Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Provectus
Healthcare organizations generate piles of documents and forms in different formats, making it difficult to achieve operational excellence and streamline business processes. Manual entry and OCR are no longer viable, and healthcare entities are looking for new solutions to handle documents.
In this presentation you can learn about:
- Healthcare document types and use cases
- IDP framework: building blocks for document processing solutions
- The document processing market landscape
- Methodology for solution evaluation: comparing apples to apples
Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
Big MDM Part 2: Using a Graph Database for MDM and Relationship ManagementCaserta
During this Big Data Warehousing Meetup, we discussed how graph databases work, shared some real world use cases, and showed a live demo of the world’s leading graph database, Neo4J. Pitney Bowes demonstrated their new MDM product developed on a graph database.
For more information, check out the other slides from this meetup or visit our website at www.casertaconcepts.com
Intelligent Document Processing (IDP) is a next-generation solution for extracting data from complex, unstructured documents. Unlike the technologies that came before it, IDP can handle document complexity and variation with the help of multiple AI technologies and machine learning.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Storyboarding for Data Visualization Designspatialhistory
This is derived from a lecture given by Frederico Freitas at the Spatial History Project / Center for Spatial and Textual Analysis at Stanford University. It describes how the process of storyboarding helps clarify design intent and facilitates design decision-making.
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Provectus
Healthcare organizations generate piles of documents and forms in different formats, making it difficult to achieve operational excellence and streamline business processes. Manual entry and OCR are no longer viable, and healthcare entities are looking for new solutions to handle documents.
In this presentation you can learn about:
- Healthcare document types and use cases
- IDP framework: building blocks for document processing solutions
- The document processing market landscape
- Methodology for solution evaluation: comparing apples to apples
Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
Big MDM Part 2: Using a Graph Database for MDM and Relationship ManagementCaserta
During this Big Data Warehousing Meetup, we discussed how graph databases work, shared some real world use cases, and showed a live demo of the world’s leading graph database, Neo4J. Pitney Bowes demonstrated their new MDM product developed on a graph database.
For more information, check out the other slides from this meetup or visit our website at www.casertaconcepts.com
Intelligent Document Processing (IDP) is a next-generation solution for extracting data from complex, unstructured documents. Unlike the technologies that came before it, IDP can handle document complexity and variation with the help of multiple AI technologies and machine learning.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Storyboarding for Data Visualization Designspatialhistory
This is derived from a lecture given by Frederico Freitas at the Spatial History Project / Center for Spatial and Textual Analysis at Stanford University. It describes how the process of storyboarding helps clarify design intent and facilitates design decision-making.
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
this slide is for brief introduction to the big data with little bit of fun through memes.
it is prepared with the articles from different websites about big data and some of my own words so it would be great if you like it
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
Top 8 Data Science Tools | Open Source Tools for Data Scientists | EdurekaEdureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
this slide is for brief introduction to the big data with little bit of fun through memes.
it is prepared with the articles from different websites about big data and some of my own words so it would be great if you like it
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
알파고의 작동 원리를 설명한 슬라이드입니다.
English version: http://www.slideshare.net/ShaneSeungwhanMoon/how-alphago-works
- 비전공자 분들을 위한 티저: 바둑 인공지능은 과연 어떻게 만들까요? 딥러닝 딥러닝 하는데 그게 뭘까요? 바둑 인공지능은 또 어디에 쓰일 수 있을까요?
- 전공자 분들을 위한 티저: 알파고의 main components는 재밌게도 CNN (Convolutional Neural Network), 그리고 30년 전부터 유행하던 Reinforcement learning framework와 MCTS (Monte Carlo Tree Search) 정도입니다. 새로울 게 없는 재료들이지만 적절히 활용하는 방법이 신선하네요.
"의료산업과 병원의 미래, 어떻게 그리고 계신가요?"
인공지능, 딥러닝, 빅데이터 분석 등 첨단기술의 바람이 의료계에도 불고 있습니다.
유튜브 생방송으로 진행되었던 본 웨비나는, IBM과 함께 헬스케어, 의료계가 준비해야할 로드맵을 탐색할 수 있는 기회였습니다. 자세한 내용이 더 궁금하시다면 eocho@kr.ibm.com으로 문의주세요!
기술은 곁에 있다. 시험관 아기들이 많다 보니 쌍둥이들이 많이 늘어났다. 디지털 트윈은 현실과 똑같은 디지털 쌍둥이를 만들어 더 나은 현실의 결과를 만들어 낸다. 수집한 다양한 정보를 가상에서 분석하고 최적화하는 방안을 도출하는 지능형 융합기술이다. NASA, GE가 먼저 제품 설계부터 공장 운영 감시, 작업량 예측, 생산 손실 예측, 고장 진단/예측 등에 활용했다. 제조업뿐 아니라 다양한 분야에 적용할 수 있다. 핵심기술은 모사, 관제, 모의, 연합, 자율의 단계로 진행된다. 보건의료 분야에도 인구 집단 모델링으로 헬스케어 서비스를 개선하고 의료진의 치료에 관련된 의사결정을 개선한다. 환자를 컴퓨터 모델링으로 치료법을 사전 테스트하여 최적화 후 실제 적용한다. 막대한 시간과 돈이 투자되는 신약 개발 임상 실험에도 적용하여 합병증과 위험 부담 줄일 수 있는 대안을 만들어 낸다. 국내외 사례도 담았다.
Slides used to deliver presentation on Korean healthcare system overview. Main topics are: payer, healthcare delivery system, regulation, stakeholders.
Key Success factors in digital healthcareChiweon Kim
The slides were used at Bio Korea 2016.
The topic is key success factors in digital healthcare and it deals with 3 topics: Utility, Business model, platform.
20151022 디지털 헬스케어 임상시험 임상시험글로벌선도센터 심포지엄 v4Chiweon Kim
2015년 10월 22일 분당서울대병원에서 열렸던
임상시험글로벌선도센터 공동 심포지엄 'Information Technology in Clinical Trials'에서 발표한 자료입니다.
제 블로그에 올렸던 글들을 정리한 내용입니다.
아래 글들을 참고하세요
http://www.chiweon.com/?p=2547
http://www.chiweon.com/?p=2578
http://www.chiweon.com/?p=2593
http://www.chiweon.com/?p=2639
16. 인공지능의 발전으로 기존의 의학지식을 넘어서는 발견이 이루어지
고 있음
• IBM 왓슨과 Medtronic의 협업으로
지속형 혈당 측정계를 사용해서 최대
3시간 전에 저혈당 발생을 예측할 수
있는 알고리즘을 개발했다고 발표함
• 현대 의학은 질병이 발생한 다음을
다루고 있기 때문에 그 전에 생기는 일
에 대한 지식이 부족하기 때문에 기존
에 이런 예측이 힘들었음
3. 의료적인 사실과 연관된 데이터 또는 알고리즘 찾기
20. 영역 회사
MedyMatch • 뇌 영상 판독
이미지
판독
• 망막, 폐결핵, 초음파 판독
• 영상 판독 (상세 불명)
• 심초음파 판독, 전용 기기 사용
• 유방 촬영, 次 CXR에서 폐암
• 흉부 방사선 판독
• 암 영상 판독
• 암 영상 판독
• 폐결핵, 유방 촬영, 병리
• 심초음파 판독, 전용 기기 사용
의료 인공지능 회사와 적용 대상 1/3
적용 대상분야
21. • 혈액 분석, 기생충
• 성장판 나이, 흉부 CT 등
• 유방 촬영
• 1차 판독과 핵심 이미지 제시
• 심장 혈류량 추정. FDA 510(K)
• 골밀도, 지방간, 폐기종 지표 정량화
• 무릎 2D 이미지를 3D 전환 FDA 510(K)
• 이미지 프로세싱
• ? 의료 영상 딥러닝 플랫폼?
의료 인공지능 회사와 적용 대상 2/3
• 용어 인식 (예: ‘고혈압’ 단어 인식)
이미지
판독정량화
이미지
처리
기타
용어영역 회사 적용 대상분야
22. 의료 인공지능 회사와 적용 대상 3/3
• Drug discovery
• novel small molecule discovery
• Drug discovery
• Drug discovery
• 암 liquid biopsy
• 유전자 변이 분석, 유전체 기반 치료제 개발
• 데이터 종합, 자동화
• 데이터 종합, 자동화
• 영상+환자 정보로 위험 판별
• 데이터 종합, 자동화
신약개발데이터분석유전자영역 회사 적용 대상분야
23. 강의 내용
인공지능 적용 분야
의료 서비스의 특징과 인공지능의 효용
인공지능 비즈니스 모델
인공지능을 위한 빅데이터
24. 의료에서 인공지능의 효용
효용
생산성 향상
의료의 질 향상
새로운 지식
• 왓슨 oncology와 항암 전문의
• 영상 판독과 영상의학과 전문의
• 일차 진료 의사 대상인 경우
• 최고 수준 전문가들의 수준을 높이기 보다는 일반 의사들의
능력 향상 효과
• 기존 의학 교과서에 없는 지식 발견
25. 의료 영상의 특징과 인공지능 적용 효용
영상의 특징
판독 오류 시
영향
X-ray
학습 데이터 량
CT, MRI
• 그림자를 보는 것
• 이상을 놓칠 가능성 높음
• 타과 의사 판독 비율 높음
• 직접 보는 것
• 영상의학과 전문의 판독 비율 높음
• 일반적인 질환은 진단을 놓쳐도 큰 문제
되지 않음
• 증상 없는 암은 문제가 됨
• 오류 가능성이 적으나 오류 발생 시 문제
발생 가능성 큼
• 생산성 향상이 목표인 경우 정상만 걸러도 도움이 됨
• 의료의 질 향상이 목표인 경우 정상을 거르는 것만으로 부족할 수 있음
26. 의료 영상 판독에서 필요한 인공지능의 효용과 특성
의료의 질 향상생산성 향상
주된 효용
해당 분야
데이터 특성
• 해당 분야 전문가가 더 빠르게 일할
수 있도록 (더 적은 사람으로?)
• 정상 이미지를 걸러줌
• 영상의학전문의 CT, MRI 판독
• 소화기전문의 캡슐 내시경 판독
• 뇌파, 근전도, 심전도 판독
• 정상 여부 판독의 경우 정상 VS
비정상으로 필요한 데이터 적음 (예:
군대 신체 검사)
• 일차 진료 의사가 수준 높은 진료를
제공할 수 있도록
• 일차진료의사 X-ray 판독
• 유방 촬영 판독 (오진 시 문제가 큰
암 스크리닝)
• 질환 진단이 필요한 경우 필요한
데이터 많음
• 단 특정 질환 여부를 보는 것이
필요한 경우 필요한 데이터 적음 (예:
결핵협회에서 결핵 판독)
27. 의사가 필요로 하는 용도는 판독인가?
초음파는 시술자가 제대로
실시하는 것이 중요하나
검사 방법을 교육받기
힘든 경우가 있음. 진단
보다는 제대로 검사를
실시할 수 있도록
도와주는 인공지능의
가치가 판독보다 높을 수
있음
28. 강의 내용
인공지능 적용 분야
의료 서비스의 특징과 인공지능의 효용
인공지능 비즈니스 모델
인공지능을 위한 빅데이터
29. 보험 적용: 유방 촬영 판독에서 컴퓨터 보조 진단에 대한 수가 적용
유방 촬영에 대한 Computed-Aided-Detection (CAD)
• 유방암 선별/1차 진단 검사인 유방 촬영 판독 시 자동화된 판독 시스템 이용
• 메디케어에서 CAD 사용에 대해 추가 수가를 지불함 (2002년)
• 의료의 질 향상과 관련될 가능성 높음
• (인공지능 여부를 떠나서) CAD에 대해 수가가 인정된 예외적인 사례
• 한국에서 수가 인정을 받을 수 있을것인가?
30. 보험 적용: 생산성 향상과 관련되는 경우
수가 하락이 CAD에 영향 줄 수 있음
• 의료 영상 판독 시 영상의학과 전문의가
판독하면 10% 가산 (판독료) 지급
• 단순 엑스레이에 대해 판독료 폐지 검토
• 만약 폐지되면 단순 엑스레이 판독에 대해서
병원들은 (충분히 가격이 싸다는 전제 하에)
인공지능을 도입하고 타과 전문읟르이
판독하도록 할 가능성 있음
• 즉, 수가를 새로 만드는 것 뿐 아니라 기존
수가의 변화가 인공지능과 같은 CAD 도입을
촉진할 수 있음 수가가 인정된 예외적인 사례
• 한국에서 수가 인정을 받을 수 있을것인가?
32. 인공지능 비즈니스 모델: 병원에서의 도입
• J&J는 수면 마취 관리 시스템인 Sedasys를 개발해서 출시함
• 건강한 사람의 내시경 시, (마취과 의사가 아닌 전문 간호사 등의) 사람이 옆에 대기하
고, 근처에 마취과 의사가 상주한다는 전제하에 FDA 허가를 취득함
• 인건비가 비싼 마취과 의사 없이도 수면 마취를 할 수 있어 마취 비용을 절감할 수 있
다는 것을 내세움
• 의미있는 사업 성과를 거두지 못하고 사업 종료함
– 기기의 안정성 문제를 제기하는 곳이 있으나 J&J는 안정성 이슈는 없다고 반박
– 마취과 의사들의 반발 때문에 시장에 안착하지 못했다는 의견 있음 (‘절대 甲‘)
• 마취과 의사들의 반발이 중요한 이슈였을까?
– 병원 입장에서 마취 비용을 절감하는 것은 크게 중요하지 않을 수 있음
– 행위별 수가제에서 마취에 들어가는 비용을 환자나 보험에 전가하면 그만임
– 따라서 병원 입장에서 적극적으로 Sedasys를 도입할 인센티브가 없음
33. 강의 내용
인공지능 적용 분야
의료 서비스의 특징과 인공지능의 효용
인공지능 비즈니스 모델
인공지능을 위한 빅데이터
34. IBM은 인공지능의 영역을 확대시키기 위해 다양한 의료 데이터 회
사를 인수함
Explorys: 5천만 명의 임상 데이터 보유 Phythel: Population health
management 회사
Merge: 클라우드 기반 PACS 회사
35. 질병 발생의 예측이 가능해 질 것임
5분 뒤에 심근경색이
생길 것이니 지금
응급실로 가세요
40. 언더아머의 디지털 전략 ‘Connected fitness’ 진행 방향
’11.2
E39 출시
MapMyFitness 인수
($ 150 Mil)
Endomondo 인수
($85 Mil)
MyFitnessPal 인수
($475 Mil)
Record 앱 출시
HealthBox 공개
Gemini 2 RE 공개
’13.3 ’15.1 ’15.2 ’16.1’15.7
Gritness 인수
($? Mil)
’13.11
Armour 39 출시
2015년
매출 $3.96 Bil, 영업이익 $409 Mil
41. 언더아머 Connected fitness 현황
• 계획된 전체 모습 달성: “I think we filled in a puzzle to a certain extent.”
• Largest digital health and fitness community with > 150 million users and
growing more than 100,000 every single day
• Revenue: $25~30 Mil (Endomondo + MyFitnessPal)
• Digital team
• 팀원: 440명 (2015년 9월 기준)
• 60명 (2013년) → 200명 (2014년)
• “even the biggest technology companies don't have a team this big
working on it”
42. 언더아머의 데이터 수집
• 데이터의 의미
– Ultimately, what is this? It is a massive consumer insight engine
– Data is one of the key ingredients that we see in each one of these businesses
for us as we drive. …. will actually help us drive making those great decisions.
• 수집된 데이터 사례
– (‘15.9월까지) 1년간 식사 기록 60억 건. 운동 활동 13억 건 (달리기 2억 건 포함)
– Gear tracker 사용자 60~70만명 (어떤 러닝화를 쓰는 지, 언제 교체 했는지 기록)
– Gone are the days where you bring eight runners in.
• 데이터 적용 분야
– 제품 개발
– 교체 주기 파악
– 사용자 운동 능력 향상
43. 언더아머의 데이터 수집 (1): 제품 개발
• Will help us make better business decisions that will inform us about our athletes to
build better products that are more on time with them
• 데이터 사례
– Should men and women wear the same running shoes?
– Average run: 3.1 miles
– 여성 특성
• 달리는 빈도가 더 높음
• 화요일에 더 많이 달림
• 날씨에 덜 신경 씀
– 남성 특성: 여성 보다 마라톤을 두배 더 많이 뜀
44. 언더아머의 데이터 수집 (2): 교체 주기 파악
• 데이터 사례
– 더 많이 운동하는 사람이 더 많은 장비를 산다.
– 달리기 하는 사람들이 신발을 바꾸는 이유?
• 뛰는 거리를 늘리면서 신발 브랜드를 자주 바꾼다
• 데이터로 무엇을 할 수 있는가?
– 300마일, 400마일을 뛸 때 무슨 일이 생기는 지를 파악해서 제안을 할 수 있음
– We can track the wearability, lifespan and gain insights on most importantly
what our core mission is, is to make an athlete better by making better product.
• This creates an opportunity to connect with her during the transition points in her
life to become the brand of choice
45. 언더아머의 데이터 수집 (3): 운동 능력 향상
• 운동 능력 향상과 관련된 데이터
– 지속적으로 측정한 데이터
• 수면과 운동량과의 관계
• 수면과 걸음걸이와의 관계
– 현재 상태에 대한 데이터
• 피곤한 상태인가?
• 운동을 과도하게 한 상태인가?
• 데이터로 할 수 있는 것
– 현재 상태에 대한 피드백
– Predictive analysis가 가능해 질 것임
– 개인별 맞춤 조언을 제공할 수 있음
• 과거 선수들이 수십만 달러를 내고 받을 수 있던 분석을 무료로 받을 수 있음
• 프리미엄 구독 솔루션을 통해 개인별 트레이닝 계획을 짜주고 있음
46. 인공지능 학습을 위한 데이터
인공지능 학습을 위한 양질의 의료 데이터 확보
인공지능 학습의 특징
• 지도 학습: 전문가가
제시하는 ‘정답’이 필요
• 약한 지도 학습, 비지도
학습도 있으나 부족한
경우가 많음
• 현재까지 인공지능을
의료에 적용하는 연구는
의사가 제시하는 진단을
기준으로 학습한 결과임
• 따라서 결과가 정리된
데이터가 중오
정답이 없는
경우
정답이 있는
경우
기타
• 전문가들 간의 합의를
정답으로 간주
• 예: 당뇨성 망막병증
• 어느 정도의 합의가 정답?
• 병리 등 더욱 엄격한 검사
결과를 정답으로 간주
• 예: 피부암 사진 판독 시 병리
조직 검사 결과를 정답으로
• 더욱 엄격한 검사 판독에서
이견이 있다면?
• 확진된 환자의 데이터를
종합 관리