The document discusses global trends in the digital healthcare industry and regulation. It notes that in 2018, a record $14.6 billion was invested globally in digital health, continuing a trend of annual increases since 2015. However, Korea does not have any of the 38 digital health unicorn startups valued over $1 billion that exist globally. It defines key terms like digital health, mHealth, and personal genomics. It also discusses regulatory issues and the increasing role of artificial intelligence. The future of digital medicine is that it will become integrated into ordinary medicine.
This document discusses digital healthcare and artificial intelligence in medicine. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea. It details his background and accomplishments, including establishing the first research institute for digital healthcare in Korea. It also discusses his investments and advisory work with several healthcare startups. The document promotes Dr. Choi's book on medical artificial intelligence and its potential to transform the conservative medical system.
The document summarizes the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It discusses how artificial intelligence is reshaping the conservative medical system and how quickly AI is developing and influencing healthcare. The convergence of information technology, biotechnology, and medicine is creating innovation that will transform medical education and clinical practice.
When digital medicine becomes the medicine (1/2)Yoon Sup Choi
This document introduces the journal npj Digital Medicine, which focuses on digital medicine. Digital medicine uses digital tools like biosensors, algorithms, and artificial intelligence to upgrade healthcare by individualizing it and making it data-driven. It has the potential to democratize healthcare by empowering individuals. The journal aims to foster collaboration between the many fields involved in digital medicine like computer science, healthcare, engineering, and more to accelerate progress. It argues digital innovations can help address challenges in current healthcare systems by providing more personalized care at sustainable costs while also improving clinical research.
Professor Yoon Sup Choi discusses the future of digital healthcare and insurance. He argues that the rapid development and widespread impact of medical artificial intelligence is challenging for traditionally specialized medical professionals to understand. However, this book provides a good guide by clearly explaining concepts of medical AI, its applications, and relationships with doctors. The book also analyzes perspectives on various medical AI developments, uses, and possibilities in a balanced manner.
The document discusses the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It also discusses artificial intelligence in medicine and how AI is revolutionizing the traditionally conservative medical system. However, the fast development and wide influence of medical AI is difficult for modern medical experts to understand. The document provides case studies and insights into the current state and future of medical AI.
The document discusses the post-COVID-19 era and the partnership between the pharmaceutical industry and digital healthcare. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea who has published numerous papers and books on medical AI. He founded the first research institute on digital healthcare in Korea and co-founded a startup accelerator for healthcare companies. The COVID-19 pandemic has accelerated changes in healthcare and increased investment in digital health.
This document discusses digital healthcare and artificial intelligence in medicine. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea. It details his background and accomplishments, including establishing the first research institute for digital healthcare in Korea. It also discusses his investments and advisory work with several healthcare startups. The document promotes Dr. Choi's book on medical artificial intelligence and its potential to transform the conservative medical system.
The document summarizes the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It discusses how artificial intelligence is reshaping the conservative medical system and how quickly AI is developing and influencing healthcare. The convergence of information technology, biotechnology, and medicine is creating innovation that will transform medical education and clinical practice.
When digital medicine becomes the medicine (1/2)Yoon Sup Choi
This document introduces the journal npj Digital Medicine, which focuses on digital medicine. Digital medicine uses digital tools like biosensors, algorithms, and artificial intelligence to upgrade healthcare by individualizing it and making it data-driven. It has the potential to democratize healthcare by empowering individuals. The journal aims to foster collaboration between the many fields involved in digital medicine like computer science, healthcare, engineering, and more to accelerate progress. It argues digital innovations can help address challenges in current healthcare systems by providing more personalized care at sustainable costs while also improving clinical research.
Professor Yoon Sup Choi discusses the future of digital healthcare and insurance. He argues that the rapid development and widespread impact of medical artificial intelligence is challenging for traditionally specialized medical professionals to understand. However, this book provides a good guide by clearly explaining concepts of medical AI, its applications, and relationships with doctors. The book also analyzes perspectives on various medical AI developments, uses, and possibilities in a balanced manner.
The document discusses the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It also discusses artificial intelligence in medicine and how AI is revolutionizing the traditionally conservative medical system. However, the fast development and wide influence of medical AI is difficult for modern medical experts to understand. The document provides case studies and insights into the current state and future of medical AI.
The document discusses the post-COVID-19 era and the partnership between the pharmaceutical industry and digital healthcare. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea who has published numerous papers and books on medical AI. He founded the first research institute on digital healthcare in Korea and co-founded a startup accelerator for healthcare companies. The COVID-19 pandemic has accelerated changes in healthcare and increased investment in digital health.
1) The document discusses digital healthcare in Korea, focusing on medical artificial intelligence.
2) It introduces Yoon Sup Choi, a leading expert in digital healthcare and medical AI in Korea. He is a professor and director of a digital healthcare institute.
3) The document provides endorsements of Choi's book on medical AI from other doctors and professors, praising its overview of the current state and future of medical AI.
I do not have a definitive view on when artificial general intelligence or superintelligence may be achieved. This is an area of ongoing research and debate among experts.
1. Digital healthcare is coming in the form of an unavoidable tsunami.
2. Digital healthcare can be implemented through the convergence of IT, BT, and medicine to create innovations in the digital healthcare field and social value.
3. There are new waves and challenges of digital healthcare, as well as the path towards the future.
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가 - 최윤섭 (updated 18년 10월)Yoon Sup Choi
Professor Yoon Sup Choi is a leading expert in digital healthcare and artificial intelligence in Korea. He is a professor at Sungkyunkwan University and the director of the Digital Healthcare Institute. The document provides background information on Professor Choi, including his educational background, positions held, and publications. It also contains endorsements from other academics on his book "Medical Artificial Intelligence", which provides a balanced perspective on the current state and future of medical AI, and will serve as a useful guide for medical students and professionals. The book covers topics like the concepts and applications of medical AI, and its relationship with doctors.
- The document introduces Professor Yoon Sup Choi, a leading expert in digital healthcare and medical artificial intelligence in South Korea.
- He is the director of the Digital Healthcare Institute at Sungkyunkwan University and has published over 10 papers in journals like Science.
- He has founded the Digital Healthcare Research Institute, the first in Korea to seriously study digital health, and co-founded Digital Healthcare Partners, Korea's only healthcare startup accelerator.
- His book "Medical Artificial Intelligence" discusses the past, present and future of medical AI, including cases and its application and relationship to doctors.
The document provides an overview of digital healthcare and some of the anticipated legal issues. It was written by Professor Yoon Sup Choi of Sungkyunkwan University, who is also the director of the Digital Healthcare Institute. He has experience investing in startups and advising various digital health companies. The document discusses how artificial intelligence is rapidly transforming the conservative medical system and some of the challenges this poses for medical professionals. It also briefly introduces the author's background and perspectives on digital healthcare innovation.
Professor Yoon Sup Choi summarizes digital therapeutics as an uncharted territory. He discusses how artificial intelligence is revolutionizing the traditionally conservative medical system. Digital therapeutics, also known as digital drugs or digiceuticals, have the potential to either replace or augment traditional pharmaceuticals. Major pharmaceutical companies are increasingly investing in digital health startups developing digital therapeutics. If approved by regulators, digital therapeutics could become a third category of treatment alongside chemicals and proteins.
The document introduces Dr. Yoon-Seop Choi, a leading expert in digital healthcare in Korea. It provides details about his background, career experiences, research focus, and roles founding several digital health startups and investment organizations. The key points are:
1) Dr. Choi is a pioneer in digital healthcare in Korea, introducing the field through research, writing, and lectures.
2) He founded the Digital Healthcare Research Institute and is managing director of Digital Healthcare Partners, Korea's first digital health startup accelerator.
3) Dr. Choi has invested in and advised several prominent Korean digital health startups such as Buoyo, Jitto, and Mobile Doctor.
1) The document discusses the concept of a "digital phenotype", which refers to aspects of a person's interactions with technology that can provide diagnostic or prognostic insights into their health conditions.
2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms.
3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
This document describes a study that used a type of deep learning called a convolutional neural network to create an algorithm for detecting diabetic retinopathy and macular edema in retinal fundus photographs. The algorithm was trained on a dataset of over 128,000 retinal images graded by ophthalmologists. It was then validated on two separate datasets, achieving high sensitivity and specificity for detecting referable diabetic retinopathy. The results demonstrate the potential for deep learning algorithms to accurately analyze medical images and help screen for diabetic eye disease.
When digital medicine becomes the medicine (2/2)Yoon Sup Choi
The study assessed the quality and accuracy of care provided by 8 major commercial telemedicine companies for common acute illnesses. Standardized patients presented 6 conditions and completed 599 virtual visits total. The completeness of histories and physical exams ranged from 51.7-82.4% across companies. Correct diagnoses were provided in 65.4-93.8% of visits. Adherence to clinical guidelines for management decisions was 54.3% overall but varied significantly between conditions and companies, from 34.4-66.1% across companies. Greater variation was seen for viral infections than musculoskeletal conditions. This suggests the quality of telemedicine can differ substantially between providers.
The document discusses the relationship between digital therapeutics and telemedicine. It notes that while digital therapeutics and telemedicine are not the same, telemedicine can play a role in clinical trials, prescriptions, and monitoring of patients using digital therapeutics. The COVID-19 pandemic has greatly accelerated the adoption of virtual clinical trials which rely on remote monitoring and delivery of investigational drugs to patients. Regulators have also relaxed some rules to help speed up trials of potential COVID-19 treatments. This shift towards remote and decentralized trials is expected to continue even after the pandemic.
Professor Yoon Sup Choi discusses digital health and the future of healthcare centered around changes in the pharmaceutical industry. He notes that three key steps in implementing digital medicine are: 1) measuring data through devices like smartphones, wearables, and genetic analysis; 2) collecting the data; and 3) gaining insights from the data using artificial intelligence. Choi also provides an overview of the digital health industry landscape and increasing investment in digital health startups from pharmaceutical companies and other investors.
The document introduces Yoon Sup Choi, a leading expert in digital healthcare and artificial intelligence in medicine in Korea. It provides details about his educational and professional background, including having studied computer science and bioengineering, working at various research institutions, and publishing over 10 papers in prestigious journals. It describes his role as the founder and director of several organizations focusing on digital healthcare innovation and startup investment. Choi has invested in and advised several healthcare startups in Korea. He aims to create healthcare innovations and advance the field of digital healthcare through his research, writing, speaking and support of startups.
- The traditional business model of personal genomics companies sees individuals pay to sequence their genomes and receive analysis results, while the companies keep the genomic data and sell it to pharmaceutical companies. However, this model has limitations in addressing high sequencing costs for individuals, lack of individual control over their data, and lack of incentives.
- The proposed Nebula model uses blockchain technology to connect individuals directly with data buyers, eliminating personal genomics companies as middlemen. This is intended to reduce sequencing costs for individuals, give them control over their genomic data and how it is used, and provide incentives.
- The model aims to satisfy both individuals, by addressing the above issues, and data buyers' needs around data availability, acquisition, and
The document discusses the use of Fitbit devices in clinical trials. It notes that while Fitbit is not a medical device, it is widely used in medical research studies. The number of clinical trials using Fitbit has been increasing each year. Fitbit is used in trials both as an intervention to increase patients' activity levels, and to monitor activity levels of research participants. Examples of studies exploring if Fitbit can increase activity in obese children, post-surgery patients, and cancer patients are provided.
'인공지능은 의료를 어떻게 혁신하는가' 주제의 2017년 11월 버전입니다.
'How Artificial Intelligence would Innovate the medicine of the future'
최윤섭 소장 (최윤섭 디지털 헬스케어 연구소)
Yoon Sup Choi, PhD (Director/Founder, Digital Healthcare Institute)
yoonsup.choi@gmail.com
This document summarizes discussions from the Health 2.0 2017 Annual Conference. Speakers discussed trends in digital health investing, with topics including wearables, VR, artificial intelligence, and FDA pre-certification. They noted the hype cycle for emerging technologies and said digital health will likely follow suit, with initial excitement preceding longer-than-expected adoption timelines. However, digital health was said to eventually become a larger field than anticipated. The summary emphasizes this is a critical time for increasing healthcare system value and anticipates more M&A and investment activity.
Digital Healthcare Partners is a digital health accelerator in Korea that discovers, cultivates, invests in, and connects digital health startups. It provides mentoring, business development support, clinical validation, and investment to early-stage startups. Recent deals include a seed investment in 3billion, a company developing genetic diagnosis services for rare diseases using genome analysis. Global trends in digital health funding in Q1 2017 included large deals in areas like population health, EHR, and e-commerce. The largest deal was Grail's $900M series B for its liquid biopsy cancer diagnostic technology.
Real world data is no longer just for those trained in health economics and outcomes research — it can and will touch everyone in the pharma/healthcare space.
CBI asked industry's foremost RWD thought leaders a variety of questions to better understand how bio/pharmaceutical teams can collaborate and capture data in an aggregated form to continue to improve the value of products in development with real world, real-time data.
Real World Data - The New Currency in HealthcareJohn Reites
White paper published in June 2015 by CBI Life Sciences with interview insights from John Reites.
Real World Data (RWD) have become the bio/pharmaceutical industry’s treasure trove for information to inspire stakeholder decision-making. As an industry, professionals have increasingly been looking to RWD to not only assess the bene ts and risks of new medicines in clinical and real world settings, but also as a way to advise healthcare reimbursement decisions worldwide.
1) The document discusses digital healthcare in Korea, focusing on medical artificial intelligence.
2) It introduces Yoon Sup Choi, a leading expert in digital healthcare and medical AI in Korea. He is a professor and director of a digital healthcare institute.
3) The document provides endorsements of Choi's book on medical AI from other doctors and professors, praising its overview of the current state and future of medical AI.
I do not have a definitive view on when artificial general intelligence or superintelligence may be achieved. This is an area of ongoing research and debate among experts.
1. Digital healthcare is coming in the form of an unavoidable tsunami.
2. Digital healthcare can be implemented through the convergence of IT, BT, and medicine to create innovations in the digital healthcare field and social value.
3. There are new waves and challenges of digital healthcare, as well as the path towards the future.
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가 - 최윤섭 (updated 18년 10월)Yoon Sup Choi
Professor Yoon Sup Choi is a leading expert in digital healthcare and artificial intelligence in Korea. He is a professor at Sungkyunkwan University and the director of the Digital Healthcare Institute. The document provides background information on Professor Choi, including his educational background, positions held, and publications. It also contains endorsements from other academics on his book "Medical Artificial Intelligence", which provides a balanced perspective on the current state and future of medical AI, and will serve as a useful guide for medical students and professionals. The book covers topics like the concepts and applications of medical AI, and its relationship with doctors.
- The document introduces Professor Yoon Sup Choi, a leading expert in digital healthcare and medical artificial intelligence in South Korea.
- He is the director of the Digital Healthcare Institute at Sungkyunkwan University and has published over 10 papers in journals like Science.
- He has founded the Digital Healthcare Research Institute, the first in Korea to seriously study digital health, and co-founded Digital Healthcare Partners, Korea's only healthcare startup accelerator.
- His book "Medical Artificial Intelligence" discusses the past, present and future of medical AI, including cases and its application and relationship to doctors.
The document provides an overview of digital healthcare and some of the anticipated legal issues. It was written by Professor Yoon Sup Choi of Sungkyunkwan University, who is also the director of the Digital Healthcare Institute. He has experience investing in startups and advising various digital health companies. The document discusses how artificial intelligence is rapidly transforming the conservative medical system and some of the challenges this poses for medical professionals. It also briefly introduces the author's background and perspectives on digital healthcare innovation.
Professor Yoon Sup Choi summarizes digital therapeutics as an uncharted territory. He discusses how artificial intelligence is revolutionizing the traditionally conservative medical system. Digital therapeutics, also known as digital drugs or digiceuticals, have the potential to either replace or augment traditional pharmaceuticals. Major pharmaceutical companies are increasingly investing in digital health startups developing digital therapeutics. If approved by regulators, digital therapeutics could become a third category of treatment alongside chemicals and proteins.
The document introduces Dr. Yoon-Seop Choi, a leading expert in digital healthcare in Korea. It provides details about his background, career experiences, research focus, and roles founding several digital health startups and investment organizations. The key points are:
1) Dr. Choi is a pioneer in digital healthcare in Korea, introducing the field through research, writing, and lectures.
2) He founded the Digital Healthcare Research Institute and is managing director of Digital Healthcare Partners, Korea's first digital health startup accelerator.
3) Dr. Choi has invested in and advised several prominent Korean digital health startups such as Buoyo, Jitto, and Mobile Doctor.
1) The document discusses the concept of a "digital phenotype", which refers to aspects of a person's interactions with technology that can provide diagnostic or prognostic insights into their health conditions.
2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms.
3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
This document describes a study that used a type of deep learning called a convolutional neural network to create an algorithm for detecting diabetic retinopathy and macular edema in retinal fundus photographs. The algorithm was trained on a dataset of over 128,000 retinal images graded by ophthalmologists. It was then validated on two separate datasets, achieving high sensitivity and specificity for detecting referable diabetic retinopathy. The results demonstrate the potential for deep learning algorithms to accurately analyze medical images and help screen for diabetic eye disease.
When digital medicine becomes the medicine (2/2)Yoon Sup Choi
The study assessed the quality and accuracy of care provided by 8 major commercial telemedicine companies for common acute illnesses. Standardized patients presented 6 conditions and completed 599 virtual visits total. The completeness of histories and physical exams ranged from 51.7-82.4% across companies. Correct diagnoses were provided in 65.4-93.8% of visits. Adherence to clinical guidelines for management decisions was 54.3% overall but varied significantly between conditions and companies, from 34.4-66.1% across companies. Greater variation was seen for viral infections than musculoskeletal conditions. This suggests the quality of telemedicine can differ substantially between providers.
The document discusses the relationship between digital therapeutics and telemedicine. It notes that while digital therapeutics and telemedicine are not the same, telemedicine can play a role in clinical trials, prescriptions, and monitoring of patients using digital therapeutics. The COVID-19 pandemic has greatly accelerated the adoption of virtual clinical trials which rely on remote monitoring and delivery of investigational drugs to patients. Regulators have also relaxed some rules to help speed up trials of potential COVID-19 treatments. This shift towards remote and decentralized trials is expected to continue even after the pandemic.
Professor Yoon Sup Choi discusses digital health and the future of healthcare centered around changes in the pharmaceutical industry. He notes that three key steps in implementing digital medicine are: 1) measuring data through devices like smartphones, wearables, and genetic analysis; 2) collecting the data; and 3) gaining insights from the data using artificial intelligence. Choi also provides an overview of the digital health industry landscape and increasing investment in digital health startups from pharmaceutical companies and other investors.
The document introduces Yoon Sup Choi, a leading expert in digital healthcare and artificial intelligence in medicine in Korea. It provides details about his educational and professional background, including having studied computer science and bioengineering, working at various research institutions, and publishing over 10 papers in prestigious journals. It describes his role as the founder and director of several organizations focusing on digital healthcare innovation and startup investment. Choi has invested in and advised several healthcare startups in Korea. He aims to create healthcare innovations and advance the field of digital healthcare through his research, writing, speaking and support of startups.
- The traditional business model of personal genomics companies sees individuals pay to sequence their genomes and receive analysis results, while the companies keep the genomic data and sell it to pharmaceutical companies. However, this model has limitations in addressing high sequencing costs for individuals, lack of individual control over their data, and lack of incentives.
- The proposed Nebula model uses blockchain technology to connect individuals directly with data buyers, eliminating personal genomics companies as middlemen. This is intended to reduce sequencing costs for individuals, give them control over their genomic data and how it is used, and provide incentives.
- The model aims to satisfy both individuals, by addressing the above issues, and data buyers' needs around data availability, acquisition, and
The document discusses the use of Fitbit devices in clinical trials. It notes that while Fitbit is not a medical device, it is widely used in medical research studies. The number of clinical trials using Fitbit has been increasing each year. Fitbit is used in trials both as an intervention to increase patients' activity levels, and to monitor activity levels of research participants. Examples of studies exploring if Fitbit can increase activity in obese children, post-surgery patients, and cancer patients are provided.
'인공지능은 의료를 어떻게 혁신하는가' 주제의 2017년 11월 버전입니다.
'How Artificial Intelligence would Innovate the medicine of the future'
최윤섭 소장 (최윤섭 디지털 헬스케어 연구소)
Yoon Sup Choi, PhD (Director/Founder, Digital Healthcare Institute)
yoonsup.choi@gmail.com
This document summarizes discussions from the Health 2.0 2017 Annual Conference. Speakers discussed trends in digital health investing, with topics including wearables, VR, artificial intelligence, and FDA pre-certification. They noted the hype cycle for emerging technologies and said digital health will likely follow suit, with initial excitement preceding longer-than-expected adoption timelines. However, digital health was said to eventually become a larger field than anticipated. The summary emphasizes this is a critical time for increasing healthcare system value and anticipates more M&A and investment activity.
Digital Healthcare Partners is a digital health accelerator in Korea that discovers, cultivates, invests in, and connects digital health startups. It provides mentoring, business development support, clinical validation, and investment to early-stage startups. Recent deals include a seed investment in 3billion, a company developing genetic diagnosis services for rare diseases using genome analysis. Global trends in digital health funding in Q1 2017 included large deals in areas like population health, EHR, and e-commerce. The largest deal was Grail's $900M series B for its liquid biopsy cancer diagnostic technology.
Real world data is no longer just for those trained in health economics and outcomes research — it can and will touch everyone in the pharma/healthcare space.
CBI asked industry's foremost RWD thought leaders a variety of questions to better understand how bio/pharmaceutical teams can collaborate and capture data in an aggregated form to continue to improve the value of products in development with real world, real-time data.
Real World Data - The New Currency in HealthcareJohn Reites
White paper published in June 2015 by CBI Life Sciences with interview insights from John Reites.
Real World Data (RWD) have become the bio/pharmaceutical industry’s treasure trove for information to inspire stakeholder decision-making. As an industry, professionals have increasingly been looking to RWD to not only assess the bene ts and risks of new medicines in clinical and real world settings, but also as a way to advise healthcare reimbursement decisions worldwide.
The Digital Metamorphosis of the Pharma IndustryLen Starnes
The document discusses the digital transformation of the pharmaceutical industry. It notes changes like aging populations, rising healthcare costs, empowered patients, and new business models. Doctors are becoming "digital" and using social networks and mobile devices. Patients are forming online communities to share health data. The document suggests pharmaceutical companies must adapt by using digital tools, empowering sales forces with mobile technology, and building trust with doctors, patients, and online communities. Pharma must learn from the digital behaviors of doctors and patients to keep pace with their evolving expectations.
Is Technology Removing the ‘Care’ from Healthcare?MSL
The document discusses a roundtable report on the implications of the Topol Review for the pharmaceutical industry. The Topol Review explored how to support digital healthcare technologies in the NHS. The roundtable addressed questions about ensuring personal care in a digital environment, bridging digital divides, and benefits of technologies like genomics and AI. Key points discussed included the need for patients to be partners, addressing skills and access issues, and the industry needing to adapt to remain relevant in a changing healthcare system focused on personalized care.
The 10 most trusted diagnostics and pathology center.Merry D'souza
Inquisitiveness is a primary trait of human beings. The human brain which processes millions of thoughts in a flash of time needs to know about everything and anything around it. Human well being or fitness is one such area that has grown by leaps and bound over centuries given to the human quest to stay healthy. Specific to it is the study of diseases, their cause and effect which comes under the broad spectrum of Pathology.
The document summarizes a panel discussion on digital health held by the INSEAD Healthcare Club of Switzerland. It discusses how digital health has the potential to transform life sciences through technologies like sensors, data collection, and precision medicine. However, significant regulatory hurdles around data sharing and privacy still exist. While companies like Bristol-Myers Squibb and Novartis are pursuing digital health projects, it is unclear which players like pharmaceutical companies, technology giants, insurers, or patients will ultimately lead the transformation. The panelists debated these issues and shared lessons learned from their experiences in digital health.
Using technology-enabled social prescriptions to disrupt healthcareDr Sven Jungmann
As chronic diseases are increasingly straining healthcare systems, social factors are gaining importance. Since the birth of social medicine (19th century), we saw many failed attempts to beat the dominance of the biomedical model. Social prescriptions have come, raising hopes that non-biomedical solutions will improve outcomes and optimise resource use. Social Prescriptions connect citizens to support to address social determinants of health and encourage self-care for physical and mental health. Social prescriptions can make us healthier cheaper and with fewer side effects than most drugs. Social prescriptions can become a disruptive force as they can be personalised, improve lifestyle-related diseases, and support non-biomedical issues affected by social determinants of health.
In this report we set out ten provocative statements predicting the world of 2020. Each prediction is articulated and brought to life through a series of portraits which imagine how patients, healthcare professionals and life sciences organizations might behave in this new world. Our predictions lean more towards an optimistic view of the future, although we organized that many in our industry are organized about the constraints and therefore pace of change. We describe the big trends rolled forward to 2020 and some of the constraints that will need to be overcome.
We also provide examples and evidence, based on the here and now, that show that the predictions are perfectly plausible, perhaps inspiring and surprising!
Our industry is changing quickly – requiring a bold response that is often difficult to implement – and yet organizations struggle to understand how to respond effectively and build a sense of urgency. We hope this report creates rich dialogue and enables a move to action.– we have had enormous fun discussing these predictions and sharing our experiences. We hope you have the same experience within your own organizations as you peruse this report and reflect on your current situation and future scenarios.
20 tendencias digitales en salud digital_ The Medical FuturistRichard Canabate
Resaltado de las tendencias que darán forma a la atención médica post COVID19.
No se trata de enumerar estas tendencias, sino de dar una valiosa visión de los factores de conducción detrás de ellas mientras que es muy específico. Se trata de mostrar cuáles son las áreas exactas que deben destacarse entre todas las áreas en el tema "IA en la atención médica", por ejemplo.
The coming era of digital therapeutics nrc live - sept 2018Chris Hogg
Thoughts on the past, present and future of digital therapeutics and digital medicines. Presentation for NRC Live Zorgtech Conference in Amsterdam, September 2018.
The Razorfish Healthware Report from Doctors 2.0 & You Conference 2014, including the section "Digital advance in the patient journey", with my views about omnichannel marketing in healthcare
The document summarizes the Doctors 2.0 & You Conference 2014 in Paris which discussed how technologies, web 2.0 tools, apps, and social media are changing relationships in healthcare. Startups pitched innovative digital solutions, including platforms for second medical opinions, sharing medical images, and personalized health management. Presenters discussed how digital is both disrupting and empowering medicine by facilitating connected communities, data sharing for research, and patient-centered care through tools like telemedicine and online education. Social media was highlighted as an important tool for participatory medicine by stimulating earlier collaboration and research dissemination.
The ten predictions for 2020
1. Health consumers in 2020
Informed and demanding patients are now partners in their own healthcare
2. Health care delivery systems in 2020
The era of digitised medicine - new business models drive new ideas
3. Wearables and mHealth applications in 2020
Measuring quality of life not just clinical indicators
4. Big Data in 2020
Health data is pervasive – requiring new tools and provider models
5. Regulation in 2020
Regulations reflect the convergence of technology and science
6. Research and Development in 2020
The networked laboratory - partnerships and big data amidst new scrutiny
7. The pharmaceutical commercial model in 2020
Local is important but with a shift from volume to value
8. The pharmaceutical enterprise configuration - the back office in 2020
Single, global and responsible for insight enablement
9. New business models in emerging markets in 2020
Still emerging, but full of creativity for the world
10. Impact of behaviours on corporate reputation in 2020
A new dawn of trust
Digital Transformation In Healthcare_ Trends, Challenges And Solutions.pdfLucas Lagone
Explore digital transformation in Healthcare, Trends, face challenges, and discover effective solutions for a seamless transition in the healthcare industry.
The document discusses how life sciences companies can deliver value beyond traditional medications by leveraging digital technologies. It recommends that life sciences IT organizations 1) drive digital innovation through rapid projects, 2) enable on-demand digital services, and 3) provide personalized apps and digital assets. To achieve this, the document outlines several strategic themes and IT capabilities needed, including establishing fluid and hybrid IT operations to support two-speed organizations and abstracting systems management. The goal is to transform IT value chains into fully digital services management to promote new digital offerings for patients.
The document discusses the opportunities and challenges of social media marketing in the pharmaceutical industry. It notes that while social networking can help interact with healthcare professionals, consumers, and for knowledge sharing, pharmaceutical companies tend to have more risk-averse cultures that value control over information. Effective social media engagement may require companies to adopt more open and collaborative approaches.
Digitalisation Of Healthcare - Towards A Better Future - Free Download E bookkevin brown
Digital health has been around for quite some
time. Advancements in technology, rising
demand for better care, and governments' focus
on improved health economy have contributed
to the digital transformation in the healthcare
sector. Healthcare providers and professionals
are continuously challenged to come up with
innovative and cost-effective ways of providing
effective care and better patient outcomes.
In the past few years, digital technologies
have changed the healthcare landscape into
becoming more patient-centric, with care givers
focusing on engaging patients and improving
their experiences.
According a Deloitte report, global healthcare
spending is estimated to cross US$10 trillion by
2022. As the global healthcare market embraces
digitalisation, innovation has a major role to
play. Healthcare companies have been investing
heavily in digital technologies to drive innovation
and value-based care, while making care giving
more accessible and efficient. Digitalisation results
in better usage of patient data by care givers
enabling them to offer personalised healthcare
to the patients.
Pharmaceutical companies are increasingly recognizing the value of real-world evidence and digital health technologies. Real-world data from electronic health records, wearable devices, and other sources can provide insights into drug effectiveness outside of controlled clinical trials. This data has the potential to transform drug development and delivery of personalized healthcare. It allows evaluation of treatments using broader and longer-term patient data. Pharma is exploring applications of real-world evidence such as improving clinical trial design and identifying new drug targets and uses based on unanticipated real-world findings. Widespread collection and use of real-world data may help address industry challenges like rising development costs and ensuring drug safety.
In 2022, individuals are more informed about their health through genetic testing and digital technologies. They are engaged in managing their own health through wearable devices and health apps. The quantified self has embraced prevention and consumers demand specific treatments, sharing health data willingly with providers. New entrants are disrupting care delivery through virtual clinics and gamification programs supported by insurers.
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
This document discusses the use of deep learning to improve the diagnosis of breast cancer from pathology images. It describes a study where a deep learning model was trained on a large dataset of pathology slides to detect regions of breast cancer metastases. The model was able to detect cancer metastases with an accuracy of over 99%, significantly outperforming pathologists. It also reduced the time needed for analysis from hours to minutes. This demonstrates the potential for deep learning to help pathologists more accurately and efficiently diagnose cancer from digital pathology images.
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Presentation by Herman Kienhuis (Curiosity VC) on Investing in AI for ABS Alu...Herman Kienhuis
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This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
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This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
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These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
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6. 27
Switzerland
EUROPE
$3.2B
$1.96B $1B
$3.5B
NORTH AMERICA
$12B Valuation
$1.8B
$3.1B$3.2B
$1B
$1B
38 healthcare unicorns valued at $90.7B
Global VC-backed digital health companies with a private market valuation of $1B+ (7/26/19)
UNITED KINGDOM
$1.5B
MIDDLE EAST
$1B Valuation
ISRAEL
$7B
$1B$1.2B
$1B
$1.65B
$1.8B
$1.25B
$2.8B
$1B $1B
$2B Valuation
$1.5B
UNITED STATES
GERMANY
$1.7B
$2.5B
CHINA
ASIA
$3B
$5.5B Valuation
$5B
$2.4B
$2.4B
France
$1.1B $3.5B
$1.6B
$1B
$1B
$1B
$1B
CB Insights, Global Healthcare Reports 2019 2Q
•전 세계적으로 38개의 디지털 헬스케어 유니콘 스타트업 (=기업가치 $1B 이상) 이 있으나,
•국내에는 하나도 없음
7. 헬스케어
넓은 의미의 건강 관리에는 해당되지만,
디지털 기술이 적용되지 않고, 전문 의료 영역도 아닌 것
예) 운동, 영양, 수면
디지털 헬스케어
건강 관리 중에 디지털 기술이 사용되는 것
예) 사물인터넷, 인공지능, 3D 프린터, VR/AR
모바일 헬스케어
디지털 헬스케어 중
모바일 기술이 사용되는 것
예) 스마트폰, 사물인터넷, SNS
개인 유전정보분석
암유전체, 질병위험도,
보인자, 약물 민감도
예) 웰니스, 조상 분석
헬스케어 관련 분야 구성도(ver 0.6)
의료
질병 예방, 치료, 처방, 관리
등 전문 의료 영역
원격의료
원격 환자 모니터링
원격진료
전화, 화상, 판독
디지털 치료제
당뇨 예방 앱
중독 치료 앱
ADHD 치료게임
14. •복잡한 의료 데이터의 분석 및 insight 도출
•영상 의료/병리 데이터의 분석/판독
•연속 데이터의 모니터링 및 예방/예측
의료 인공지능의 세 유형
15. •복잡한 의료 데이터의 분석 및 insight 도출
•영상 의료/병리 데이터의 분석/판독
•연속 데이터의 모니터링 및 예방/예측
의료 인공지능의 세 유형
16.
17. 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
18. 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
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.
•소아 환자 130만 명의 EMR 데이터 101.6 million 개 분석
•딥러닝 기반의 자연어 처리 기술
•의사의 hypothetico-deductive reasoning 모방
•소아 환자의 common disease를 진단하는 인공지능
Nat Med 2019 Feb
19. GP at Hand
•영국 바빌론헬스의 GP at Hand
•챗봇 기반의 질병 진단 + 원격 진료
•영국 NHS 에서 활용 중
22. Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10
features were significantly correlated to the scores:
• strong correlation: circadian movement, normalized entropy, location variance
• correlation: phone usage features, usage duration and usage frequency
23. Mindstrong Health
• 스마트폰 사용 패턴을 바탕으로
• 인지능력, 우울증, 조현병, 양극성 장애, PTSD 등을 측정
• 미국 국립정신건강연구소 소장인 Tomas Insel 이 공동 설립
• 아마존의 제프 베조스 투자
24. BRIEF COMMUNICATION OPEN
Digital biomarkers of cognitive function
Paul Dagum1
To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of
smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several
neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of
digital biomarkers that predicted test scores with high correlations (p 10−4
). These preliminary results suggest that passive
measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment.
npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4
INTRODUCTION
By comparison to the functional metrics available in other
disciplines, conventional measures of neuropsychiatric disorders
have several challenges. First, they are obtrusive, requiring a
subject to break from their normal routine, dedicating time and
often travel. Second, they are not ecological and require subjects
to perform a task outside of the context of everyday behavior.
Third, they are episodic and provide sparse snapshots of a patient
only at the time of the assessment. Lastly, they are poorly scalable,
taxing limited resources including space and trained staff.
In seeking objective and ecological measures of cognition, we
attempted to develop a method to measure memory and
executive function not in the laboratory but in the moment,
day-to-day. We used human–computer interaction on smart-
phones to identify digital biomarkers that were correlated with
neuropsychological performance.
RESULTS
In 2014, 27 participants (ages 27.1 ± 4.4 years, education
14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological
assessment and a test of the smartphone app. Smartphone
human–computer interaction data from the 7 days following
the neuropsychological assessment showed a range of correla-
tions with the cognitive scores. Table 1 shows the correlation
between each neurocognitive test and the cross-validated
predictions of the supervised kernel PCA constructed from
the biomarkers for that test. Figure 1 shows each participant
test score and the digital biomarker prediction for (a) digits
backward, (b) symbol digit modality, (c) animal fluency,
(d) Wechsler Memory Scale-3rd Edition (WMS-III) logical
memory (delayed free recall), (e) brief visuospatial memory test
(delayed free recall), and (f) Wechsler Adult Intelligence Scale-
4th Edition (WAIS-IV) block design. Construct validity of the
predictions was determined using pattern matching that
computed a correlation of 0.87 with p 10−59
between the
covariance matrix of the predictions and the covariance matrix
of the tests.
Table 1. Fourteen neurocognitive assessments covering five cognitive
domains and dexterity were performed by a neuropsychologist.
Shown are the group mean and standard deviation, range of score,
and the correlation between each test and the cross-validated
prediction constructed from the digital biomarkers for that test
Cognitive predictions
Mean (SD) Range R (predicted),
p-value
Working memory
Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4
Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5
Executive function
Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4
Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6
Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4
Language
Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4
FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3
Dexterity
Grooved pegboard test
(dominant hand)
62.7 (6.7) 51–75 0.73 ± 0.09, 10−4
Memory
California verbal learning test
(delayed free recall)
14.1 (1.9) 9–16 0.62 ± 0.12, 10−3
WMS-III logical memory
(delayed free recall)
29.4 (6.2) 18–42 0.81 ± 0.07, 10−6
Brief visuospatial memory test
(delayed free recall)
10.2 (1.8) 5–12 0.77 ± 0.08, 10−5
Intelligence scale
WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6
WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6
WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4
Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018
1
Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA
Correspondence: Paul Dagum (paul@mindstronghealth.com)
www.nature.com/npjdigitalmed
Published in partnership with the Scripps Translational Science Institute
• 총 45가지 스마트폰 사용 패턴: 타이핑, 스크롤, 화면 터치
• 스페이스바 누른 후, 다음 문자 타이핑하는 행동
• 백스페이스를 누른 후, 그 다음 백스페이스
• 주소록에서 사람을 찾는 행동 양식
• 스마트폰 사용 패턴과 인지 능력의 상관 관계
• 20-30대 피험자 27명
• Working Memory, Language, Dexterity etc
25. BRIEF COMMUNICATION OPEN
Digital biomarkers of cognitive function
Paul Dagum1
To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of
smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several
neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of
digital biomarkers that predicted test scores with high correlations (p 10−4
). These preliminary results suggest that passive
measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment.
npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4
INTRODUCTION
By comparison to the functional metrics available in other
disciplines, conventional measures of neuropsychiatric disorders
have several challenges. First, they are obtrusive, requiring a
subject to break from their normal routine, dedicating time and
often travel. Second, they are not ecological and require subjects
to perform a task outside of the context of everyday behavior.
Third, they are episodic and provide sparse snapshots of a patient
only at the time of the assessment. Lastly, they are poorly scalable,
taxing limited resources including space and trained staff.
In seeking objective and ecological measures of cognition, we
attempted to develop a method to measure memory and
executive function not in the laboratory but in the moment,
day-to-day. We used human–computer interaction on smart-
phones to identify digital biomarkers that were correlated with
neuropsychological performance.
RESULTS
In 2014, 27 participants (ages 27.1 ± 4.4 years, education
14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological
assessment and a test of the smartphone app. Smartphone
human–computer interaction data from the 7 days following
the neuropsychological assessment showed a range of correla-
tions with the cognitive scores. Table 1 shows the correlation
between each neurocognitive test and the cross-validated
predictions of the supervised kernel PCA constructed from
the biomarkers for that test. Figure 1 shows each participant
test score and the digital biomarker prediction for (a) digits
backward, (b) symbol digit modality, (c) animal fluency,
(d) Wechsler Memory Scale-3rd Edition (WMS-III) logical
memory (delayed free recall), (e) brief visuospatial memory test
(delayed free recall), and (f) Wechsler Adult Intelligence Scale-
4th Edition (WAIS-IV) block design. Construct validity of the
predictions was determined using pattern matching that
computed a correlation of 0.87 with p 10−59
between the
covariance matrix of the predictions and the covariance matrix
of the tests.
Table 1. Fourteen neurocognitive assessments covering five cognitive
domains and dexterity were performed by a neuropsychologist.
Shown are the group mean and standard deviation, range of score,
and the correlation between each test and the cross-validated
prediction constructed from the digital biomarkers for that test
Cognitive predictions
Mean (SD) Range R (predicted),
p-value
Working memory
Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4
Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5
Executive function
Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4
Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6
Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4
Language
Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4
FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3
Dexterity
Grooved pegboard test
(dominant hand)
62.7 (6.7) 51–75 0.73 ± 0.09, 10−4
Memory
California verbal learning test
(delayed free recall)
14.1 (1.9) 9–16 0.62 ± 0.12, 10−3
WMS-III logical memory
(delayed free recall)
29.4 (6.2) 18–42 0.81 ± 0.07, 10−6
Brief visuospatial memory test
(delayed free recall)
10.2 (1.8) 5–12 0.77 ± 0.08, 10−5
Intelligence scale
WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6
WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6
WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4
Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018
1
Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA
Correspondence: Paul Dagum (paul@mindstronghealth.com)
www.nature.com/npjdigitalmed
Published in partnership with the Scripps Translational Science Institute
Fig. 1 A blue square represents a participant test Z-score normed to the 27 participant scores and a red circle represents the digital biomarker
prediction Z-score normed to the 27 predictions. Test scores and predictions shown are a digits backward, b symbol digit modality, c animal
fluency, d Wechsler memory Scale-3rd Edition (WMS-III) logical memory (delayed free recall), e brief visuospatial memory test (delayed free
recall), and f Wechsler adult intelligence scale-4th Edition (WAIS-IV) block design
Digital biomarkers of cognitive function
P Dagum
2
1234567890():,;
• 스마트폰 사용 패턴과 인지 능력의 높은 상관 관계
• 파란색: 표준 인지 능력 테스트 결과
• 붉은색: 마인드 스트롱의 스마트폰 사용 패턴
26. •복잡한 의료 데이터의 분석 및 insight 도출
•영상 의료/병리 데이터의 분석/판독
•연속 데이터의 모니터링 및 예방/예측
의료 인공지능의 세 유형
27.
28. NATURE MEDICINE
and the algorithm led to the best accuracy, and the algorithm mark-
edly sped up the review of slides35
. This study is particularly notable,
41
Table 2 | FDA AI approvals are accelerating
Company FDA Approval Indication
Apple September 2018 Atrial fibrillation detection
Aidoc August 2018 CT brain bleed diagnosis
iCAD August 2018 Breast density via
mammography
Zebra Medical July 2018 Coronary calcium scoring
Bay Labs June 2018 Echocardiogram EF
determination
Neural Analytics May 2018 Device for paramedic stroke
diagnosis
IDx April 2018 Diabetic retinopathy diagnosis
Icometrix April 2018 MRI brain interpretation
Imagen March 2018 X-ray wrist fracture diagnosis
Viz.ai February 2018 CT stroke diagnosis
Arterys February 2018 Liver and lung cancer (MRI, CT)
diagnosis
MaxQ-AI January 2018 CT brain bleed diagnosis
Alivecor November 2017 Atrial fibrillation detection via
Apple Watch
Arterys January 2017 MRI heart interpretation
NATURE MEDICINE
인공지능 기반 의료기기
FDA 인허가 현황
Nature Medicine 2019
• Zebra Medical Vision
• 2019년 5월: 흉부 엑스레이에서 기흉 판독
• 2019년 6월: head CT 에서 뇌출혈 판독
• Aidoc
• 2019년 5월: CT에서 폐색전증 판독
• 2019년 6월: CT에서 경추골절 판독
+
34. •Some polyps were detected with only partial appearance.
•detected in both normal and insufficient light condition.
•detected under both qualified and suboptimal bowel preparations.
ARTICLESNATURE BIOMEDICAL ENGINEERING
from patients who underwent colonoscopy examinations up to 2
years later.
Also, we demonstrated high per-image-sensitivity (94.38%
and 91.64%) in both the image (datasetA) and video (datasetC)
analyses. DatasetsA and C included large variations of polyp mor-
phology and image quality (Fig. 3, Supplementary Figs. 2–5 and
Supplementary Videos 3 and 4). For images with only flat and iso-
datasets are often small and do not represent the full range of colon
conditions encountered in the clinical setting, and there are often
discrepancies in the reporting of clinical metrics of success such as
sensitivity and specificity19,20,26
. Compared with other metrics such
as precision, we believe that sensitivity and specificity are the most
appropriate metrics for the evaluation of algorithm performance
because of their independence on the ratio of positive to negative
Fig. 3 | Examples of polyp detection for datasetsA and C. Polyps of different morphology, including flat isochromatic polyps (left), dome-shaped polyps
(second from left, middle), pedunculated polyps (second from right) and sessile serrated adenomatous polyps (right), were detected by the algorithm
(as indicated by the green tags in the bottom set of images) in both normal and insufficient light conditions, under both qualified and suboptimal bowel
preparations. Some polyps were detected with only partial appearance (middle, second from right). See Supplementary Figs 2–6 for additional examples.
flat isochromatic polyps dome-shaped polyps sessile serrated adenomatous polypspedunculated polyps
대장내시경에서의 용종 발견 보조 인공지능