인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)

디지털 헬스케어 연구소 소장

성균관대 디지털헬스학과 초빙교수

최윤섭, PhD
인공지능은 의료를 어떻게 혁신하는가

“It's in Apple's DNA that technology alone is not enough.  
It's technology married with liberal arts.”

The Convergence of IT, BT and Medicine

최윤섭 지음
의료인공지능
표지디자인•최승협
컴퓨터
털 헬
치를 만드는 것을 화두로
기업가, 엔젤투자가, 에반
의 대표적인 전문가로, 활
이 분야를 처음 소개한 장
포항공과대학교에서 컴
동 대학원 시스템생명공
취득하였다. 스탠퍼드대
조교수, KT 종합기술원 컨
구원 연구조교수 등을 거
저널에 10여 편의 논문을
국내 최초로 디지털 헬스
윤섭 디지털 헬스케어 연
국내 유일의 헬스케어 스
어 파트너스’의 공동 창업
스타트업을 의료 전문가
관대학교 디지털헬스학과
뷰노, 직토, 3billion, 서지
소울링, 메디히어, 모바일
자문을 맡아 한국에서도
고 있다. 국내 최초의 디
케어 이노베이션』에 활발
을 연재하고 있다. 저서로
와 『그렇게 나는 스스로
•블로그_ http://www
•페이스북_ https://w
•이메일_ yoonsup.c
최윤섭
의료 인공지능은 보수적인 의료 시스템을 재편할 혁신을 일으키고 있다. 의료 인공지능의 빠른 발전과
광범위한 영향은 전문화, 세분화되며 발전해 온 현대 의료 전문가들이 이해하기가 어려우며, 어디서부
터 공부해야 할지도 막연하다. 이런 상황에서 의료 인공지능의 개념과 적용, 그리고 의사와의 관계를 쉽
게 풀어내는 이 책은 좋은 길라잡이가 될 것이다. 특히 미래의 주역이 될 의학도와 젊은 의료인에게 유용
한 소개서이다.
━ 서준범, 서울아산병원 영상의학과 교수, 의료영상인공지능사업단장
인공지능이 의료의 패러다임을 크게 바꿀 것이라는 것에 동의하지 않는 사람은 거의 없다. 하지만 인공
지능이 처리해야 할 의료의 난제는 많으며 그 해결 방안도 천차만별이다. 흔히 생각하는 만병통치약 같
은 의료 인공지능은 존재하지 않는다. 이 책은 다양한 의료 인공지능의 개발, 활용 및 가능성을 균형 있
게 분석하고 있다. 인공지능을 도입하려는 의료인, 생소한 의료 영역에 도전할 인공지능 연구자 모두에
게 일독을 권한다.
━ 정지훈, 경희사이버대 미디어커뮤니케이션학과 선임강의교수, 의사
서울의대 기초의학교육을 책임지고 있는 교수의 입장에서, 산업화 이후 변하지 않은 현재의 의학 교육
으로는 격변하는 인공지능 시대에 의대생을 대비시키지 못한다는 한계를 절실히 느낀다. 저와 함께 의
대 인공지능 교육을 개척하고 있는 최윤섭 소장의 전문적 분석과 미래 지향적 안목이 담긴 책이다. 인공
지능이라는 미래를 대비할 의대생과 교수, 그리고 의대 진학을 고민하는 학생과 학부모에게 추천한다.
━ 최형진, 서울대학교 의과대학 해부학교실 교수, 내과 전문의
최근 의료 인공지능의 도입에 대해서 극단적인 시각과 태도가 공존하고 있다. 이 책은 다양한 사례와 깊
은 통찰을 통해 의료 인공지능의 현황과 미래에 대해 균형적인 시각을 제공하여, 인공지능이 의료에 본
격적으로 도입되기 위한 토론의 장을 마련한다. 의료 인공지능이 일상화된 10년 후 돌아보았을 때, 이 책
이 그런 시대를 이끄는 길라잡이 역할을 하였음을 확인할 수 있기를 기대한다.
━ 정규환, 뷰노 CTO
의료 인공지능은 다른 분야 인공지능보다 더 본질적인 이해가 필요하다. 단순히 인간의 일을 대신하는
수준을 넘어 의학의 패러다임을 데이터 기반으로 변화시키기 때문이다. 따라서 인공지능을 균형있게 이
해하고, 어떻게 의사와 환자에게 도움을 줄 수 있을지 깊은 고민이 필요하다. 세계적으로 일어나고 있는
이러한 노력의 결과물을 집대성한 이 책이 반가운 이유다.
━ 백승욱, 루닛 대표
의료 인공지능의 최신 동향뿐만 아니라, 의의와 한계, 전망, 그리고 다양한 생각거리까지 주는 책이다.
논쟁이 되는 여러 이슈에 대해서도 저자는 자신의 시각을 명확한 근거에 기반하여 설득력 있게 제시하
고 있다. 개인적으로는 이 책을 대학원 수업 교재로 활용하려 한다.
━ 신수용, 성균관대학교 디지털헬스학과 교수
최윤섭지음
의료인공지능
값 20,000원
ISBN 979-11-86269-99-2
최초의 책!
계 안팎에서 제기
고 있다. 현재 의
분 커버했다고 자
것인가, 어느 진료
제하고 효용과 안
누가 지는가, 의학
쉬운 언어로 깊이
들이 의료 인공지
적인 용어를 최대
서 다른 곳에서 접
를 접하게 될 것
너무나 빨리 발전
책에서 제시하는
술을 공부하며, 앞
란다.
의사 면허를 취득
저가 도움되면 좋
를 불러일으킬 것
화를 일으킬 수도
슈에 제대로 대응
분은 의학 교육의
예비 의사들은 샌
지능과 함께하는
레이닝 방식도 이
전에 진료실과 수
겠지만, 여러분들
도생하는 수밖에
미래의료학자 최윤섭 박사가 제시하는
의료 인공지능의 현재와 미래
의료 딥러닝과 IBM 왓슨의 현주소
인공지능은 의사를 대체하는가
값 20,000원
ISBN 979-11-86269-99-2
레이닝 방식도 이
전에 진료실과 수
겠지만, 여러분들
도생하는 수밖에
소울링, 메디히어, 모바일
자문을 맡아 한국에서도
고 있다. 국내 최초의 디
케어 이노베이션』에 활발
을 연재하고 있다. 저서로
와 『그렇게 나는 스스로
•블로그_ http://www
•페이스북_ https://w
•이메일_ yoonsup.c

의료 인공지능
•1부: 제 2의 기계시대와 의료 인공지능

•2부: 의료 인공지능의 과거와 현재

•3부: 미래를 어떻게 맞이할 것인가

대한영상의학회 춘계학술대회 2017.6

Vinod Khosla
Founder, 1st CEO of Sun Microsystems
Partner of KPCB, CEO of KhoslaVentures
LegendaryVenture Capitalist in SiliconValley

“Technology will replace 80% of doctors”

https://www.youtube.com/watch?time_continue=70&v=2HMPRXstSvQ
“영상의학과 전문의를 양성하는 것을 당장 그만둬야 한다.
5년 안에 딥러닝이 영상의학과 전문의를 능가할 것은 자명하다.”
Hinton on Radiology

•AP 통신: 로봇이 인간 대신 기사를 작성

•초당 2,000 개의 기사 작성 가능

•기존에 300개 기업의 실적 ➞ 3,000 개 기업을 커버

• 1978
• As part of the obscure task of “discovery” —
providing documents relevant to a lawsuit — the
studios examined six million documents at a
cost of more than $2.2 million, much of it to pay
for a platoon of lawyers and paralegals who
worked for months at high hourly rates.
• 2011
• Now, thanks to advances in artiﬁcial intelligence,
“e-discovery” software can analyze documents
in a fraction of the time for a fraction of the
cost.
• In January, for example, Blackstone Discovery of
Palo Alto, Calif., helped analyze 1.5 million
documents for less than $100,000.

“At its height back in 2000, the U.S. cash equities trading desk at
Goldman Sachs’s New York headquarters employed 600 traders,
buying and selling stock on the orders of the investment bank’s
large clients. Today there are just two equity traders left”

•일본의 Fukoku 생명보험에서는 보험금 지급 여부를 심사하
는 사람을 30명 이상 해고하고, IBM Watson Explorer 에게
맡기기로 결정

•의료 기록을 바탕으로 Watson이 보험금 지급 여부를 판단

•인공지능으로 교체하여 생산성을 30% 향상

•2년 안에 ROI 가 나올 것이라고 예상

•1년차: 140m yen

•2년차: 200m yen

No choice but to bring AI into the medicine

Martin Duggan,“IBM Watson Health - Integrated Care & the Evolution to Cognitive Computing”

•약한 인공 지능 (Artificial Narrow Intelligence)

• 특정 방면에서 잘하는 인공지능

• 체스, 퀴즈, 메일 필터링, 상품 추천, 자율 운전

•강한 인공 지능 (Artificial General Intelligence)

• 모든 방면에서 인간 급의 인공 지능

• 사고, 계획, 문제해결, 추상화, 복잡한 개념 학습

•초 인공 지능 (Artificial Super Intelligence)

• 과학기술, 사회적 능력 등 모든 영역에서 인간보다 뛰어난 인공 지능

• “충분히 발달한 과학은 마법과 구분할 수 없다” - 아서 C. 클라크

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
90%
50%
10%
PT-AI
AGI
EETNTOP100 Combined
언제쯤 기계가 인간 수준의 지능을 획득할 것인가?
Philosophy and Theory of AI (2011)
Artiﬁcial General Intelligence (2012)
Greek Association for Artiﬁcial Intelligence
Survey of most frequently cited 100 authors (2013)
Combined
응답자
누적 비율
Superintelligence, Nick Bostrom (2014)

Superintelligence: Science of ﬁction?
Panelists: Elon Musk (Tesla, SpaceX), Bart Selman (Cornell), Ray Kurzweil (Google),
David Chalmers (NYU), Nick Bostrom(FHI), Demis Hassabis (Deep Mind), Stuart
Russell (Berkeley), Sam Harris, and Jaan Tallinn (CSER/FLI)
January 6-8, 2017, Asilomar, CA
https://brunch.co.kr/@kakao-it/49
https://www.youtube.com/watch?v=h0962biiZa4

Superintelligence: Science of ﬁction?
Panelists: Elon Musk (Tesla, SpaceX), Bart Selman (Cornell), Ray Kurzweil (Google),
David Chalmers (NYU), Nick Bostrom(FHI), Demis Hassabis (Deep Mind), Stuart
Russell (Berkeley), Sam Harris, and Jaan Tallinn (CSER/FLI)
January 6-8, 2017, Asilomar, CA
Q: 초인공지능이란 영역은 도달 가능한 것인가?
Q: 초지능을 가진 개체의 출현이 가능할 것이라고 생각하는가?
Table 1
Elon Musk Start Russell Bart Selman Ray Kurzweil David Chalmers Nick Bostrom DemisHassabis Sam Harris Jaan Tallinn
YES YES YES YES YES YES YES YES YES
Table 1-1
YES YES YES YES YES YES YES YES YES
Q: 초지능의 실현이 일어나기를 희망하는가?
Table 1-1-1
Complicated Complicated Complicated YES Complicated YES YES Complicated Complicated
https://brunch.co.kr/@kakao-it/49
https://www.youtube.com/watch?v=h0962biiZa4

http://waitbutwhy.com/2015/01/artiﬁcial-intelligence-revolution-2.html

•복잡한 의료 데이터의 분석 및 insight 도출

•영상 의료/병리 데이터의 분석/판독

•연속 데이터의 모니터링 및 예방/예측
의료 인공지능의 세 유형

Jeopardy!
2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지

600,000 pieces of medical evidence
2 million pages of text from 42 medical journals and clinical trials
69 guidelines, 61,540 clinical trials
IBM Watson on Medicine
Watson learned...
+
1,500 lung cancer cases
physician notes, lab results and clinical research
+
14,700 hours of hands-on training

메이요 클리닉 협력

(임상 시험 매칭)
전남대병원

도입
인도 마니팔 병원

WFO 도입
식약처 인공지능

가이드라인 초안
메드트로닉과

혈당관리 앱 시연
2011 2012 2013 2014 2015
뉴욕 MSK암센터 협력

(폐암)
MD앤더슨 협력

(백혈병)
MD앤더슨

파일럿 결과 발표

@ASCO
왓슨 펀드,

웰톡에 투자
뉴욕게놈센터 협력

(교모세포종 분석)
GeneMD,

왓슨 모바일 디벨로퍼

챌린지 우승
클리블랜드 클리닉 협력

(암 유전체 분석)
한국 IBM

왓슨 사업부 신설
Watson Health 출범
피텔, 익스플로리스 인수

J&J, 애플, 메드트로닉 협력
에픽 시스템즈, 메이요클리닉

제휴 (EHR 분석)
동경대 도입

( WFO)
왓슨 펀드,

모더나이징 메디슨

투자
학계/의료계
산업계
패쓰웨이 지노믹스 OME

클로즈드 알파 서비스 시작
트루븐 헬스

인수
애플 리서치 키트

통한 수면 연구 시작
2017
가천대

길병원

도입
메드트로닉

Sugar.IQ 출시
제약사

테바와 제휴
태국 범룽랏 국제 병원,

WFO 도입
머지

헬스케어

인수
2016
언더 아머 제휴
브로드연구소협력발표(유
전체 분석-항암제 내성)
마니팔 병원의  
WFO 정확성 발표
대구가톨릭병원

대구동산병원  
도입
부산대병원

도입
왓슨 펀드,

패쓰웨이 지노믹스

투자
제퍼디! 우승
조선대병원

도입
한국 왓슨

컨소시움 출범
쥬피터  
메디컬  
센터

도입

가이드라인
메이요 클리닉

임상시험매칭

결과발표
2018
건양대병원

도입
IBM Watson Health Chronicle
WFO

최초 논문

메이요 클리닉 협력

(임상 시험 매칭)
전남대병원

도입
인도 마니팔 병원

WFO 도입

가이드라인 초안
메드트로닉과

혈당관리 앱 시연
2011 2012 2013 2014 2015
뉴욕 MSK암센터 협력

(폐암)
MD앤더슨 협력

(백혈병)
MD앤더슨

파일럿 결과 발표

@ASCO
왓슨 펀드,

웰톡에 투자
뉴욕게놈센터 협력

(교모세포종 분석)
GeneMD,

왓슨 모바일 디벨로퍼

챌린지 우승
클리블랜드 클리닉 협력

(암 유전체 분석)
한국 IBM

왓슨 사업부 신설
Watson Health 출범
피텔, 익스플로리스 인수

J&J, 애플, 메드트로닉 협력
에픽 시스템즈, 메이요클리닉

제휴 (EHR 분석)
동경대 도입

( WFO)
왓슨 펀드,

모더나이징 메디슨

투자
학계/의료계
산업계
패쓰웨이 지노믹스 OME

클로즈드 알파 서비스 시작
트루븐 헬스

인수
애플 리서치 키트

통한 수면 연구 시작
2017
가천대

길병원

도입
메드트로닉

Sugar.IQ 출시
제약사

테바와 제휴
태국 범룽랏 국제 병원,

WFO 도입
머지

헬스케어

인수
2016
언더 아머 제휴
브로드연구소협력발표(유
전체 분석-항암제 내성)
마니팔 병원의  
WFO 정확성 발표
부산대병원

도입
왓슨 펀드,

패쓰웨이 지노믹스

투자
제퍼디! 우승
조선대병원

도입
한국 왓슨

컨소시움 출범
쥬피터  
메디컬  
센터

도입

가이드라인
메이요 클리닉

임상시험매칭

결과발표
2018
건양대병원

도입
IBM Watson Health Chronicle
WFO

최초 논문
대구가톨릭병원

대구동산병원  
도입

Annals of Oncology (2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601
Validation study to assess performance of IBM cognitive
computing system Watson for oncology with Manipal
multidisciplinary tumour board for 1000 consecutive cases:  
An Indian experience
•인도 마니팔 병원의 1,000명의 암환자 에 대해 의사와 WFO의 권고안의 ‘일치율’을 비교

•유방암 638명, 대장암 126명, 직장암 124명, 폐암 112명

•의사-왓슨 일치율

•추천(50%), 고려(28%), 비추천(17%)

•의사의 진료안 중 5%는 왓슨의 권고안으로 제시되지 않음

•일치율이 암의 종류마다 달랐음

•직장암(85%), 폐암(17.8%)

•삼중음성 유방암(67.9%), HER2 음성 유방암 (35%)

San Antonio Breast Cancer Symposium—December 6-10, 2016
Concordance WFO (@T2) and MMDT (@T1* v. T2**)
(N= 638 Breast Cancer Cases)
Time Point
/Concordance
REC REC + FC
n % n %
T1* 296 46 463 73
T2** 381 60 574 90
This presentation is the intellectual property of the author/presenter.Contact somusp@yahoo.com for permission to reprint and/or distribute.26
* T1 Time of original treatment decision by MMDT in the past (last 1-3 years)
** T2 Time (2016) of WFO’s treatment advice and of MMDT’s treatment decision upon blinded re-review of non-concordant
cases

WFO in ASCO 2017
• Early experience with IBM WFO cognitive computing system for lung  
 
and colorectal cancer treatment (마니팔 병원) 
• 지난 3년간: lung cancer(112), colon cancer(126), rectum cancer(124)
• lung cancer: localized 88.9%, meta 97.9%
• colon cancer: localized 85.5%, meta 76.6%
• rectum cancer: localized 96.8%, meta 80.6%
Performance of WFO in India
2017 ASCO annual Meeting, J Clin Oncol 35, 2017 (suppl; abstr 8527)

WFO in ASCO 2017
•가천대 길병원의 대장암과 위암 환자에 왓슨 적용 결과

• 대장암 환자(stage II-IV) 340명

• 진행성 위암 환자 185명 (Retrospective) 
• 의사와의 일치율

• 대장암 환자: 73%

• 보조 (adjuvant) 항암치료를 받은 250명: 85%

• 전이성 환자 90명: 40% 
• 위암 환자: 49%

• Trastzumab/FOLFOX 가 국민 건강 보험 수가를 받지 못함

• S-1(tegafur, gimeracil and oteracil)+cisplatin):

• 국내는 매우 루틴; 미국에서는 X

ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
& Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artiﬁcial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest

ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
& Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artiﬁcial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest
Table 2. MMDT and WFO recommendations after the initial and blinded second reviews
Review of breast cancer cases (N 5 638) Concordant cases, n (%) Non-concordant cases, n (%)
Recommended For consideration Total Not recommended Not available Total
Initial review (T1MMDT versus T2WFO) 296 (46) 167 (26) 463 (73) 137 (21) 38 (6) 175 (27)
Second review (T2MMDT versus T2WFO) 397 (62) 194 (30) 591 (93) 36 (5) 11 (2) 47 (7)
T1MMDT, original MMDT recommendation from 2014 to 2016; T2WFO, WFO advisor treatment recommendation in 2016; T2MMDT, MMDT treatment recom-
mendation in 2016; MMDT, Manipal multidisciplinary tumor board; WFO, Watson for Oncology.
31%
18%
1% 2% 33%
5% 31%
6%
0% 10% 20%
Not available Not recommended RecommendedFor consideration
30% 40% 50% 60% 70% 80% 90% 100%
8% 25% 61%
64%
64%
29% 51%
62%
Concordance, 93%
Concordance, 80%
Concordance, 97%
Concordance, 95%
Concordance, 86%
2%
2%
Overall
(n=638)
Stage I
(n=61)
Stage II
(n=262)
Stage III
(n=191)
Stage IV
(n=124)
5%
Figure 1. Treatment concordance between WFO and the MMDT overall and by stage. MMDT, Manipal multidisciplinary tumor board; WFO,
Watson for Oncology.
5%Non-metastatic
HR(+)HER2/neu(+)Triple(–)
Metastatic
Non-metastatic
Metastatic
Non-metastatic
Metastatic
10%
1%
2%
1% 5% 20%
20%10%
0%
Not applicable Not recommended For consideration Recommended
20% 40% 60% 80% 100%
5%
74%
65%
34% 64%
5% 38% 56%
15% 20% 55%
36% 59%
Concordance, 95%
Concordance, 75%
Concordance, 94%
Concordance, 98%
Concordance, 94%
Concordance, 85%
Figure 2. Treatment concordance between WFO and the MMDT by stage and receptor status. HER2/neu, human epidermal growth factor
receptor 2; HR, hormone receptor; MMDT, Manipal multidisciplinary tumor board; WFO, Watson for Oncology.
Annals of Oncology Original article

잠정적 결론
•왓슨 포 온콜로지와 의사의 일치율:

•암종별로 다르다.

•같은 암종에서도 병기별로 다르다.

•같은 암종에 대해서도 병원별/국가별로 다르다.

•시간이 흐름에 따라 달라질 가능성이 있다.

원칙이 필요하다
•어떤 환자의 경우, 왓슨에게 의견을 물을 것인가?

•왓슨을 (암종별로) 얼마나 신뢰할 것인가?

•왓슨의 의견을 환자에게 공개할 것인가?

•왓슨과 의료진의 판단이 다른 경우 어떻게 할 것인가?

•왓슨에게 보험 급여를 매길 수 있는가?
이러한 기준에 따라 의료의 질/치료효과가 달라질 수 있으나,

현재 개별 병원이 개별적인 기준으로 활용하게 됨

Empowering the Oncology Community for Cancer Care
Genomics
Oncology
Clinical
Trial
Matching
Watson Health’s oncology clients span more than 35 hospital systems
“Empowering the Oncology Community
for Cancer Care”
Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive”

IBM Watson Health
Watson for Clinical Trial Matching (CTM)
18
1. According to the National Comprehensive Cancer Network (NCCN)
2. http://csdd.tufts.edu/files/uploads/02_-_jan_15,_2013_-_recruitment-retention.pdf© 2015 International Business Machines Corporation
Searching across
eligibility criteria of clinical
trials is time consuming
and labor intensive
Current
Challenges
Fewer than 5% of
adult cancer patients
participate in clinical
trials1
37% of sites fail to meet
minimum enrollment
targets. 11% of sites fail
to enroll a single patient 2
The Watson solution
• Uses structured and unstructured
patient data to quickly check
eligibility across relevant clinical
trials
• Provides eligible trial
considerations ranked by
relevance
• Increases speed to qualify
patients
Clinical Investigators
(Opportunity)
• Trials to Patient: Perform
feasibility analysis for a trial
• Identify sites with most
potential for patient enrollment
• Optimize inclusion/exclusion
criteria in protocols
Faster, more efficient
recruitment strategies,
better designed protocols
Point of Care
(Offering)
• Patient to Trials:
Quickly find the
right trial that a
patient might be
eligible for
amongst 100s of
open trials
available
Improve patient care
quality, consistency,
increased efficiencyIBM Confidential

•총 16주간 HOG( Highlands Oncology Group)의 폐암과 유방암 환자 2,620명을 대상

•90명의 환자를 3개의 노바티스 유방암 임상 프로토콜에 따라 선별

•임상 시험 코디네이터: 1시간 50분

•Watson CTM: 24분 (78% 시간 단축)

•Watson CTM은 임상 시험 기준에 해당되지 않는 환자 94%를 자동으로 스크리닝

•메이요 클리닉의 유방암 신약 임상시험에 등록자의 수가 80% 증가하였다는 결과 발표

Watson Genomics Overview
20
Watson Genomics Content
• 20+ Content Sources Including:
• Medical Articles (23Million)
• Drug Information
• Clinical Trial Information
• Genomic Information
Case Sequenced
VCF / MAF, Log2, Dge
Encryption
Molecular Profile
Analysis
Pathway Analysis
Drug Analysis
Service Analysis, Reports, & Visualizations

•In all 29 instances of retrospective analysis, WGA finds actionable insights and
identifies potential drugs for consideration.

•The automated generation of these insights is achieved by WGA in minutes.

Kazimierz O.
Wrzeszczynski, PhD*
Mayu O. Frank, NP,
MS*
Takahiko Koyama, PhD*
Kahn Rhrissorrakrai, PhD*
Nicolas Robine, PhD
Filippo Utro, PhD
Anne-Katrin Emde, PhD
Bo-Juen Chen, PhD
Kanika Arora, MS
Minita Shah, MS
Vladimir Vacic, PhD
Raquel Norel, PhD
Erhan Bilal, PhD
Ewa A. Bergmann, MSc
Julia L. Moore Vogel,
PhD
Jeffrey N. Bruce, MD
Andrew B. Lassman, MD
Peter Canoll, MD, PhD
Christian Grommes, MD
Steve Harvey, BS
Laxmi Parida, PhD
Vanessa V. Michelini, BS
Michael C. Zody, PhD
Vaidehi Jobanputra, PhD
Ajay K. Royyuru, PhD
Robert B. Darnell, MD,
Comparing sequencing assays and
human-machine analyses in actionable
genomics for glioblastoma
ABSTRACT
Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare
potentially actionable calls from each.
Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal
DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA
sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians
and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated
system for prioritizing somatic variants and identifying drugs.
Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA com-
pleted a comparable analysis in a fraction of the time required by the human analysts.
Conclusions: The development of an effective human-machine interface in the analysis of deep
cancer genomic datasets may provide potentially clinically actionable calls for individual pa-
tients in a more timely and efficient manner than currently possible.
ClinicalTrials.gov identifier: NCT02725684. Neurol Genet 2017;3:e164; doi: 10.1212/
NXG.0000000000000164
GLOSSARY
CNV 5 copy number variant; EGFR 5 epidermal growth factor receptor; GATK 5 Genome Analysis Toolkit; GBM 5 glioblas-
toma; IRB 5 institutional review board; NLP 5 Natural Language Processing; NYGC 5 New York Genome Center; RNA-seq 5
RNA sequencing; SNV 5 single nucleotide variant; SV 5 structural variant; TCGA 5 The Cancer Genome Atlas; TPM 5
transcripts per million; VCF 5 variant call file; VUS 5 variants of uncertain significance; WGA 5 Watson Genomic Analytics;
WGS 5 whole-genome sequencing.
The clinical application of next-generation sequencing technology to cancer diagnosis and treat-
ment is in its early stages.1–3
An initial implementation of this technology has been in targeted
panels, where subsets of cancer-relevant and/or highly actionable genes are scrutinized for
potentially actionable mutations. This approach has been widely adopted, offering high redun-
dancy of sequence coverage for the small number of sites of known clinical utility at relatively

Table 3 List of variants identified as actionable by 3 different platforms
Gene Variant
Identified variant Identified associated drugs
NYGC WGA FO NYGC WGA FO
CDKN2A Deletion Yes Yes Yes Palbociclib, LY2835219
LEE001
Palbociclib LY2835219 Clinical trial
CDKN2B Deletion Yes Yes Yes Palbociclib, LY2835219
LEE002
Palbociclib LY2835219 Clinical trial
EGFR Gain (whole arm) Yes — — Cetuximab — —
ERG Missense P114Q Yes Yes — RI-EIP RI-EIP —
FGFR3 Missense L49V Yes VUS — TK-1258 — —
MET Amplification Yes Yes Yes INC280 Crizotinib, cabozantinib Crizotinib, cabozantinib
MET Frame shift R755fs Yes — — INC280 — —
MET Exon skipping Yes — — INC280 — —
NF1 Deletion Yes — — MEK162 — —
NF1 Nonsense R461* Yes Yes Yes MEK162 MEK162, cobimetinib,
trametinib, GDC-0994
Everolimus, temsirolimus,
trametinib
PIK3R1 Insertion
R562_M563insI
Yes Yes — BKM120 BKM120, LY3023414 —
PTEN Loss (whole arm) Yes — — Everolimus, AZD2014 — —
STAG2 Frame shift R1012 fs Yes Yes Yes Veliparib, clinical trial Olaparib —
DNMT3A Splice site 2083-1G.C — — Yes — — —
TERT Promoter-146C.T Yes — Yes — — —
ABL2 Missense D716N Germline NA VUS
mTOR Missense H1687R Germline NA VUS
NPM1 Missense E169D Germline NA VUS
NTRK1 Missense G18E Germline NA VUS
PTCH1 Missense P1250R Germline NA VUS
TSC1 Missense G1035S Germline NA VUS
Abbreviations: FO 5 FoundationOne; NYGC 5 New York Genome Center; RNA-seq 5 RNA sequencing; WGA 5 Watson Genomic Analytics; WGS 5 whole-
genome sequencing.
Genes, variant description, and, where appropriate, candidate clinically relevant drugs are listed. Variants identified by the FO as variants of uncertain
significance (VUS) were identified by the NYGC as germline variants.
• WGA analysis vastly accelerated the time to discovery of potentially actionable variants from the VCF ﬁles.
• WGA was able to provide reports of potentially clinically actionable insights within 10 minutes
• , while human analysis of this patient's VCF ﬁle took an estimated 160 hours of person-time

•“향후 10년 동안 첫번째 cardiovascular event 가 올 것인가” 예측

•전향적 코호트 스터디: 영국 환자 378,256 명

•일상적 의료 데이터를 바탕으로 기계학습으로 질병을 예측하는 첫번째 대규모 스터디

•기존의 ACC/AHA 가이드라인과 4가지 기계학습 알고리즘의 정확도를 비교

•Random forest; Logistic regression; Gradient bossting; Neural network

Stephen F.Weng et al PLoS One 2017
Can machine-learning improve cardiovascular
risk prediction using routine clinical data?
in a sensitivity of 62.7% and PPV of 17.1%. The random forest algorithm resulted in a net
increase of 191 CVD cases from the baseline model, increasing the sensitivity to 65.3% and
PPV to 17.8% while logistic regression resulted in a net increase of 324 CVD cases (sensitivity
67.1%; PPV 18.3%). Gradient boosting machines and neural networks performed best, result-
ing in a net increase of 354 (sensitivity 67.5%; PPV 18.4%) and 355 CVD (sensitivity 67.5%;
PPV 18.4%) cases correctly predicted, respectively.
The ACC/AHA baseline model correctly predicted 53,106 non-cases from 75,585 total non-
cases, resulting in a specificity of 70.3% and NPV of 95.1%. The net increase in non-cases
Table 3. Top 10 risk factor variables for CVD algorithms listed in descending order of coefficient effect size (ACC/AHA; logistic regression),
weighting (neural networks), or selection frequency (random forest, gradient boosting machines). Algorithms were derived from training cohort of
295,267 patients.
ACC/AHA Algorithm Machine-learning Algorithms
Men Women ML: Logistic
Regression
ML: Random Forest ML: Gradient Boosting
Machines
ML: Neural Networks
Age Age Ethnicity Age Age Atrial Fibrillation
Total Cholesterol HDL Cholesterol Age Gender Gender Ethnicity
HDL Cholesterol Total Cholesterol SES: Townsend
Deprivation Index
Ethnicity Ethnicity Oral Corticosteroid
Prescribed
Smoking Smoking Gender Smoking Smoking Age
Age x Total Cholesterol Age x HDL Cholesterol Smoking HDL cholesterol HDL cholesterol Severe Mental Illness
Treated Systolic Blood
Pressure
Age x Total Cholesterol Atrial Fibrillation HbA1c Triglycerides SES: Townsend
Deprivation Index
Age x Smoking Treated Systolic Blood
Pressure
Chronic Kidney Disease Triglycerides Total Cholesterol Chronic Kidney Disease
Age x HDL Cholesterol Untreated Systolic
Blood Pressure
Rheumatoid Arthritis SES: Townsend
Deprivation Index
HbA1c BMI missing
Untreated Systolic
Blood Pressure
Age x Smoking Family history of
premature CHD
BMI Systolic Blood Pressure Smoking
Diabetes Diabetes COPD Total Cholesterol SES: Townsend
Deprivation Index
Gender
Italics: Protective Factors
https://doi.org/10.1371/journal.pone.0174944.t003
PLOS ONE | https://doi.org/10.1371/journal.pone.0174944 April 4, 2017 8 / 14
•기존 ACC/AHA 가이드라인의 위험 요소의 일부분만 기계학습 알고리즘에도 포함

•하지만, Diabetes는 네 모델 모두에서 포함되지 않았다.

•기존의 위험 예측 툴에는 포함되지 않던, 아래와 같은 새로운 요소들이 포함되었다.

•COPD, severe mental illness, prescribing of oral corticosteroids

•triglyceride level 등의 바이오 마커

Stephen F.Weng et al PLoS One 2017
Can machine-learning improve cardiovascular
risk prediction using routine clinical data?
correctly predicted compared to the baseline ACC/AHA model ranged from 191 non-cases for
the random forest algorithm to 355 non-cases for the neural networks. Full details on classifi-
cation analysis can be found in S2 Table.
Discussion
Compared to an established AHA/ACC risk prediction algorithm, we found all machine-
learning algorithms tested were better at identifying individuals who will develop CVD and
those that will not. Unlike established approaches to risk prediction, the machine-learning
methods used were not limited to a small set of risk factors, and incorporated more pre-exist-
Table 4. Performance of the machine-learning (ML) algorithms predicting 10-year cardiovascular disease (CVD) risk derived from applying train-
ing algorithms on the validation cohort of 82,989 patients. Higher c-statistics results in better algorithm discrimination. The baseline (BL) ACC/AHA
10-year risk prediction algorithm is provided for comparative purposes.
Algorithms AUC c-statistic Standard Error* 95% Conﬁdence
Interval
Absolute Change from Baseline
LCL UCL
BL: ACC/AHA 0.728 0.002 0.723 0.735 —
ML: Random Forest 0.745 0.003 0.739 0.750 +1.7%
ML: Logistic Regression 0.760 0.003 0.755 0.766 +3.2%
ML: Gradient Boosting Machines 0.761 0.002 0.755 0.766 +3.3%
ML: Neural Networks 0.764 0.002 0.759 0.769 +3.6%
*Standard error estimated by jack-knife procedure [30]
https://doi.org/10.1371/journal.pone.0174944.t004
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
• 네 가지 기계학습 모델 모두 기존의 ACC/AHA 가이드라인 대비 더 정확했다.
• Neural Networks 이 AUC=0.764 로 가장 정확했다.
• “이 모델을 활용했더라면 355 명의 추가적인 cardiovascular event 를 예방했을 것”
• Deep Learning 을 활용하면 정확도는 더 높아질 수 있을 것
• Genetic information 등의 추가적인 risk factor 를 활용해볼 수 있다.

•2018년 1월 구글이 전자의무기록(EMR)을 분석하여, 환자 치료 결과를 예측하는 인공지능 발표

•환자가 입원 중에 사망할 것인지

•장기간 입원할 것인지

•퇴원 후에 30일 내에 재입원할 것인지

•퇴원 시의 진단명 
•이번 연구의 특징: 확장성

•과거 다른 연구와 달리 EMR의 일부 데이터를 pre-processing 하지 않고,

•전체 EMR 를 통채로 모두 분석하였음: UCSF, UCM (시카고 대학병원)

•특히, 비정형 데이터인 의사의 진료 노트도 분석

ARTICLE OPEN
Scalable and accurate deep learning with electronic health
records
Alvin Rajkomar 1,2
, Eyal Oren1
, Kai Chen1
, Andrew M. Dai1
, Nissan Hajaj1
, Michaela Hardt1
, Peter J. Liu1
, Xiaobing Liu1
, Jake Marcus1
,
Mimi Sun1
, Patrik Sundberg1
, Hector Yee1
, Kun Zhang1
, Yi Zhang1
, Gerardo Flores1
, Gavin E. Duggan1
, Jamie Irvine1
, Quoc Le1
,
Kurt Litsch1
, Alexander Mossin1
, Justin Tansuwan1
, De Wang1
, James Wexler1
, Jimbo Wilson1
, Dana Ludwig2
, Samuel L. Volchenboum3
,
Katherine Chou1
, Michael Pearson1
, Srinivasan Madabushi1
, Nigam H. Shah4
, Atul J. Butte2
, Michael D. Howell1
, Claire Cui1
,
Greg S. Corrado1
and Jeffrey Dean1
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare
quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR
data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation
of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that
deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple
centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic
medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR
data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for
tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day
unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge
diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases.
We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case
study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the
patient’s chart.
npj Digital Medicine (2018)1:18 ; doi:10.1038/s41746-018-0029-1
INTRODUCTION
The promise of digital medicine stems in part from the hope that,
by digitizing health data, we might more easily leverage computer
information systems to understand and improve care. In fact,
routinely collected patient healthcare data are now approaching
the genomic scale in volume and complexity.1
Unfortunately,
most of this information is not yet used in the sorts of predictive
statistical models clinicians might use to improve care delivery. It
is widely suspected that use of such efforts, if successful, could
provide major benefits not only for patient safety and quality but
also in reducing healthcare costs.2–6
In spite of the richness and potential of available data, scaling
the development of predictive models is difficult because, for
traditional predictive modeling techniques, each outcome to be
predicted requires the creation of a custom dataset with specific
variables.7
It is widely held that 80% of the effort in an analytic
model is preprocessing, merging, customizing, and cleaning
nurses, and other providers are included. Traditional modeling
approaches have dealt with this complexity simply by choosing a
very limited number of commonly collected variables to consider.7
This is problematic because the resulting models may produce
imprecise predictions: false-positive predictions can overwhelm
physicians, nurses, and other providers with false alarms and
concomitant alert fatigue,10
which the Joint Commission identified
as a national patient safety priority in 2014.11
False-negative
predictions can miss significant numbers of clinically important
events, leading to poor clinical outcomes.11,12
Incorporating the
entire EHR, including clinicians’ free-text notes, offers some hope
of overcoming these shortcomings but is unwieldy for most
predictive modeling techniques.
Recent developments in deep learning and artificial neural
networks may allow us to address many of these challenges and
unlock the information in the EHR. Deep learning emerged as the
preferred machine learning approach in machine perception
www.nature.com/npjdigitalmed
•2018년 1월 구글이 전자의무기록(EMR)을 분석하여, 환자 치료 결과를 예측하는 인공지능 발표

•환자가 입원 중에 사망할 것인지

•장기간 입원할 것인지

•퇴원 후에 30일 내에 재입원할 것인지

•퇴원 시의 진단명 
•이번 연구의 특징: 확장성

•과거 다른 연구와 달리 EMR의 일부 데이터를 pre-processing 하지 않고,

•전체 EMR 를 통채로 모두 분석하였음: UCSF, UCM (시카고 대학병원)

•특히, 비정형 데이터인 의사의 진료 노트도 분석

Figure 4: The patient record shows a woman with metastatic breast cancer with malignant pleural
e usions and empyema. The patient timeline at the top of the ﬁgure contains circles for every
time-step for which at least a single token exists for the patient, and the horizontal lines show the
data-type. There is a close-up view of the most recent data-points immediately preceding a prediction
made 24 hours after admission. We trained models for each data-type and highlighted in red the
tokens which the models attended to – the non-highlighted text was not attended to but is shown for
context. The models pick up features in the medications, nursing ﬂowsheets, and clinical notes to
make the prediction.
• TAAN(Time-Aware Neural Nework)를 이용하여,

• 전이성 유방암 환자의 EMR에서 어떤 부분을 인공지능이 더 유의하게 보았는지를 표시해본 결과,

• 실제로 사망 위험도와 관계가 높은 데이터를 더 중요하게 보았음

• 진료 기록: 농양(empyema), 흉수(pleural effusions) 등

• 간호 기록: 반코마이신, 메트로니다졸 등의 항생제 투약, 욕창(pressure ulcer)의 위험이 높음

• 흉부에 삽입하는 튜브(카테터)의 상표인 'PleurX'도 중요 단어로 파악

LETTERS
https://doi.org/10.1038/s41591-018-0335-9
1
Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China. 2
Institute for Genomic Medicine, Institute of
Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. 3
Hangzhou YITU Healthcare Technology Co. Ltd,
Hangzhou, China. 4
Department of Thoracic Surgery/Oncology, First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and
National Clinical Research Center for Respiratory Disease, Guangzhou, China. 5
Guangzhou Kangrui Co. Ltd, Guangzhou, China. 6
Guangzhou Regenerative
Medicine and Health Guangdong Laboratory, Guangzhou, China. 7
Veterans Administration Healthcare System, San Diego, CA, USA. 8
These authors contributed
equally: Huiying Liang, Brian Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu. *e-mail: kang.zhang@gmail.com; xiahumin@hotmail.com
Artificial intelligence (AI)-based methods have emerged as
powerful tools to transform medical care. Although machine
learning classifiers (MLCs) have already demonstrated strong
performance in image-based diagnoses, analysis of diverse
and massive electronic health record (EHR) data remains chal-
lenging. Here, we show that MLCs can query EHRs in a manner
similar to the hypothetico-deductive reasoning used by physi-
cians and unearth associations that previous statistical meth-
ods have not found. Our model applies an automated natural
language processing system using deep learning techniques
to extract clinically relevant information from EHRs. In total,
101.6 million data points from 1,362,559 pediatric patient
visits presenting to a major referral center were analyzed to
train and validate the framework. Our model demonstrates
high diagnostic accuracy across multiple organ systems and is
comparable to experienced pediatricians in diagnosing com-
mon childhood diseases. Our study provides a proof of con-
cept for implementing an AI-based system as a means to aid
physiciansintacklinglargeamountsofdata,augmentingdiag-
nostic evaluations, and to provide clinical decision support in
cases of diagnostic uncertainty or complexity. Although this
impact may be most evident in areas where healthcare provid-
ers are in relative shortage, the benefits of such an AI system
are likely to be universal.
Medical information has become increasingly complex over
time. The range of disease entities, diagnostic testing and biomark-
ers, and treatment modalities has increased exponentially in recent
years. Subsequently, clinical decision-making has also become more
complex and demands the synthesis of decisions from assessment
of large volumes of data representing clinical information. In the
current digital age, the electronic health record (EHR) represents a
massive repository of electronic data points representing a diverse
array of clinical information1–3
. Artificial intelligence (AI) methods
have emerged as potentially powerful tools to mine EHR data to aid
in disease diagnosis and management, mimicking and perhaps even
augmenting the clinical decision-making of human physicians1
.
To formulate a diagnosis for any given patient, physicians fre-
quently use hypotheticodeductive reasoning. Starting with the chief
complaint, the physician then asks appropriately targeted questions
relating to that complaint. From this initial small feature set, the
physician forms a differential diagnosis and decides what features
(historical questions, physical exam findings, laboratory testing,
and/or imaging studies) to obtain next in order to rule in or rule
out the diagnoses in the differential diagnosis set. The most use-
ful features are identified, such that when the probability of one of
the diagnoses reaches a predetermined level of acceptability, the
process is stopped, and the diagnosis is accepted. It may be pos-
sible to achieve an acceptable level of certainty of the diagnosis with
only a few features without having to process the entire feature set.
Therefore, the physician can be considered a classifier of sorts.
In this study, we designed an AI-based system using machine
learning to extract clinically relevant features from EHR notes to
mimic the clinical reasoning of human physicians. In medicine,
machine learning methods have already demonstrated strong per-
formance in image-based diagnoses, notably in radiology2
, derma-
tology4
, and ophthalmology5–8
, but analysis of EHR data presents
a number of difficult challenges. These challenges include the vast
quantity of data, high dimensionality, data sparsity, and deviations
Evaluation and accurate diagnoses of pediatric
diseases using artificial intelligence
Huiying Liang1,8
, Brian Y. Tsui 2,8
, Hao Ni3,8
, Carolina C. S. Valentim4,8
, Sally L. Baxter 2,8
,
Guangjian Liu1,8
, Wenjia Cai 2
, Daniel S. Kermany1,2
, Xin Sun1
, Jiancong Chen2
, Liya He1
, Jie Zhu1
,
Pin Tian2
, Hua Shao2
, Lianghong Zheng5,6
, Rui Hou5,6
, Sierra Hewett1,2
, Gen Li1,2
, Ping Liang3
,
Xuan Zang3
, Zhiqi Zhang3
, Liyan Pan1
, Huimin Cai5,6
, Rujuan Ling1
, Shuhua Li1
, Yongwang Cui1
,
Shusheng Tang1
, Hong Ye1
, Xiaoyan Huang1
, Waner He1
, Wenqing Liang1
, Qing Zhang1
, Jianmin Jiang1
,
Wei Yu1
, Jianqun Gao1
, Wanxing Ou1
, Yingmin Deng1
, Qiaozhen Hou1
, Bei Wang1
, Cuichan Yao1
,
Yan Liang1
, Shu Zhang1
, Yaou Duan2
, Runze Zhang2
, Sarah Gibson2
, Charlotte L. Zhang2
, Oulan Li2
,
Edward D. Zhang2
, Gabriel Karin2
, Nathan Nguyen2
, Xiaokang Wu1,2
, Cindy Wen2
, Jie Xu2
, Wenqin Xu2
,
Bochu Wang2
, Winston Wang2
, Jing Li1,2
, Bianca Pizzato2
, Caroline Bao2
, Daoman Xiang1
, Wanting He1,2
,
Suiqin He2
, Yugui Zhou1,2
, Weldon Haw2,7
, Michael Goldbaum2
, Adriana Tremoulet2
, Chun-Nan Hsu 2
,
Hannah Carter2
, Long Zhu3
, Kang Zhang 1,2,7
* and Huimin Xia 1
*
NATURE MEDICINE | www.nature.com/naturemedicine
Nat Med 2019 Feb
•소아 환자 130만명의 EMR 데이터 101.6 million 개 분석

•딥러닝 기반의 자연어 처리 기술

•의사의 hypotetico-deductive reasoning 모방

•소아 환자의 common disease를 진단하는 인공지능

LETTERS
https://doi.org/10.1038/s41591-018-0335-9
1
Hangzhou, China. 4
.
, derma-
tology4
Huiying Liang1,8
, Brian Y. Tsui 2,8
, Hao Ni3,8
,
Guangjian Liu1,8
, Wenjia Cai 2
, Xin Sun1
, Jiancong Chen2
, Liya He1
, Jie Zhu1
,
Pin Tian2
, Hua Shao2
, Rui Hou5,6
, Sierra Hewett1,2
, Gen Li1,2
, Ping Liang3
,
Xuan Zang3
, Zhiqi Zhang3
, Liyan Pan1
, Huimin Cai5,6
, Rujuan Ling1
, Shuhua Li1
, Yongwang Cui1
,
Shusheng Tang1
, Hong Ye1
, Xiaoyan Huang1
, Waner He1
, Wenqing Liang1
, Qing Zhang1
, Jianmin Jiang1
,
Wei Yu1
, Jianqun Gao1
, Wanxing Ou1
, Yingmin Deng1
, Qiaozhen Hou1
, Bei Wang1
, Cuichan Yao1
,
Yan Liang1
, Shu Zhang1
, Yaou Duan2
, Runze Zhang2
, Sarah Gibson2
, Oulan Li2
,
Edward D. Zhang2
, Gabriel Karin2
, Nathan Nguyen2
, Xiaokang Wu1,2
, Cindy Wen2
, Jie Xu2
, Wenqin Xu2
,
Bochu Wang2
, Winston Wang2
, Jing Li1,2
, Bianca Pizzato2
, Caroline Bao2
, Daoman Xiang1
, Wanting He1,2
,
Suiqin He2
, Yugui Zhou1,2
, Weldon Haw2,7
, Michael Goldbaum2
, Chun-Nan Hsu 2
,
Hannah Carter2
, Long Zhu3
, Kang Zhang 1,2,7
* and Huimin Xia 1
*
Nat Med 2019 Feb
LETTERSNATURE MEDICINE
examination, laboratory testing, and PACS (picture archiving and
communication systems) reports), the F1 scores exceeded 90%
except in one instance, which was for categorical variables detected
tree, similar to how a human physician might evaluate a patient’s
features to achieve a diagnosis based on the same clinical data
incorporated into the information model. Encounters labeled by
Systemic generalized diseases
Varicella without complication
Influenza
Infectious mononucleosis
Sepsis
Exanthema subitum
Neuropsychiatric diseases
Tic disorder
Attention-deficit hyperactivity disorders
Bacterial meningitis
Encephalitis
Convulsions
Genitourinary diseases
Respiratory diseases
Upper respiratory
diseases
Acute upper respiratory infection
Sinusitis
Acute sinusitis
Acute recurrent sinusitis
Acute laryngitis
Acute pharyngitis
Lower respiratory
diseases
Bronchitis
Acute bronchitis
Bronchiolitis
Acute bronchitis due to Mycoplasma pneumoniae
Pneumonia
Bacterial pneumonia
Bronchopneumonia
Bacterial pneumonia elsewhere
Mycoplasma infection
Asthma
Asthma uncomplicated
Cough variant asthma
Asthma with acute exacerbation
Acute tracheitis
Gastrointestinal diseases
Diarrhea
Mouth-related diseases
Enteroviral vesicular stomatitis
with exanthem
Fig. 2 | Hierarchy of the diagnostic framework in a large pediatric cohort. A hierarchical logistic regression classifier was used to establish a diagnostic
system based on anatomic divisions. An organ-based approach was used, wherein diagnoses were first separated into broad organ systems, then
subsequently divided into organ subsystems and/or into more specific diagnosis groups.

LETTERS
https://doi.org/10.1038/s41591-018-0335-9
1
Hangzhou, China. 4
.
, derma-
tology4
Huiying Liang1,8
, Brian Y. Tsui 2,8
, Hao Ni3,8
,
Guangjian Liu1,8
, Wenjia Cai 2
, Xin Sun1
, Jiancong Chen2
, Liya He1
, Jie Zhu1
,
Pin Tian2
, Hua Shao2
, Rui Hou5,6
, Sierra Hewett1,2
, Gen Li1,2
, Ping Liang3
,
Xuan Zang3
, Zhiqi Zhang3
, Liyan Pan1
, Huimin Cai5,6
, Rujuan Ling1
, Shuhua Li1
, Yongwang Cui1
,
Shusheng Tang1
, Hong Ye1
, Xiaoyan Huang1
, Waner He1
, Wenqing Liang1
, Qing Zhang1
, Jianmin Jiang1
,
Wei Yu1
, Jianqun Gao1
, Wanxing Ou1
, Yingmin Deng1
, Qiaozhen Hou1
, Bei Wang1
, Cuichan Yao1
,
Yan Liang1
, Shu Zhang1
, Yaou Duan2
, Runze Zhang2
, Sarah Gibson2
, Oulan Li2
,
Edward D. Zhang2
, Gabriel Karin2
, Nathan Nguyen2
, Xiaokang Wu1,2
, Cindy Wen2
, Jie Xu2
, Wenqin Xu2
,
Bochu Wang2
, Winston Wang2
, Jing Li1,2
, Bianca Pizzato2
, Caroline Bao2
, Daoman Xiang1
, Wanting He1,2
,
Suiqin He2
, Yugui Zhou1,2
, Weldon Haw2,7
, Michael Goldbaum2
, Chun-Nan Hsu 2
,
Hannah Carter2
, Long Zhu3
, Kang Zhang 1,2,7
* and Huimin Xia 1
*
Nat Med 2019 Feb
LETTERSNATURE MEDICINE
of our system was especially strong for the common conditions of
acute upper respiratory infection and sinusitis, both of which were
diagnosed with an accuracy of 0.95 between the machine-predicted
diagnosis and the human physician-generated diagnosis. In con-
trast, dangerous conditions tend to be less common and would have
diagnostic hierarchy decision tree can be adjusted to what is most
appropriate for the clinical situation.
In terms of implementation, we foresee this type of AI-assisted
diagnostic system being integrated into clinical practice in several
ways. First, it could assist with triage procedures. For example,
Table 2 | Illustration of diagnostic performance of our AI model and physicians
Disease conditions Our model Physicians
Physician group 1 Physician group 2 Physician group 3 Physician group 4 Physician group 5
Asthma 0.920 0.801 0.837 0.904 0.890 0.935
Encephalitis 0.837 0.947 0.961 0.950 0.959 0.965
Gastrointestinal disease 0.865 0.818 0.872 0.854 0.896 0.893
Group: ‘Acute laryngitis’ 0.786 0.808 0.730 0.879 0.940 0.943
Group: ‘Pneumonia’ 0.888 0.829 0.767 0.946 0.952 0.972
Group: ‘Sinusitis’ 0.932 0.839 0.797 0.896 0.873 0.870
Lower respiratory 0.803 0.803 0.815 0.910 0.903 0.935
Mouth-related diseases 0.897 0.818 0.872 0.854 0.896 0.893
Neuropsychiatric disease 0.895 0.925 0.963 0.960 0.962 0.906
Respiratory 0.935 0.808 0.769 0.89 0.907 0.917
Systemic or generalized 0.925 0.879 0.907 0.952 0.907 0.944
Upper respiratory 0.929 0.817 0.754 0.884 0.916 0.916
Root 0.889 0.843 0.863 0.908 0.903 0.912
Average F1 score 0.885 0.841 0.839 0.907 0.915 0.923
We used the F1score to evaluate the diagnosis performance across different groups (rows); our model, two junior physician groups (groups 1 and 2), and three senior physician groups (groups 3, 4, and
5) (see Methods section for description). We observed that our model performed better than junior physician groups but slightly worse than three experienced physician groups. Root is the first level of
diagnosis classification.
•multiple organ system에 대해서,

•주니어 스태프 보다는 높은 정확도

•시니어 스태프 보다는 낮은 정확도

Deep Learning
http://theanalyticsstore.ie/deep-learning/

인공지능
기계학습
딥러닝
전문가 시스템
사이버네틱스
…
인공신경망
결정트리
서포트 벡터 머신
…
컨볼루션 신경망 (CNN)
순환신경망(RNN)
…
인공지능과 딥러닝의 관계

페이스북의 딥페이스
Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14.
Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three
locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million
parameters, where more than 95% come from the local and fully connected layers.
very few parameters. These layers merely expand the input
into a set of simple local features.
The subsequent layers (L4, L5 and L6) are instead lo-
cally connected [13, 16], like a convolutional layer they ap-
ply a filter bank, but every location in the feature map learns
a different set of filters. Since different regions of an aligned
image have different local statistics, the spatial stationarity
The goal of training is to maximize the probability of
the correct class (face id). We achieve this by minimiz-
ing the cross-entropy loss for each training sample. If k
is the index of the true label for a given input, the loss is:
L = log pk. The loss is minimized over the parameters
by computing the gradient of L w.r.t. the parameters and
Human: 95% vs. DeepFace in Facebook: 97.35%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)

Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering
Human: 95% vs. FaceNet of Google: 99.63%
False accept
False reject
s. This shows all pairs of images that were
on LFW. Only eight of the 13 errors shown
he other four are mislabeled in LFW.
on Youtube Faces DB
ge similarity of all pairs of the first one
our face detector detects in each video.
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
lead to truly amazing results. Figure 7 shows one cluster in
a users personal photo collection, generated using agglom-
erative clustering. It is a clear showcase of the incredible
invariance to occlusion, lighting, pose and even age.
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-
sification. Our end-to-end training both simplifies the setup
and shows that directly optimizing a loss relevant to the task
at hand improves performance.
Another strength of our model is that it only requires
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-
구글의 페이스넷

바이두의 얼굴 인식 인공지능
Jingtuo Liu (2015) Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Human: 95% vs.Baidu: 99.77%
3
Although several algorithms have achieved nearly perfect
accuracy in the 6000-pair verification task, a more practical
can achieve 95.8% identification rate, relatively reducing the
error rate by about 77%.
TABLE 3. COMPARISONS WITH OTHER METHODS ON SEVERAL EVALUATION TASKS
Score = -0.060 (pair #113) Score = -0.022 (pair #202) Score = -0.034 (pair #656)
Score = -0.031 (pair #1230) Score = -0.073 (pair #1862) Score = -0.091(pair #2499)
Score = -0.024 (pair #2551) Score = -0.036 (pair #2552) Score = -0.089 (pair #2610)
Method
Performance on tasks
Pair-wise
Accuracy(%)
Rank-1(%)
DIR(%) @
FAR =1%
Verification(%
)@ FAR=0.1%
Open-set
Identification(%
)@ Rank =
1,FAR = 0.1%
IDL Ensemble
Model
99.77 98.03 95.8 99.41 92.09
IDL Single Model 99.68 97.60 94.12 99.11 89.08
FaceNet[12] 99.63 NA NA NA NA
DeepID3[9] 99.53 96.00 81.40 NA NA
Face++[2] 99.50 NA NA NA NA
Facebook[15] 98.37 82.5 61.9 NA NA
Learning from
Scratch[4]
97.73 NA NA 80.26 28.90
HighDimLBP[10] 95.17 NA NA
41.66(reported
in [4])
18.07(reported
in [4])
• 6,000쌍의 얼굴 사진 중에 바이두의 인공지능은 불과 14쌍만을 잘못 판단

• 알고 보니 이 14쌍 중의 5쌍의 사진은 오히려 정답에 오류가 있었고,  
 
실제로는 인공지능이 정확 (red box)

REVIEW ARTICLE | FOCUS
https://doi.org/10.1038/s41591-018-0300-7
Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA. e-mail: etopol@scripps.edu
M
edicine is at the crossroad of two major trends. The first
is a failed business model, with increasing expenditures
and jobs allocated to healthcare, but with deteriorating key
outcomes, including reduced life expectancy and high infant, child-
hood, and maternal mortality in the United States1,2
. This exem-
plifies a paradox that is not at all confined to American medicine:
investment of more human capital with worse human health out-
comes. The second is the generation of data in massive quantities,
from sources such as high-resolution medical imaging, biosensors
with continuous output of physiologic metrics, genome sequenc-
ing, and electronic medical records. The limits on analysis of such
data by humans alone have clearly been exceeded, necessitating
an increased reliance on machines. Accordingly, at the same time
that there is more dependence than ever on humans to provide
healthcare, algorithms are desperately needed to help. Yet the inte-
gration of human and artificial intelligence (AI) for medicine has
barely begun.
Looking deeper, there are notable, longstanding deficiencies in
healthcare that are responsible for its path of diminishing returns.
These include a large number of serious diagnostic errors, mis-
takes in treatment, an enormous waste of resources, inefficiencies
in workflow, inequities, and inadequate time between patients and
clinicians3,4
. Eager for improvement, leaders in healthcare and com-
puter scientists have asserted that AI might have a role in address-
ing all of these problems. That might eventually be the case, but
researchers are at the starting gate in the use of neural networks to
ameliorate the ills of the practice of medicine. In this Review, I have
gathered much of the existing base of evidence for the use of AI in
medicine, laying out the opportunities and pitfalls.
Artificial intelligence for clinicians
Almost every type of clinician, ranging from specialty doctor to
paramedic, will be using AI technology, and in particular deep
learning, in the future. This largely involved pattern recognition
using deep neural networks (DNNs) (Box 1) that can help interpret
medical scans, pathology slides, skin lesions, retinal images, electro-
cardiograms, endoscopy, faces, and vital signs. The neural net inter-
pretation is typically compared with physicians’ assessments using a
plot of true-positive versus false-positive rates, known as a receiver
operating characteristic (ROC), for which the area under the curve
(AUC) is used to express the level of accuracy (Box 1).
Radiology. One field that has attracted particular attention for
application of AI is radiology5
. Chest X-rays are the most common
type of medical scan, with more than 2 billion performed worldwide
per year. In one study, the accuracy of one algorithm, based on a
121-layer convolutional neural network, in detecting pneumonia in
over 112,000 labeled frontal chest X-ray images was compared with
that of four radiologists, and the conclusion was that the algorithm
outperformed the radiologists. However, the algorithm’s AUC of
0.76, although somewhat better than that for two previously tested
DNN algorithms for chest X-ray interpretation5
, is far from optimal.
In addition, the test used in this study is not necessarily comparable
with the daily tasks of a radiologist, who will diagnose much more
than pneumonia in any given scan. To further validate the conclu-
sions of this study, a comparison with results from more than four
radiologists should be made. A team at Google used an algorithm
that analyzed the same image set as in the previously discussed
study to make 14 different diagnoses, resulting in AUC scores that
ranged from 0.63 for pneumonia to 0.87 for heart enlargement or
a collapsed lung6
. More recently, in another related study, it was
shown that a DNN that is currently in use in hospitals in India for
interpretation of four different chest X-ray key findings was at least
as accurate as four radiologists7
. For the narrower task of detecting
cancerous pulmonary nodules on a chest X-ray, a DNN that retro-
spectively assessed scans from over 34,000 patients achieved a level
of accuracy exceeding 17 of 18 radiologists8
. It can be difficult for
emergency room doctors to accurately diagnose wrist fractures,
but a DNN led to marked improvement, increasing sensitivity from
81% to 92% and reducing misinterpretation by 47% (ref. 9
).
Similarly, DNNs have been applied across a wide variety of
medical scans, including bone films for fractures and estimation of
aging10–12
, classification of tuberculosis13
, and vertebral compression
fractures14
; computed tomography (CT) scans for lung nodules15
,
liver masses16
, pancreatic cancer17
, and coronary calcium score18
;
brain scans for evidence of hemorrhage19
, head trauma20
, and acute
referrals21
; magnetic resonance imaging22
; echocardiograms23,24
;
and mammographies25,26
. A unique imaging-recognition study
focusing on the breadth of acute neurologic events, such as stroke
or head trauma, was carried out on over 37,000 head CT 3-D scans,
which the algorithm analyzed for 13 different anatomical find-
ings versus gold-standard labels (annotated by expert radiologists)
and achieved an AUC of 0.73 (ref. 27
). A simulated prospective,
double-blind, randomized control trial was conducted with real
cases from the dataset and showed that the deep-learning algorithm
could interpret scans 150 times faster than radiologists (1.2 versus
177seconds). But the conclusion that the algorithm’s diagnostic
accuracyinscreeningacuteneurologicscanswaspoorerthanhuman
High-performance medicine: the convergence of
human and artificial intelligence
Eric J. Topol
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along
with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact
at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow
and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health.
The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these
applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely
be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
REVIEW ARTICLE | FOCUS
https://doi.org/10.1038/s41591-018-0300-7
NATURE MEDICINE | VOL 25 | JANUARY 2019 | 44–56 | www.nature.com/naturemedicine44
an
ed
as
tio
rit
da
of
al
an
(T
m
ap
D
an
be
la
Table 1 | Peer-reviewed publications of AI algorithms compared
with doctors
Specialty Images Publication
Radiology/
neurology
CT head, acute
neurological events
Titano et al. 27
CT head for brain
hemorrhage
Arbabshirani et al.19
CT head for trauma Chilamkurthy et al.20
CXR for metastatic lung
nodules
Nam et al.8
CXR for multiple findings Singh et al.7
Mammography for breast
density
Lehman et al.26
Wrist X-ray* Lindsey et al.9
Pathology Breast cancer Ehteshami Bejnordi et al.41
Lung cancer (+driver
mutation)
Coudray et al.33
Brain tumors
(+methylation)
Capper et al.45
Breast cancer metastases* Steiner et al.35
Breast cancer metastases Liu et al.34
Dermatology Skin cancers Esteva et al.47
Melanoma Haenssle et al.48
Skin lesions Han et al.49
Ophthalmology Diabetic retinopathy Gulshan et al.51
Diabetic retinopathy* Abramoff et al.31
Diabetic retinopathy* Kanagasingam et al.32
Congenital cataracts Long et al.38
Retinal diseases (OCT) De Fauw et al.56
Macular degeneration Burlina et al.52
Retinopathy of prematurity Brown et al.60
AMD and diabetic
retinopathy
Kermany et al.53
Gastroenterology Polyps at colonoscopy* Mori et al.36
Polyps at colonoscopy Wang et al.37
Cardiology Echocardiography Madani et al.23
Echocardiography Zhang et al.24
T
C
A
A
iC
Z
B
N
ID
Ic
Im
V
A
M
A
A

•손 엑스레이 영상을 판독하여 환자의 골연령 (뼈 나이)를 계산해주는 인공지능

• 기존에 의사는 그룰리히-파일(Greulich-Pyle)법 등으로 표준 사진과 엑스레이를 비교하여 판독

• 인공지능은 참조표준영상에서 성별/나이별 패턴을 찾아서 유사성을 확률로 표시 + 표준 영상 검색

•의사가 성조숙증이나 저성장을 진단하는데 도움을 줄 수 있음

- 1 -
보 도 자 료
국내에서 개발한 인공지능(AI) 기반 의료기기 첫 허가
- 인공지능 기술 활용하여 뼈 나이 판독한다 -
식품의약품안전처 처장 류영진 는 국내 의료기기업체 주 뷰노가
개발한 인공지능 기술이 적용된 의료영상분석장치소프트웨어
뷰노메드 본에이지 를 월 일 허가했다고
밝혔습니다
이번에 허가된 뷰노메드 본에이지 는 인공지능 이 엑스레이 영상을
분석하여 환자의 뼈 나이를 제시하고 의사가 제시된 정보 등으로
성조숙증이나 저성장을 진단하는데 도움을 주는 소프트웨어입니다
그동안 의사가 환자의 왼쪽 손 엑스레이 영상을 참조표준영상
과 비교하면서 수동으로 뼈 나이를 판독하던 것을 자동화하여
판독시간을 단축하였습니다
이번 허가 제품은 년 월부터 빅데이터 및 인공지능 기술이
적용된 의료기기의 허가 심사 가이드라인 적용 대상으로 선정되어
임상시험 설계에서 허가까지 맞춤 지원하였습니다
뷰노메드 본에이지 는 환자 왼쪽 손 엑스레이 영상을 분석하여 의
료인이 환자 뼈 나이를 판단하는데 도움을 주기 위한 목적으로
허가되었습니다
- 2 -
분석은 인공지능이 촬영된 엑스레이 영상의 패턴을 인식하여 성별
남자 개 여자 개 로 분류된 뼈 나이 모델 참조표준영상에서
성별 나이별 패턴을 찾아 유사성을 확률로 표시하면 의사가 확률값
호르몬 수치 등의 정보를 종합하여 성조숙증이나 저성장을 진단합
니다
임상시험을 통해 제품 정확도 성능 를 평가한 결과 의사가 판단한
뼈 나이와 비교했을 때 평균 개월 차이가 있었으며 제조업체가
해당 제품 인공지능이 스스로 인지 학습할 수 있도록 영상자료를
주기적으로 업데이트하여 의사와의 오차를 좁혀나갈 수 있도록
설계되었습니다
인공지능 기반 의료기기 임상시험계획 승인건수는 이번에 허가받은
뷰노메드 본에이지 를 포함하여 현재까지 건입니다
임상시험이 승인된 인공지능 기반 의료기기는 자기공명영상으로
뇌경색 유형을 분류하는 소프트웨어 건 엑스레이 영상을 통해
폐결절 진단을 도와주는 소프트웨어 건 입니다
참고로 식약처는 인공지능 가상현실 프린팅 등 차 산업과
관련된 의료기기 신속한 개발을 지원하기 위하여 제품 연구 개발부터
임상시험 허가에 이르기까지 전 과정을 맞춤 지원하는 차세대
프로젝트 신개발 의료기기 허가도우미 등을 운영하고 있
습니다
식약처는 이번 제품 허가를 통해 개개인의 뼈 나이를 신속하게
분석 판정하는데 도움을 줄 수 있을 것이라며 앞으로도 첨단 의료기기
개발이 활성화될 수 있도록 적극적으로 지원해 나갈 것이라고
밝혔습니다

저는 뷰노의 자문을 맡고 있으며, 지분 관계가 있음을 밝힙니다

AJR:209, December 2017 1
Since 1992, concerns regarding interob-
server variability in manual bone age esti-
mation [4] have led to the establishment of
several automatic computerized methods for
bone age estimation, including computer-as-
sisted skeletal age scores, computer-aided
skeletal maturation assessment systems, and
BoneXpert (Visiana) [5–14]. BoneXpert was
developed according to traditional machine-
learning techniques and has been shown to
have a good performance for patients of var-
ious ethnicities and in various clinical set-
tings [10–14]. The deep-learning technique
is an improvement in artificial neural net-
works. Unlike traditional machine-learning
techniques, deep-learning techniques allow
an algorithm to program itself by learning
from the images given a large dataset of la-
beled examples, thus removing the need to
specify rules [15].
Deep-learning techniques permit higher
levels of abstraction and improved predic-
tions from data. Deep-learning techniques
Computerized Bone Age
Estimation Using Deep Learning–
Based Program: Evaluation of the
Accuracy and Efficiency
Jeong Rye Kim1
Woo Hyun Shim1
Hee Mang Yoon1
Sang Hyup Hong1
Jin Seong Lee1
Young Ah Cho1
Sangki Kim2
Kim JR, Shim WH, Yoon MH, et al.
1
Department of Radiology and Research Institute of
Radiology, Asan Medical Center, University of Ulsan
College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu,
Seoul 05505, South Korea. Address correspondence to
H. M. Yoon (espoirhm@gmail.com).
2
Vuno Research Center, Vuno Inc., Seoul, South Korea.
Pediatric Imaging • Original Research
Supplemental Data
Available online at www.ajronline.org.
AJR 2017; 209:1–7
0361–803X/17/2096–1
© American Roentgen Ray Society
B
one age estimation is crucial for
developmental status determina-
tions and ultimate height predic-
tions in the pediatric population,
particularly for patients with growth disor-
ders and endocrine abnormalities [1]. Two
major left-hand wrist radiograph-based
methods for bone age estimation are current-
ly used: the Greulich-Pyle [2] and Tanner-
Whitehouse [3] methods. The former is much
more frequently used in clinical practice.
Greulich-Pyle–based bone age estimation is
performed by comparing a patient’s left-hand
radiograph to standard radiographs in the
Greulich-Pyle atlas and is therefore simple
and easily applied in clinical practice. How-
ever, the process of bone age estimation,
which comprises a simple comparison of
multiple images, can be repetitive and time
consuming and is thus sometimes burden-
some to radiologists. Moreover, the accuracy
depends on the radiologist’s experience and
tends to be subjective.
Keywords: bone age, children, deep learning, neural
network model
DOI:10.2214/AJR.17.18224
J. R. Kim and W. H. Shim contributed equally to this work.
Received March 12, 2017; accepted after revision
July 7, 2017.
S. Kim is employed by Vuno, Inc., which created the deep
learning–based automatic software system for bone
age determination. J. R. Kim, W. H. Shim, H. M. Yoon,
S. H. Hong, J. S. Lee, and Y. A. Cho are employed by
Asan Medical Center, which holds patent rights for the
deep learning–based automatic software system for
bone age assessment.
OBJECTIVE. The purpose of this study is to evaluate the accuracy and efficiency of a
new automatic software system for bone age assessment and to validate its feasibility in clini-
cal practice.
MATERIALS AND METHODS. A Greulich-Pyle method–based deep-learning tech-
nique was used to develop the automatic software system for bone age determination. Using
this software, bone age was estimated from left-hand radiographs of 200 patients (3–17 years
old) using first-rank bone age (software only), computer-assisted bone age (two radiologists
with software assistance), and Greulich-Pyle atlas–assisted bone age (two radiologists with
Greulich-Pyle atlas assistance only). The reference bone age was determined by the consen-
sus of two experienced radiologists.
RESULTS. First-rank bone ages determined by the automatic software system showed a
69.5% concordance rate and significant correlations with the reference bone age (r = 0.992;
p < 0.001). Concordance rates increased with the use of the automatic software system for
both reviewer 1 (63.0% for Greulich-Pyle atlas–assisted bone age vs 72.5% for computer-as-
sisted bone age) and reviewer 2 (49.5% for Greulich-Pyle atlas–assisted bone age vs 57.5% for
computer-assisted bone age). Reading times were reduced by 18.0% and 40.0% for reviewers
1 and 2, respectively.
CONCLUSION. Automatic software system showed reliably accurate bone age estima-
tions and appeared to enhance efficiency by reducing reading times without compromising
the diagnostic accuracy.
Kim et al.
Accuracy and Efficiency of Computerized Bone Age Estimation
Pediatric Imaging
Original Research
Downloadedfromwww.ajronline.orgbyFloridaAtlanticUnivon09/13/17fromIPaddress131.91.169.193.CopyrightARRS.Forpersonaluseonly;allrightsreserved
• 총 환자의 수: 200명

• 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스

• 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험)

• 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독)

• 인공지능: VUNO의 골연령 판독 딥러닝
AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380.

40
50
60
70
80
인공지능 의사 A 의사 B
69.5%
63%
49.5%
정확도(%)
영상의학과 펠로우

(소아영상 세부전공)
영상의학과

2년차 전공의
인공지능 vs 의사




골연령 판독에 인간 의사와 인공지능의 시너지 효과
Digital Healthcare Institute
Director,Yoon Sup Choi, PhD
yoonsup.choi@gmail.com

40
50
60
70
80
인공지능 의사 A 의사 B
40
50
60
70
80
의사 A  
+ 인공지능
의사 B  
+ 인공지능
69.5%
63%
49.5%
72.5%
57.5%
정확도(%)
영상의학과 펠로우

(소아영상 세부전공)
영상의학과

2년차 전공의
인공지능 vs 의사 인공지능 + 의사




골연령 판독에 인간 의사와 인공지능의 시너지 효과

총 판독 시간 (m)
0
50
100
150
200
w/o AI w/ AI
0
50
100
150
200
w/o AI w/ AI
188m
154m
180m
108m
saving 40%
of time
saving 18%
of time
의사 A 의사 B
골연령 판독에서 인공지능을 활용하면

판독 시간의 절감도 가능





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