Implementation of Personalized Medicine by Artificial Intelligence Platform

被引：1

作者：

Yakimenko, Yurii ^{[1
]}

Stirenko, Sergii ^{[1
]}

Koroliouk, Dimitri ^{[1
]}

Gordienko, Yuri ^{[1
]}

Zanzotto, Fabio Massimo ^{[1
]}

机构：

[1] Natl Tech Univ Ukraine, Igor Sikorsky Kyiv Polytech Inst, Kiev, Ukraine

来源：

SOFT COMPUTING FOR SECURITY APPLICATIONS, ICSCS 2022 | 2023年 / 1428卷

关键词：

Artificial Intelligence (AI); Computer-Aided Detection (CADe); Computer-Aided Diagnosis (CADx); Deep learning; Healthcare;

D O I：

10.1007/978-981-19-3590-9_46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Artificial intelligence (AI) can automate and dramatically accelerate Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) by automatically processing medical data without the involvement of medical personnel on the screening stage and making it available on a regular basis nationwide. In this position paper, some our recent approaches are reviewed with the proposition to integrate them as a set of modern advanced medical services under the conditional name proactive "AI-based platform" (AIP). The main motive is to use the methods of AI, peripheral intelligence (Edge Intelligence-EI), the Internet of Things (IoT), wearable electronics (WE), and Big Data technologies (BDT). The additional promising way of the further development of AIP should include AI-based personalized medicine (AIPM) that can allow practitioners to find cures tuned for patients. AI-based personalized medicine promises to be transformative for stakeholders involved in the complex diseases. However, clinicians do not understand their suggestions and decisions. That is why the current real challenge is to build AI-based personalized medicine that can be accepted by clinical community. The current EU-funded project "knowledge at the tips of your figures (KATY)" is presented shortly which grasps the above challenge and proposes an AIPM approach that can bring medical CADe/CADx to the tips of the fingers of clinical community.

引用

页码：597 / 611

页数：15

共 38 条

[1]

Alienin O., 2022, ARTIFICIAL INTELLIGE

[2]

[Anonymous], 2019, MED FUTURIST

[3] Evaluating diagnostic content of AI-generated radiology reports of chest X-rays [J].

Babar, Zaheer ;

van Laarhoven, Twan ;

Zanzotto, Fabio Massimo ;

Marchiori, Elena .

ARTIFICIAL INTELLIGENCE IN MEDICINE, 2021, 116 (116)

[4]

Chen Y.-W., 2020, Deep Learning in Healthcare

[5]

Doms Volodymyr, 2021, Advances in Artificial Systems for Logistics Engineering. Lecture Notes on Data Engineering and Communication Technologies (82), P131, DOI 10.1007/978-3-030-80475-6_13

[6] A guide to deep learning in healthcare [J].

Esteva, Andre ;

Robicquet, Alexandre ;

Ramsundar, Bharath ;

Kuleshov, Volodymyr ;

DePristo, Mark ;

Chou, Katherine ;

Cui, Claire ;

Corrado, Greg ;

Thrun, Sebastian ;

Dean, Jeff .

NATURE MEDICINE, 2019, 25 (01) :24-29

[7] Effect of Data Augmentation and Lung Mask Segmentation for Automated Chest Radiograph Interpretation of Some Lung Diseases [J].

Gang, Peng ;

Zeng, Wei ;

Gordienko, Yuri ;

Kochura, Yuriy ;

Alienin, Oleg ;

Rokovyi, Oleksandr ;

Stirenko, Sergii .

NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV, 2019, 1142 :333-340

[8]

Gang P, 2019, 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), P557, DOI [10.1109/SSCI44817.2019.9002970, 10.1109/ssci44817.2019.9002970]

[9]

Gordienko N, 2015, 2015 8TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), P193, DOI 10.1109/MIPRO.2015.7160263

[10]

Gordienko Yuri, 2021, Advances in Artificial Systems for Logistics Engineering. Lecture Notes on Data Engineering and Communications Technologies (82), P3, DOI 10.1007/978-3-030-80475-6_1

← 1 2 3 4 →