recognized as one of the main approaches of AM. As a clinical and evidence-driven discipline with long histories, AM is also heavily relied on in the utilization of big healthcare and therapeutic data for improving the capability of diagnosis and treatm作者: Hypomania 時(shí)間: 2025-3-28 20:58 作者: CHAFE 時(shí)間: 2025-3-29 00:49
,Ageing and Alzheimer’s Disease,earch studies from machine intelligence for computer vision, robotics, and natural language processing to more theoretical machine learning algorithm design and, recently, “deep learning” development. The application of AI in medical fields is booming, including the use of AI in data collection, ana作者: resistant 時(shí)間: 2025-3-29 06:06
Aim in Genomics,move into the realm of complex diseases, this approach fails to provide the insight needed to explain disease pathogenesis. Network medicine is a new paradigm that applies network science, artificial intelligence (in particular, machine learning and graph mining), and systems biology approaches to s作者: quiet-sleep 時(shí)間: 2025-3-29 07:47 作者: faction 時(shí)間: 2025-3-29 13:14 作者: 蚊帳 時(shí)間: 2025-3-29 19:31
AIM in Medical Informatics,ed to improve the medical analyses. We present the most common used algorithms in automatic medical diagnosis and the advance in explainability of machine learning-based systems to validate healthcare decision-making.作者: 疲勞 時(shí)間: 2025-3-29 20:02 作者: emission 時(shí)間: 2025-3-30 03:21
,AIM and the Patient’s Perspective,y and ensure that a greater public good is achieved and distributed fairly across society. Furthermore, developers need to ensure data privacy by design and to make use of innovative technologies being specifically developed for this purpose. AI technologists need to enter a meaningful and open dial作者: encyclopedia 時(shí)間: 2025-3-30 05:19
AIM and Evolutionary Theory,e genotypes to avoid the emergence of resistance. Advanced computational methods are also used in antimicrobial drug design and to anticipate outbreaks of infectious disease and the evolution of epidemics, such as the SARS-CoV-2 pandemic. In detailing these advances, we discuss illustrative examples作者: Mangle 時(shí)間: 2025-3-30 08:24
Artificial Intelligence in Evidence-Based Medicine,ng processes. AI can help engage patients and elicit values (e.g., chatbot-based decision aids) as well as provide coordinated care for patients with multimorbidities..However, improperly implementing AI can also exacerbate problems in EBM. For instance, if AI-enabled decision support systems fail t作者: comely 時(shí)間: 2025-3-30 14:41 作者: exercise 時(shí)間: 2025-3-30 19:57
Artificial Intelligence in Public Health,pect, rather than at the desired moment. Evidence-based policy would appear to be more legitimate and robust. AI could also change the way public health systems are organized at various levels. Learning healthcare systems, for example, are designed to adapt more or less autonomously to changing heal作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-30 21:21 作者: sphincter 時(shí)間: 2025-3-31 01:51 作者: hauteur 時(shí)間: 2025-3-31 06:54
Aim in Genomics,and structure of disease modules are largely unexplored. The purpose of this study is to systematically analyze the relationship between structural proximity of disease modules and categorical similarity of diseases, by aligning human-curated disease taxonomies with disease taxonomies automatically 作者: reflection 時(shí)間: 2025-3-31 10:54 作者: 以煙熏消毒 時(shí)間: 2025-3-31 13:36
https://doi.org/10.1007/978-0-387-92213-3ed to improve the medical analyses. We present the most common used algorithms in automatic medical diagnosis and the advance in explainability of machine learning-based systems to validate healthcare decision-making.作者: 使顯得不重要 時(shí)間: 2025-3-31 19:50
https://doi.org/10.1007/978-0-387-92213-3ence (AI)-based studies on account of their niche study considerations. As such, there has been a concerted effort to produce AI-specific extensions to preexisting instruments, such as CONSORT, SPIRIT, STARD, TRIPOD, QUADAS, and PROBAST. This chapter expands upon why AI-specific amendments to these 作者: colony 時(shí)間: 2025-3-31 23:20
https://doi.org/10.1007/978-94-007-4503-2y and ensure that a greater public good is achieved and distributed fairly across society. Furthermore, developers need to ensure data privacy by design and to make use of innovative technologies being specifically developed for this purpose. AI technologists need to enter a meaningful and open dial作者: 尖牙 時(shí)間: 2025-4-1 02:33
Ika Darnhofer,David Gibbon,Benoit Dedieue genotypes to avoid the emergence of resistance. Advanced computational methods are also used in antimicrobial drug design and to anticipate outbreaks of infectious disease and the evolution of epidemics, such as the SARS-CoV-2 pandemic. In detailing these advances, we discuss illustrative examples作者: 最有利 時(shí)間: 2025-4-1 08:46
