CURRENT POSITION

Distinguished Professor
College of Medical Science and Technology
​Taipei Medical University
Chief & Dermatologist
Department of Dermatology/
Skin Laser Center
Taipei Medical University - Wanfang Hospital
Editor-in-Chief
International Journal for Quality in Health Care (published jointly by Oxford University Press and ISQua)
BMJ Health & Care Informatics journal (the official journal of FCI and published by BMJ)
President-Elect / President
IMIA plays a major global role in the application of information science and technology in the fields of healthcare and research in medical, health and bio-informatics.

EDUCATION

M.D.
Medicine

Taipei Medical University
​1984-1991

Ph.D
​Medical Informatics

University of Utah School of Medicine
​1991-1994

EXPERTISE

AWARDS

衛生獎章
​
​​

行政院衛生署,2007
​

PUBLICATION

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

Key Points
Question:  Can deep learning develop a risk-prediction model without information on UV exposure or specific lesions to predict nonmelanoma skin cancer in an Asian population?

Findings:  A deep learning convolutional neural network was trained using nonimaging and sequential medical records to predict the development of nonmelanoma skin cancer. The area under the receiver operating characteristic curve for the model was 0.89; several potential determining factors for prediction such as history of precancerous lesions and use of some photosensitizing medications were also identified.

Meaning:  A machine learning algorithm can accurately identify individuals at high risk of nonmelanoma skin cancer and may help to optimize prevention interventions and adherence.

Hsiao-Han Wang, Yu-Hsiang Wang, Chia-Wei Liang, Yu-Chuan Li* (2019). JAMA Dermatology. Published online September 4, 2019.

Toward Precise and Preventive Healthcare with Computational Tools

Precision and prevention medicine is emphasized recently to improve healthcare quality and effectiveness. To achieve such a goal, computational tools play an important role in providing more accurate indices of diagnosis and prognosis, or disease management. This month's Editor's Choice articles demonstrate how computational tools could help classify, visualize and manage health data.

The first Editor's Choice article, “Evolution of linear and nonlinear fetal heart rate indices throughout pregnancy in appropriate, small for gestational age and preterm fetuses: A cohort study,” reveals that linear and nonlinear fetal heart rate (FHR) analysis by special computational algorithm is used to show the information on the evolution of fetal autonomic nervous throughout pregnancy. Gonçalves et al. analyzed the FHR of three groups of fetuses: appropriate for gestational age (AGA), small for gestational age (SGA), and fetuses born preterm (PTB). Some indices from FHR analysis demonstrated accurate classification power with area under ROC curve (auROC) above 0.75, have the potentials to identify SGA and PTB in clinical practice.
Chiu, H. W., & Jack Li, Y. C. (2018). Toward precise and preventive healthcare with computational tools. Computer Methods and Programs in Biomedicine, 153, A1.

Using Modified Information Delivery to Enhance the Traditional Pharmacy OSCE Program at TMU – a Pilot Study

Highlight
  • Continuum of competency (one scenario – multiple continuous missions); 2) Traditional OSCE (one mission per station)
  • Critical reasoning is difficult to teach and assess using only standardized patients and medical personnel – therefore iOSCE was developed as solution
  • Simulation modalities each has its own limitations, thus, we integrate the Virtual Patient in order to aid trainees during their assessment
  • Information technology (e.g. VP) can offer solutions for healthcare education by enhancing critical thinking and clinical reasoning.​
Lin, Che-Wei & H. Chang, Elizabeth & L Clinciu, Daniel & Peng, Yun-Ting & Huang, Wen-Chen & Wu, Chien-Chih & Wu, Jen-Chieh & Li, Yu-Chuan. (2017). Using Modified Information Delivery to Enhance the Traditional Pharmacy OSCE program at TMU – a Pilot Study. Computer Methods and Programs in Biomedicine.

The Usefulness and Actual Use of Wearable Devices Among the Elderly Population

Highlight
  • A combined study of review on the usefulness of available wearable devices and survey on actual use of wearable devices.
  • There are increasing number of wearable devices made available which can be used for elderly age groups.
  • Age and sex equality and health status need to be considered during clinical assessment of the devices.
  • The awareness and use of wearables among elderly is scant.
  • Physical and mental activity wearable monitors are among the top future preferred devices.
Shewatambara Kekade, Chung-Ho Hseieh, Mohaimenul Islam, Suleman Atique, Abdulwahed Mohammed Khalfan, Yu-Chuan Li, Shabbir Syed Abdul*. The usefulness and actual use of wearable devices among the elderly population. Computer Methods and Programs in Biomedicine 2018(153):137-159.

Does Aspirin Use Reduce the Risk for Cancer?

Recently, studies have reported that aspirin has chemopreventive properties. In this study, we used the Taiwan NHI database, which covers a population of 23 million (99.99%) Taiwanese from 2001 to 2011. This was a case–control study which identified 601,733 patients using ICD-9-CM codes who were diagnosed with cancer. Each case with 4 eligible controls was matched for age, sex, and index date and adjusted for confounding factors. The observed overall cancer risk (adjusted OR (AOR), 0.95; 95% CI 0.94 to 0.96) reduced with aspirin use, specifically, colorectal (AOR, 0.97; 95% CI 0.94 to 0.99) and digestive system (AOR, 0.96; 95% CI 0.94 to 0.98) cancers. Findings from the Asian population would contribute to the discussion on aspirin's safety profile.
Usman Iqbal, Yang Hsuan Chia, Jian Wen Shan, Yen Yun, Li Yu Chuan. Does aspirin use reduce the risk for cancer? Journal of Investigative Medicine 2017(65):391-392

BOOKS

AI醫療大未來

作者：李友專
出版社：好人出版
出版時間：2018
ISBN：9789-8692-7513-2

健康醫療資訊科技
​發展政策建言

作者：李友專等人
出版社：國家衛生研究院
出版時間：2018
ISBN：9789-8605-5133-4

建立學習型健康系統之大數據基礎

作者：李友專等人
出版社：國家衛生研究院
出版時間：2018
ISBN：9789-8605-6237-8

醫學資訊管理學
​3版

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2018
ISBN：9789-8619-4503-3

醫學資訊管理學
​2版

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2013
ISBN：9789-8619-4293-3

醫療資訊管理概論

作者：龐婷文, 李友專等
出版社：華格那出版有限公司
出版時間：2010
ISBN：9789-8663-3562-4

醫學資訊管理學

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2006
ISBN：9861-9400-06

國際醫院評鑑
​JCI實戰經驗分享

副總編校：李友專等
出版社：華杏出版股份有限公司
出版時間：2006
ISBN：9572-9228-15

病人安全-理論與實務

主編：邱文達, 李友專等
​出版社：台灣醫務管理學會
出版時間：2004
​ISBN：9570-1725-09

醫療資訊管理學

作者：吳昭新, 李友專等
出版社：偉華書局有限公司
出版時間：2002
ISBN：9576-4050-68

簡明醫學資訊學

總審閱：李友專
出版社：合記圖書出版社
出版時間：2002
​ISBN：9576-6688-75

PATENT

[ Intelligent Personalize Medication Reminding System ]

＃I499995
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang, Hsien Chang Li

[ Ultrasonic and Ozone Cleaning Device Without Water Sink ]

＃M444228
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang

[ Mobile Personal Electronic Health Record System ]

＃M409487
Shabbir-Syed Abdul, Wen-Shan Jian, Yu-Chuan Li ​

PROJECT

Artificial Intelligence for Earlier and Safer Medicine Worldwide

Ministry of Science and Technology, 2018.01~2021.12
Aging population in Taiwan is growing rapidly. 12.5 percent of the population is aged 65 or older. Due to the incidence of chronic diseases and degenerative illnesses in the aging population, it has increased the chances of polypharmacy. On average, Taiwanese visits hospital about 14 times in a year and gets at least 4-5 medicines every time. Therefore, the total number of prescription per year is about 345 million. Research has shown that the rate of medication error is 0.5% to 5% which means approximately 0.75 - 7.5 million of dispensed prescriptions in Taiwan are inappropriate. The current clinical decision support system can not prevent medication error efficiently. It is also claimed that 193 tons of medicine have been wasted annually because of poor patient compliance in Taiwan.

Artificial Intelligence (AI) has been used extensively and is expected that machine learning, deep learning and natural language processing will become the major technology in next 5 to 10 years. AI, machine learning especially, is showing its potential in medicine field due to the availability of big data. As Taiwan has the world-class medical electronic data such as electronic medical records and health insurance database, it is a great opportunity to apply big data to develop AI in medicine.

In this project, we aim to develop artificial intelligence for earlier and safer medicine in Taiwan as well as worldwide which includes (1) Create model and system for earlier and safer medicine; (2) Develop artificial empathy; (3) Randomized controlled trial and (4) Medical institutions used and reinforcement learning. It will exhibit Taiwan's strengths to promote the wisdom of medical safety and enhance patient compliance. We will connect industry, government, academia, research and medical between Taiwan and other countries to build a AI with Humanity, for Humanity.
​
In the same time, this project will provide medical databases containing disease-disease, disease-medication and medication-medication association. By sharing medical resource, It will help to improve the application of medical treatments, ensure the safety and quality for healthcare.

Building MedGuard for Drug Safety

Ministry of Science and Technology, 2017.05~2017.12
Medication error is the most common medical error. Due to the complexity of drug treatment process, medication error could happen anytime when writing the prescription, manufacturing the formulation, dispensing the formulation, taking the medicine, and so on. The rule-based medication safety system is widely used to identify the accuracy of the medication information. However, there is about 50-90% overridden rate for alerts, it expressed that rule-based medication safety system is not useful. More, it is difficult to determine its feasibility and usefulness without Randomized Controlled Trial (RCT). With heightened concern about medication-use safety and medication errors, information technology (IT) could be a means to effectively lower the risk and the chance of medication errors.
​
In this project, we develop an AI model for advanced electronic safety of prescriptions. The prototype is based on Probabilistic method. We built up a knowledge base with “Big data” analyzing the associations between Disease-Medication (DM) and Medication-Medication (MM) and we further developed AOP model (Appropriateness of Prescription). The core technique has been applied in western prescriptions and Chinese prescriptions and also published in International Journals (PLOS One、pharmacoepidemiology and drug safety). We also get a U.S. patent (Patent US20150356272). According to Technology Readiness Level(TRL), MedGuard achieves TRL 6 (System/subsystem model or prototype demonstration in a relevant environment). 

This project will create project team and expert consultants team to help MedGuard achieve TRL 7-8 (product evaluation. MedGuard is a software module and it can be customized as APP, server, and Cloud. The main characters in the industry value chain are Health Administration or hospitals and pharmaceutical companies and insurance companies. Many statistics indicate that medication-use safety has great potential in business and value in terms of the patient safety, healthcare expenditure, medical audit fee, etc. Commercializing MedGuard not only reduce the risk of medication errors, but also improve healthcare quality and significantly decrease the medical waste and expense.

Enhancing Intelligent Healthcare with Usage of International Cooperative Research in Health Informatics

Ministry of Health and Welfare 2017.08~2018.04
The gradual aging of the Taiwanese population has strained the government’s ability to provide healthcare for such a large number of elderly citizens. This challenge has caused various healthcare policies to be enacted urgently. People have become more aware of the advances in medical technology that can improve their lives and now have higher expectations for how their own personal healthcare data should be leveraged to meet these expectations. Personalized healthcare, information technology, and cloud computing have opened up all kinds of new healthcare applications and possibilities. Information and communication technology as well as ehealth may lower costs and unite once disparate industries. Electronic health records are expected also to empower patients, increase healthcare literacy, increase safety, and encourage patient compliance. Patients can also participate in their own health diagnoses and make informed decisions.

Establishing an ehealth hub where various parties can share data is the main topic of this research proposal. This ehealth hub follows the Institute of Medicine’s 2007 promotion of Learning Health Systems. Charles P Friedman, a pioneer and professor with extensive experience in designing Learning Health Systems, was invited to be a mentor in this project. Through this international cooperation, sharing ideas, holding workshops, meetings, and incorporating the perspectives from different domains can be possible within this ehealth hub.

​Smoking is the largest avoidable cause of preventable morbidity and premature mortality worldwide. The prevalence of smoking worldwide is estimated at about one billion smokers, half of which will die prematurely as a consequence of their addiction. Smoking causes approximately 85% of the 3 cases of lung cancer and chronic obstructive pulmonary disease (COPD) and contributes to the development of many other lung diseases, such as Cancer of the trachea, bronchus and lung. We've passed a European Union’s Horizon 2020 research about smoke free brain. Using the wearable technology plus a mobile App that is programmed to push tailored messages for health concern and readiness to quit, tips or sustaining abstinence, use of interactive self-assessments, helpful cessation information, and access to physical activity charts.

​These database will be collected to become knowledge, through LHS system to mechanical learning. With the advancement in the medical sciences, cancers are now considered as chronic disease rather than fatal illness. This moves the focus in the fight against cancer from sustaining life towards maximizing functional capacity and Quality Of Life (QOL). A critical element in this shift has been the rise of active rehabilitation in the management of cancer. We will cooperate with CATCH project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 722012.

Furthermore, overall physical activity and health behavioural change is increasingly becoming important in cancer management. Technology advances such as gamification based on biofeedback, and neuromuscular electrical stimulation, can help address some of these information and communication technology into LHS cycle. To improve cancer patients physical activity, health behavioural and QOL. Keywords: Information and Communication Technology (ICT), eHealth, Learning Health System, Electronic Health Record, wearable technology, International Collaboration. 

TALK

​[​ AI for Safer and Earlier Medicine]

AICI 2020​
Vietnam | 2020.01.04

[​​AI學術應用到商業化：痣能達人-皮膚痣人工智能風險辨識系統]

Medical x AI Patent 揭開醫療AI新序章：商業應用的挑戰
臺北醫學大學
Taiwan | 2019.12.23

[​AI FOR SAFER AND EARLIER MEDICINE]

QS-APPLE 2019
QS Quacquarelli Symonds
Japan | 2019.11.27

[​AI for Earlier and Safer Medicine]

RESI Taipei
RESI
Taiwan | 2019.11.14

[TEDxTaipei]

TEDxTaipei 2019 年會：Come．Unity 我，我們
TEDxTaipei
Taiwan | 2019.10.06

[台灣醫學資訊學術主管同仁聯誼]

2019年國際醫學資訊聯合研討會(JCMIT2019) 暨全國醫院資訊主管會議
輔仁大學、輔大醫院、中央研究院資訊科學研究所、台灣醫學資訊學會、iThome
Taiwan | 2019.10.04-05

[醫療AI走向同理心]

2019健康照護聯合學術研討會
台灣醫務管理學會
Taiwan | 2019.10.03-04

[An AI Perspective on Individualized Cancer Risk]

5th Taiwan-Japan ARO Workshop
一般社団法人ARO協議会、TSPA(Taiwan)
Japan | 2019.09.26

[AI for Earlier and Safer Medicine]

D Forum 2019 智慧醫療論壇
DIGITIMES
Taiwan | 2019.09.20

[先覺醫療(Earlier Medicine)]

108年度三軍總醫院主管共識營
三軍總醫院
Taiwan | 2019.08.17

[AI FOR ''EARLIER MEDICINE]

2nd International Conference on AI in Healthcare
AHMC Healthcare Inc.
USA | 2019.07.19-21

[Opening remarks: collaborating with TMU]

臺北醫學大學生醫加速器臨床策略工作坊
臺北醫學大學事業發展處
Taiwan | 2019.07.05

[Panel Discussion- Healthcare Takeoff: Fly with AI and IoT]

BE #2nd Batch Demo Day
BE Accelerator, BE Capital
Taiwan | 2019.07.01

[先覺醫療 (Earlier Medicine)]

科創講堂 主題講堂Ⅰ 醫療先鋒
科學人雜誌、清華大學
Taiwan | 2019.06.29

[醫療大數據及人工智慧在臨床應用 AI for Safer and Early Treatment]

裕利集團全球策年會之台灣醫藥發展論壇
裕利集團
Taiwan | 2019.06.26

[AI for Safer and Earlier Medicine
AI醫療大未來]

2019 臺北榮民總醫院 國際醫學教育研討會：智慧醫院的醫學教育
臺北榮民總醫院教學部
Taiwan | 2019.06.21

[早覺醫療]

[2019 TAICS標準論壇] 產業AI化應用發展趨勢
台灣資通產業標準協會
Taiwan | 2019.06.13

[AI for Safer and Earlier Medicine]

健康照護整合及品質提升研討會
社團法人台灣醫務管理學會
天主教耕莘醫療財團法人永和耕莘醫院
Taiwan | 2019.05.31

[AI basics and healthcare applications]
​[Teledermatology in Taiwan, where to begin?]

AMWC Asia-TDAC 7th Taiwan Dermatology Aesthetics Conference​
Taiwanese Dermatological Association
Taiwan | 2019.05.10-13

[AI for Earlier and Safer Medicine]

藥事人員繼續教育研討會
臺北榮民總醫院‧臺北市藥師公會
Taiwan | 2019.05.05

[AI for Safer Medicine]

醫策會20國際論壇
財團法人醫院評鑑暨醫療品質策進會
Taiwan | 2019.04.26

[AI for Earlier and Safer Medicine]

How to Accelerate Taiwan Biomedical Policy Momentum as an Asia-Pacific Biomedical R +D Hub?
加速台灣成為亞太地區生醫R&D中心論壇​
​中華民國開發性製藥研究協會​
Taiwan | 2019.04.25

[智慧醫療新趨勢Medical 4.0 醫療AI新藍海- 先覺醫療(Earlier Medicine)]

花旗商業金融 醫療產業趨勢座談會
花旗銀行
Taiwan | 2019.03.20

[DIGITAL HEALTH INNOVATIONS AND TRENDS TO WATCH]

Panel Discussion
BE Accelerator, BE Capital
Taiwan | 2019.03.19

[AI醫療大未來]

聯新國際醫院課程
聯新國際醫院
Taiwan | 2019.03.09

[AI醫療大未來]

台灣人工智慧學校
台北總校第四期開學典禮
Taiwan | 2019.02.23

[AI醫療大未來]

2019 Taiwan Innovation and Entrepreneurship Forum
TAIWAN| 2019.01.21

[AI in Medicine: Around the World (Panelist)]

AI Med North America 2018
More AIMed
USA| 2018.12.13-15

[​​醫界對AI應用觀點分享座談會]

財團法人生物技術開發中心
南港生技育成中心
Taiwan | 2018.11.21

探索人類疾病的宇宙 

TEDxXinyi
​臺灣 | 2014.03.14

NEWS

《終疾戰疫 武漢肺炎》特別報導 第2集

壹電視新聞台   | Mar 07, 2020
主持人：陳雅琳
段落：突破中國蠻橫封鎖！台AI權威醫踢中突圍WHO！ （影片時間為23:37-33:48）

世衛中國化...影武者竟是她?! 疫情失控譚德塞忙舔共 讓世衛淪維尼快樂組...

 《消失的國界》 三立新聞台 | Feb 15, 2020
主持人 : 李天怡
李友專教授將首次出任WHO認可的非政府組織IMIA「國際醫療資訊協會」主席，臺灣醫療與AI，正積極走向世界。
（影片時間為7:30-11:48）

New Editor-in-Chief for BMJ Health & Care Informatics journal

InPublishing   | Feb 13, 2020
Professor Yu-Chuan (Jack) Li, a researcher of artificial intelligence (AI) in medicine and medical informatics, and a practicing dermatologist, has been appointed as the new Editor-in-Chief of BMJ Health & Care Informatics. ​​...more

突破中國封鎖！李友專獲選WHO國際醫療資訊會主席

民視新聞   劉忻怡、李建緯 | Feb 12, 2020
​台灣一直希望能正式參與WHO，但其實早在去年8月，WHO旗下的正式組織「國際醫療資訊協會」，就提名台灣的AI權威李友專出任主席，可以說是突破中國封鎖。來看看，李友專如何成功出擊。​​...more

2020大未來｜智慧醫療｜李友專：過程跟結果都重要！精準與預測醫學使更多數據被留下

未來城市@天下   劉子寧 | Jan 21, 2020
迎來5G落地、AI應用更多元的2020年，未來城市推出「2020大未來」系列，邀請各界專家分享趨勢觀察。 首先上場的是台北醫學大學醫學科技學院院長李友專，他是一名皮膚科醫師，也是國際級人工智慧和醫學資訊專家，將在2021年就任國際醫療資訊協會（IMIA）主席。以下是他的觀點： ​​...more

2019年度第十六屆國家新創獎 臨床新創組 ​痣能達人-皮膚痣人工智能風險辨識系統

國家新創獎   李友專、靳嚴博、王筱涵、林昱廷、侯則瑜
​皮膚癌在台灣十大死因中排名第九名(根據衛生福利部2018年所發表的統計資料)。在皮膚癌中，以黑色素細胞瘤(Melanoma)最為致命，全球每年有約30萬人死於此病症，且在過去30年間黑色素細胞瘤的發生率持續上升。第四期黑色素細胞瘤的5年存活率只有15%，所以早期發現，早期治療對於改善預後至關重要。​​...more

數位醫療新創應有實際效益吸引投資人、早覺醫療成AI趨勢

環球生技雜誌   李林璦、巫芝岳、吳培安 | Nov 14, 2019
今(14)日，北美最大健康醫療早期投資會議「RESI」首次於台灣舉辦，會議中也特別針對數位醫療相關投資，及智慧醫院領域分別設計論壇，邀請包括比翼資本陳彥諭、能率亞洲投資John Lin、Pacific 8 Ventures傅斯誠、富士康健康科技投資事業體Benjamin Hu、工研院創投Dennis Lee等國內眾多投資專家，以及臺北醫學大學醫學科技學院院長李友專與會。 ​​...more

​AI適用於早覺醫療 協助實現醫療品質與病人安全

​DIGITIMES    DIGITIMES企劃 | Oct 24, 2019
AI歷經了不同時期的演變，1960年隨著啟發式搜尋的發展，遂正式展開以邏輯為基礎的推理時代序幕，到了1990年在知識工程與專家系統的推波助瀾下，正式進入以法則為基礎的表示法時代。進入2015年自主學習開始藯為風潮，進而帶動了由大數據驅動的機器學習時代到來，甚至有人預測2045年會進入超級智慧時代。​​...more

FDA公布輔助臨床決策AI軟體法規指南 李友專籲政府及早跟進規範

​醫藥新聞週刊    楊琇雯 | Oct 21, 2019
美國FDA日前公布《輔助臨床決策AI軟體法規指南》，針對Clinical Decision Support (CDS)系統聚焦在高風險、應用在緊急狀況下的軟體，以及應用機器學習(ML)演算法這類可能無法充分向使用者解釋其背後邏輯的程式，描述FDA對其監管辦法，類型包含​​...more

李友專掌舵 一校三院總動員

​醫藥新聞週刊    邱玉珍 | Oct 14, 2019
全球興起AI浪潮，早在1998年就開始投入AI醫療研究的臺北醫學大學，在醫學科技學院院長李友專醫師推動下，這幾年能量大爆發，帶領北醫大體系醫療院所打造AI醫院，成為國內醫療院所領頭羊。​​...more

如何發展有溫度的AI？ 李友專：同理心與換位思考​

​DIGITIMES    蔡騰輝 | Oct 7, 2019
「很多人希望將人類智慧用電腦的方式呈現，然而，人類是否真的有辦法，將所有的智慧都明確講出來。」針對醫療人工智慧的發展，臺北醫學大學醫學科技學院院長李友專提出了這樣的反思。​​...more

The Crossover of A.I. into Vertical Industries

​InnoVEX    InnoVEX Team | Oct 4, 2019
Digital transforming and challenges within integrating startups and A.I. technologies​​...more

2019北醫x MIT數據松 跨域團隊打造未來智慧ICU藍圖

​​環球生技月刊    李林璦 | Sep 29, 2019
今(29)日，臺北醫學大學與美國麻省理工學院(MIT)計算機科學與人工智慧實驗室(CSAIL)聯合舉辦的第四屆數據松(Datathon)競賽圓滿落幕，此屆榮獲首獎為「AI輔助醫療影像檢傷應用」，團隊成員橫跨放射腫瘤科醫師、藥師、AI工程師、醫學影像專家、資工專家等，跨領域碰撞出精彩的火花​​...more

臺灣獲2023年醫療資訊國際大會Medinfo主辦權李友專榮任下屆理事長

環球生技月刊    李林璦 | Aug 27, 2019
今(27)日，在法國里昂舉行之2019國際醫療資訊協會(IMIA)年會理事會議中，張博論理事長及朱基銘理事帶領台灣醫學資訊協會(TAMI)爭取到舉辦2023年Medinfo國際醫學資訊協會大會的主辦權！​​...more 

先覺醫療 (Earlier Medicine)

科學人    科學人 | Aug 12, 2019
AI的發展可以回溯到1960年代，當時叫做Age of Reasoning，主要應用在於​如何建立電腦的邏輯推理原則；到了90年代進入Age of Representation，研究如何抽取人類的知識放進電腦裡，不過由於人類也難以清楚的表達自己知識的學習歷程，因此遇到了技術瓶頸；​​...more

人工智能發展快 在醫界扮要角

世界日報World Journal    陳開 | Jul 20, 2019
第二屆國際人工智能醫學大會（ICAIH）19日晚在阿罕布拉醫學中心（Alhambra Hospital Medical Center）舉行，諸多學者、政商界代表共襄盛舉。​​...more

有按時吃藥嗎？人工智慧能幫病人「聽醫師的話」— 早覺醫療：醫療AI的新戰場（下）

未來城市＠天下    李友專 | May 17, 2019
早覺醫療的核心精神，是在各階段健康照護中再多道守門把關，運用AI特性幫助各項照護能夠更加準確、及時、個人化、可執行。因為，要乖乖聽醫生的話，真的很難！​​...more

超越仁醫仁術

​《遇見十年後的你》大愛電視
​人工智慧在醫療體系中能做到的，遠比你想像得更多！手術房裡，手術機器人已經是醫師不可或缺的夥伴，若加入大量醫學影像來訓練人工智慧，未來它將能獨立完成手術；看病前，人工智慧幫你初步導診，替你掛號，隨時關心你的身體狀況；在診間，人工智慧化身為第二醫師，吸取最新醫療研究結果，提供適合病人最適合的治療方法。無疑的，人工智慧終將是醫生的最佳幫手，只是，醫病關係的健全互動，仍仰賴視病如親、擁有仁術的仁醫們。

如何掌握醫療體系的「三不管地帶」？早覺醫療：醫療AI的新戰場（上）

未來城市＠天下    李友專 | Apr 12, 2019
古有云：「上醫醫未病，中醫醫欲病，下醫醫已病」。以現代醫學來說，「醫未病」屬於預防醫學範疇，「醫已病」屬於急性醫學範疇。那醫欲病呢？​​...more

【數位DNA】不是恐嚇！如果個人能力還沒數位升級，你的工作就快「out」了​

Cheers    蘇思云 | Mar, 2019
如果還認為數位技能與自身職務無關，就要小心了！從餐廳老闆到大企業員工都要注意的的數位化浪潮，已經襲來，萬一個人能力還沒「數位化」，當心像無法聯網的2G手機，遲早要被市場淘汰。​​...more

電腦斷層掃描的兩難：To CAT or not to CAT，that is the question​

未來城市＠天下    李友專 | Feb 20, 2019
一次電腦斷層掃描會產生相當於八萬公斤核食的輻射量，但也可能早期發現癌症。我們應該避之唯恐不及，還是寧可錯殺也不放過？運用AI，或許能作為其利弊權衡之計。​...more

AI是實現精準醫療唯一途徑

廣編企劃 | 商業周刊 Feb 11, 2019​
根據PWC調查，全球醫療產業超過九成認為「精準醫療」是未來趨勢。美國2015年率先投入340億美元，英、法、中也急起直追投注基因研究。知名醫學期刊Lancet曾預言AI將是健康領域重要議題，現在市場上已見到美國科技巨頭們FAANG、中國BAT爭相搶進，看好AI在精準醫療帶來龐大商機！...more

AI醫療大未來：台灣第一本智慧醫療關鍵報告​

《飛碟早餐 唐湘龍時間》
主持人 : 唐湘龍
2019.01.29 周二醫療保健單元邀請臺北醫學大學醫學科技學院院長 李友專，與聽眾導讀書籍《AI醫療大未來：台灣第一本智慧醫療關鍵報告》​。

AI狂潮！娛樂、醫療、科技...直擊AI應用3大現場

今周刊編輯團隊 | 今周刊 Jan 23, 2019​
去年12月某個周日，台大法律學院霖澤館一樓會議廳，舉辦一場名為「AI（人工智慧）嘉年華」的活動。從社群網路、心理學、政治、法律、音樂到每10秒鐘產生的短視頻、身體的DNA，各種AI應用的想像力，被無窮盡放大。...more

臺灣不該是坐在醫療金礦上的乞丐，還用大鎖擋住AI發展

吳元熙 | 數位時代 Jan 16, 2019​
人工智慧（AI）與醫療碰撞，將在2019產生哪些火花？台北醫學大學醫學科技學院院長李友專認為，「友善的醫療大數據環境、精準早期偵測與預防、人性化應用」等三項AI趨勢，將會逐漸顯現。2018年，人工智慧（AI）在醫療領域光芒四射，留下諸多美好成果。國際間最大消息...more

FREE RESOURCE

The Disease Map
疾病地圖

The introduced disease maps visualise how human disorders are 'positioned' against each other in a population with respect to an individual biological organism. It is well known that many human disorders are risk factors for or consequences of other disorders. While many of such positive associations between disorders are well known and understood, many remain either entirely unnoticed or not properly recognised. Moreover, it is reasonable to assume that certain human disorders can be associated negatively, that is, a person being affected with A, has decreased chances to get affected with B (can be, but not necessary, vice versa). The presented visualisation is a novel tool for validating already known as well as discovering new, never noticed, empirical association patterns between human disorders. While some of such patters can reflect specifics of a healthcare system, many can reveal interesting biological phenomena relevant practically.
References: Shabbir Syed-Abdul, Max Moldovan, Phung-Anh Nguyen, Ruslan Enikeev, Wen-Shan Jian, Usman Iqbal, Min-Huei Hsu, Yu-Chuan Li (2015) Profiling phenome-wide associations: a population-based observational study
Max Moldovan, Ruslan Enikeev, Shabbir Syed-Abdul, Alex Nguyen, Yo-Cheng Chang and Yu-Chuan Li (2014) Disease universe: Visualisation of population-wide disease-wide associations

Phenome-wide Associations Database
疾病關聯資料庫

​To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way.In this population-based observational study, we analyzed 782 million outpatient visits and 15,394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million individuals. Our data was obtained from Taiwan’s National Health Insurance Research Database.
Reference: Profiling phenome-wide associations: a population-based observational study. Shabbir Syed-Abdul Max Moldovan Phung-Anh Nguyen Ruslan EnikeevWen-Shan Jian Usman Iqbal Min-Huei Hsu Yu-Chuan Li, Journal of the American Medical Informatics Association, Volume 22, Issue 4, 1 
Yu-Chuan (Jack) Li
College of Medical Science and Technology
​Taipei Medical University
       

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