发布求助
文献互助 智能选刊 最新文献

Future Medicine AI最新文献

筛选
英文 中文
AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability 人工智能辅助癌症靶点优先排序，在新颖性、可信度和商业可操作性之间取得最佳平衡
Future Medicine AI Pub Date : 2024-03-04 DOI: 10.2217/fmai-2023-0019
Xi Long, Barbara Steurer, Chun Wai Wong, Ekaterina Kozlova, Vladimir Naumov, F. Pun, Alex Aliper, Fengzhi Ren, Alex Zhavoronkov
{"title":"AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability","authors":"Xi Long, Barbara Steurer, Chun Wai Wong, Ekaterina Kozlova, Vladimir Naumov, F. Pun, Alex Aliper, Fengzhi Ren, Alex Zhavoronkov","doi":"10.2217/fmai-2023-0019","DOIUrl":"https://doi.org/10.2217/fmai-2023-0019","url":null,"abstract":"The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"40 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266816","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
Generative AI for medical imaging analysis and applications 生成式AI用于医学影像分析和应用
Future Medicine AI Pub Date : 2023-09-06 DOI: 10.2217/fmai-2023-0004
Tanmai Sree Musalamadugu, Hemachandran Kannan
{"title":"Generative AI for medical imaging analysis and applications","authors":"Tanmai Sree Musalamadugu, Hemachandran Kannan","doi":"10.2217/fmai-2023-0004","DOIUrl":"https://doi.org/10.2217/fmai-2023-0004","url":null,"abstract":"Generative AI plays a pivotal role in medical imaging analysis, enabling precise diagnosis, treatment planning and disease monitoring. Techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) enhance medical imaging by generating synthetic images, improving reconstruction, segmentation and facilitating disease diagnosis and treatment planning. Nonetheless, ethical, legal and regulatory concerns arise regarding patient privacy, data protection and fairness. This paper offers an overview of generative AI in medical imaging analysis, highlighting applications, challenges and case studies. It compares results with traditional methods and examines potential implications on healthcare policies. The paper concludes with recommendations for responsible implementation and suggests future research and development directions.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115087387","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
The biote-bot hybrid. The ultimate biothreat merging nanobots, AI-enabled cybernetics and synthetic biology 生物机器人的混合体。终极生物威胁融合了纳米机器人，人工智能控制论和合成生物学
Future Medicine AI Pub Date : 2023-08-29 DOI: 10.2217/fmai-2023-0008
M. Kambouris, Y. Manoussopoulos, A. Velegraki, G. Patrinos
{"title":"The biote-bot hybrid. The ultimate biothreat merging nanobots, AI-enabled cybernetics and synthetic biology","authors":"M. Kambouris, Y. Manoussopoulos, A. Velegraki, G. Patrinos","doi":"10.2217/fmai-2023-0008","DOIUrl":"https://doi.org/10.2217/fmai-2023-0008","url":null,"abstract":"The paper intends to warn stakeholders, by using open sources, of the possibility of extremely small, nano-/pico-aerial vehicles controlled locally or remotely by artificial intelligence mindsets to deliver, on specific hosts and tissues, either diverse bioagents produced by conventional and synthetic (micro)biology, including xenobiota or bionic microbiota or existing microbiota selected from natural reservoirs. The accuracy in delivery would leverage minute quantities of pathogens to cause mass-scale bioevents. Such hybrids (biote-bots) would increase the effectiveness of unfit but virulent pathogens, preserve the carried biota for the trip and contain bioagents' weaponization footprint to levels below the detection threshold of current regimes, while complicating immune response and denying pre-infection detection and identification. To respond, we suggest that novel diagnostics and surveillance amenities are needed, prompting cooperation of experts from Medicine, medical instruments/diagnostics, artificial Intelligence and from disciplines tackling cybernetics, remote sensing, surveying and tracking.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115935991","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: 1
Augmentation of a multidisciplinary team meeting with a clinical decision support system to triage breast cancer patients in the United Kingdom 增加多学科小组会议与临床决策支持系统，以分流乳腺癌患者在英国
Future Medicine AI Pub Date : 2023-08-29 DOI: 10.2217/fmai-2023-0001
Martha Martin, H. Kristeleit, D. Ruta, C. Karampera, Rezzan Hekmat, W. Felix, Bertha InHout, A. Kothari, M. Kazmi, Lesedi Ledwaba-Chapman, A. Clery, Yanzhong Wang, B. Coker, A. Preininger, Roy Vergis, Tom Eggebraaten, Christopher T. Gloe, Irene Dankwa-Mullan Irene, G. Jackson, A. Rigg
{"title":"Augmentation of a multidisciplinary team meeting with a clinical decision support system to triage breast cancer patients in the United Kingdom","authors":"Martha Martin, H. Kristeleit, D. Ruta, C. Karampera, Rezzan Hekmat, W. Felix, Bertha InHout, A. Kothari, M. Kazmi, Lesedi Ledwaba-Chapman, A. Clery, Yanzhong Wang, B. Coker, A. Preininger, Roy Vergis, Tom Eggebraaten, Christopher T. Gloe, Irene Dankwa-Mullan Irene, G. Jackson, A. Rigg","doi":"10.2217/fmai-2023-0001","DOIUrl":"https://doi.org/10.2217/fmai-2023-0001","url":null,"abstract":"Aim: Multidisciplinary team (MDT) meetings struggle with increasing caseloads. Recent National Health Service (NHS) guidance proposes that patients are triaged for ‘no discussion at MDT’. We examine whether an artificial intelligence (AI)-based clinical decision-support system (CDSS) can support human triage. Methods: Local best practice breast cancer MDT treatment decisions were compared with treatment decisions made by: two, two-person MDT triage teams with and without the CDSS; the CDSS acting ‘alone’; and the historical MDT. A decision tree on whether to triage patients to the CDSS or the MDT was created using supervised learning algorithms. Results: When localized, the CDSS achieved high concordance with local best practice (treatment plan decisions: 92% CDSS vs 96% team 1 vs 92% team 2, not significant [NS]; treatment type decisions: 89% CDSS vs 93% team 1 vs 82% team 2, NS). Using a decision tree 40.2% of cases can be correctly triaged to the CDSS for a treatment plan, and 34.6% for treatment type recommendations. Conclusion: AI-enabled CDSSs can potentially reduce the clinical workload for a breast cancer MDT by up to 40%. Before routine deployment they need to be appropriately localized and validated in prospective studies to evaluate clinical effectiveness and economic impact.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132823631","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
The revolutionary impact of chatGPT: advances in biomedicine and redefining healthcare with large language models chatGPT的革命性影响:生物医学的进步和用大语言模型重新定义医疗保健
Future Medicine AI Pub Date : 2023-08-17 DOI: 10.2217/fmai-2023-0011
Manasi Soni, Pranav Anjaria, P. Koringa
{"title":"The revolutionary impact of chatGPT: advances in biomedicine and redefining healthcare with large language models","authors":"Manasi Soni, Pranav Anjaria, P. Koringa","doi":"10.2217/fmai-2023-0011","DOIUrl":"https://doi.org/10.2217/fmai-2023-0011","url":null,"abstract":"","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132607715","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
A clinician's guide to large language models 大型语言模型的临床医生指南
Future Medicine AI Pub Date : 2023-08-17 DOI: 10.2217/fmai-2023-0003
G. Briganti
{"title":"A clinician's guide to large language models","authors":"G. Briganti","doi":"10.2217/fmai-2023-0003","DOIUrl":"https://doi.org/10.2217/fmai-2023-0003","url":null,"abstract":"The rapid advancement of artificial intelligence (AI) has led to the emergence of large language models (LLMs) as powerful tools for various applications, including healthcare. These large-scale machine learning models, such as GPT and LLaMA have demonstrated potential for improving patient outcomes and transforming medical practice. However, healthcare professionals without a background in data science may find it challenging to understand and utilize these models effectively. This paper aims to provide an accessible introduction to LLMs for healthcare professionals, discussing their core concepts, relevant applications in healthcare, ethical considerations, challenges, and future directions. With an overview of LLMs, we foster a more collaborative future between healthcare professionals and data scientists, ultimately driving better patient care and medical advancements.","PeriodicalId":154874,"journal":{"name":"Future Medicine AI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772955","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信