Smart Medical Image System: Patent Distribution, Market Trend, and Opportunity Analysis (Pre-order)

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    [id] => 1774342
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    [title] => Smart Medical Image System: Patent Distribution, Market Trend, and Opportunity Analysis (Pre-order)
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    [seo_url] => smart-medical-image-system-patent-distribution-market-trend-and-opportunity-analysis-pre-order
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    [meta_title] => Smart Medical Image System: Patent Distribution, Market Trend, and Opportunity Analysis (Pre-order)
    [keyword] => Smart Medical Image System: Patent Distribution, Market Trend, and Opportunity Analysis
    [description] => As the traditional medical image system requires doctors to manually identify and mark the pathological images one by one, the professional ability and judgment experience of the doctor directly affect
    [curr] => USD
    [category] => 1222
    [publisher] => 67
    [published_date] => 2019-08-01
    [country] => 183
    [author_name] => 
    [page_num] => 27
    [abstract] => 

As the traditional medical image system requires doctors to manually identify and mark the pathological images one by one, the professional ability and judgment experience of the doctor directly affect the medical diagnosis efficiency and the accuracy of the image recognition of the lesion. In view of this, scientists have combined artificial intelligence, image analysis, imaging medicine, and pathology images over the years to propose smart medical image system solutions to assist physicians in disease diagnosis in a more efficient manner. This report provides an overview of the components of the smart medical image system, key areas and strategies of several big names such as Siemens, Samsung Medison, IBM, GE Healthcare, and Philips Healthcare which possess US patents with regard to smart medical image systems, and examines their market trends and opportunities.

[table_of_contents] =>


1.Technology Introduction
2.Patent Analysis
2.1 Patent Mining
2.2 Patent Analysis
2.2.1 Analysis by Patent Field and Patent Application Year
2.2.2 Analysis by Patent Field and Assignee
2.2.3 Analysis by Patent Field and Enterprise & University Assignee
2.2.4 Analysis by Patent Technology and Patent Application Year
2.2.5 Analysis by Patent Technology and Country
2.2.6 Analysis by Patent Technology and Assignee
3.MIC Perspective
Appendix
Glossary of Terms
List of Companies
List of Tables
Table 1 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Patent Application Year
Table 2 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Country
Table 3 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Enterprise & University Assignee
Table 4 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Startup and Taiwanese Assignee
Table 5 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Patent Application Year
Table 6 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Country
Table 7 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Enterprise & University Assignee
Table 8 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Startup & Taiwanese Assignee
List of Figures
Figure 1 Composition Diagram of Dual Smart Medical Image Systems

[list_of_tables] =>

Overview ofsmart medical image systems and technologies
US smart medical image system patent counts by patent field, by application year, by country, by patent technology, and by assignee such as Siemens, Samsung Medison, IBM, GE Healthcare, and Philips Healthcare
Patent analysis from an industrial-analyticperspective

[list_of_figures] =>

AmCad Biomed Corporation
Arterys
Carestream Health
Case Western Reserve University
Columbia University
EBM Technologies
FDNA
GE Healthcare
Heartflow
Hypermed Imaging
IBM
iCAD
IDx
Johns Hopkins University
Leland Stanford Junior University
Merge Healthcare
Microsoft
National Central University
National Central University
Philips Healthcare
Riverain Technologies
Samsung Medison
Sectra
Siemens
Taihao Medical

[sample_page] => [brochure] => [new_price] => PDF-1800-Site Licence--Enterprise Wide Licence--Multi User Licence--Enterprise Wide Licence--Enterprise Wide Licence--Multi User Licence- )
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As the traditional medical image system requires doctors to manually identify and mark the pathological images one by one, the professional ability and judgment experience of the doctor directly affect the medical diagnosis efficiency and the accuracy of the image recognition of the lesion. In view of this, scientists have combined artificial intelligence, image analysis, imaging medicine, and pathology images over the years to propose smart medical image system solutions to assist physicians in disease diagnosis in a more efficient manner. This report provides an overview of the components of the smart medical image system, key areas and strategies of several big names such as Siemens, Samsung Medison, IBM, GE Healthcare, and Philips Healthcare which possess US patents with regard to smart medical image systems, and examines their market trends and opportunities.


1.Technology Introduction
2.Patent Analysis
2.1 Patent Mining
2.2 Patent Analysis
2.2.1 Analysis by Patent Field and Patent Application Year
2.2.2 Analysis by Patent Field and Assignee
2.2.3 Analysis by Patent Field and Enterprise & University Assignee
2.2.4 Analysis by Patent Technology and Patent Application Year
2.2.5 Analysis by Patent Technology and Country
2.2.6 Analysis by Patent Technology and Assignee
3.MIC Perspective
Appendix
Glossary of Terms
List of Companies
List of Tables
Table 1 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Patent Application Year
Table 2 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Country
Table 3 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Enterprise & University Assignee
Table 4 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Field and Startup and Taiwanese Assignee
Table 5 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Patent Application Year
Table 6 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Country
Table 7 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Enterprise & University Assignee
Table 8 Matrix Analysis of US Smart Medical Image System Patent Counts by Patent Technology and Startup & Taiwanese Assignee
List of Figures
Figure 1 Composition Diagram of Dual Smart Medical Image Systems


List Of Tables

Overview ofsmart medical image systems and technologies
US smart medical image system patent counts by patent field, by application year, by country, by patent technology, and by assignee such as Siemens, Samsung Medison, IBM, GE Healthcare, and Philips Healthcare
Patent analysis from an industrial-analyticperspective


List Of Figures

AmCad Biomed Corporation
Arterys
Carestream Health
Case Western Reserve University
Columbia University
EBM Technologies
FDNA
GE Healthcare
Heartflow
Hypermed Imaging
IBM
iCAD
IDx
Johns Hopkins University
Leland Stanford Junior University
Merge Healthcare
Microsoft
National Central University
National Central University
Philips Healthcare
Riverain Technologies
Samsung Medison
Sectra
Siemens
Taihao Medical


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