Digital Health & Artificial Intelligence 2020: Trends, Opportunities, and Outlook – IDTechEx.com

Original article can be found here (source): artificial intelligence

1. EXECUTIVE SUMMARY & CONCLUSIONS
1.1. The Scope of Digital Health
1.2. Changing Demographics Require Healthcare Reforms
1.3. Factors Encouraging the Rise of Digital Health
1.4. Enabling Technologies for Digital Health
1.5. Effective Use of Resources Enabling Cost Efficiency
1.6. The Future of Healthcare is Consumer-Led
1.7. Big Tech Players are Moving into Healthcare
1.8. Towards a Model of Value-Based Healthcare
1.9. Big Pharma is Struggling, Disruption is Inevitable
1.10. Consolidating and Collaborating to Survive and Thrive
1.11. Regulation of Digital Health: Wanting the Best of Both Worlds
1.12. Telehealth and Telemedicine are Poised for Take-Off
1.13. The Future for Telehealth and Telemedicine
1.14. Remote Patient Monitoring is Changing the Face of Healthcare
1.15. The Outlook for Remote Patient Monitoring
1.16. Digital Therapeutics – The Next Step for mHealth?
1.17. The Outlook for Digital Therapeutics
1.18. The Rise of Direct-to-Consumer Genetic Testing
1.19. Sensors in Smart Homes: Decentralization of Healthcare
1.20. Funding and IPOs
1.21. Market Outlook: Wearable Medical Devices
1.22. Outlook for Digital Health: Quality, Outcomes, and Value are Key
2. INTRODUCTION
2.1. The Scope of Digital Health
2.2. Digital Health Definitions
2.3. Changing Demographics Require Healthcare Reforms
2.4. Global Healthcare Spending is Rising
2.5. Margins are Being Squeezed in Healthcare
2.6. Trouble for Traditional Healthcare?
2.7. Mergers and Acquisitions: Vertical Integration
2.8. Big Tech Players are Moving into Healthcare
2.9. Apple – 2018 Update
2.10. Apple – 2019 Update
2.11. Amazon
2.12. Amazon – Alexa
2.13. Alphabet
2.14. Microsoft
2.15. Tencent
2.16. From Fee-for-Service to Value-Based Purchasing
2.17. Towards a Model of Value-Based Healthcare
2.18. Big Pharma is Struggling
2.19. Digital Disruptors & Big Pharma: A Match Made in Heaven?
2.20. Big Pharma: Competitions and Digital Hubs
2.21. The Future for Pharma
2.22. Rising Role of Venture Capital
2.23. Funding and IPOs
2.24. Mobile Health is Becoming the Norm
2.25. Apple Enters the Electronic Health Record Market
2.26. The Future of Healthcare is Consumer-Led
2.27. Apps are Moving Towards Voice
2.28. A Move to Precision/Personalized Medicine
2.29. Biosensors are Moving to the Point-of-Care
2.30. Consumer-Driven, Patient Centered Healthcare
2.31. Global Challenges in Implementing Digital Health
2.32. The P4 Healthcare Model
2.33. The Emergence of a P4 Healthcare System
2.34. Wellness and Prevention
2.35. Market Outlook: Wearable Medical Devices
3. ENABLING TECHNOLOGIES
3.1. Enabling Technologies for Digital Health
3.2. IoT
3.3. 5G
3.4. Access to High Quality Broadband
3.5. Artificial Intelligence and Machine Learning
3.6. VR, AR and MR
4. REGULATIONS AND SECURITY
4.1. Digital vs Traditional Healthcare
4.2. Medical Device Pathways
4.3. Regulation of Digital Therapeutics
4.4. FDA Pre-Cert Program
4.5. Pre-Cert 1.0
4.6. Digital Tools Not Under FDA Review
4.7. Regulation of Direct-to-Consumer Genetic Testing
4.8. Genetic Data, Privacy Concerns and a Lack of Trust
4.9. Unanswered questions about device security
4.10. Security Risks for Medical Devices
4.11. The Security of Data is a Critical Issue
4.12. National Systems at Risk of Large-Scale Cyber Attacks
5. TELEHEALTH & TELEMEDICINE
5.1. Defining Telehealth and Telemedicine
5.2. Telehealth Encompasses a Range of Services
5.3. There are Numerous Types of Telemedicine
5.4. The Key Services Models for Telehealth
5.5. Use Cases for Telehealth and Telemedicine
5.6. Benefits of Telehealth and Telemedicine
5.7. Challenges in Telehealth and Telemedicine
5.8. Is Telehealth a Cost-Effective Solution?
5.9. Telehealth and Telemedicine are Poised for Take-Off
5.10. The Growing Network of Care
5.11. Doctors Require Better Ways of Communication
5.12. Nomadeec
5.13. Smartphones Become the Tool for Doctors
5.14. Driving the Uptake of Telemedicine
5.15. Changes to Reimbursement of Telehealth
5.16. Reimbursement of Remote Patient Monitoring
5.17. Room to Improve and Mature
5.18. The Next-Generation of Telemedicine
5.19. Can AI Replace Your Doctor?
5.20. Babylon Health
5.21. TytoCare
5.22. American Well
5.23. Key American Well Partnerships
5.24. The Future for Telehealth and Telemedicine
5.25. The Future for Telehealth and Telemedicine (cont.)
6. REMOTE PATIENT MONITORING
6.1. Remote Patient Monitoring: Measurements and Applications
6.2. Components of a Remote Monitoring System
6.3. Remote Patient Monitoring is Changing the Face of Healthcare
6.4. Remote Patient Monitoring in Hospitals and the Home
6.5. Remote Patient Monitoring Solutions in the Home
6.6. Omron
6.7. Owlet
6.8. toSense
6.9. Biotricity
6.10. Sony
6.11. Remote Patient Monitoring Solutions in Hospitals
6.12. Evolution of the Stethoscope into the Digital Realm
6.13. Digital Stethoscopes Enable Decentralized Healthcare
6.14. The Benefits of Remote Patient Monitoring for Payers
6.15. UnitedHealthcare Motion: US
6.16. Momentum Multiple: South Africa
6.17. Vitality: UK
6.18. Vitality and Apple Watch
6.19. Fitbit
6.20. Elder Care
6.21. Elder Care: Fall Detection
6.22. Elder Care: Medical Adherence
6.23. Is Remote Patient Monitoring Really Helpful?
6.24. Skin Patches Emerging as a Key Form Factor
6.25. Contactless/Invisible RPM
6.26. Xandar Kardian
6.27. The Outlook for Remote Patient Monitoring
7. DIGITAL THERAPEUTICS
7.1. Digital Therapeutics: App-Based Healthcare
7.2. Digital Therapeutics – The Next Step for mHealth?
7.3. The Rationale Behind Digital Therapeutics (DTx)
7.4. Digital Therapeutics for Chronic Conditions Poised for Success
7.5. Tracking and Monitoring Adherence
7.6. Proteus Digital Health
7.7. Propeller Health
7.8. Mental Health is a Key Focus for DTx
7.9. Pear Therapeutics
7.10. Carrot
7.11. Akili Interactive
7.12. Insurers are Investing in Digital Therapeutics
7.13. Difficulties in Realising the Potential of Digital Therapeutics
7.14. Digital Therapeutics Alliance
7.15. Diabetes Partnerships are Proliferating
7.16. The Outlook for Digital Therapeutics
8. CASE STUDY: DIABETES MANAGEMENT
8.1. Diabetes is an Early Adopter of Digital Healthcare Initiatives
8.2. The cost of diabetes
8.3. Managing side effects accounts for 90% of the total cost of diabetes
8.4. Diabetes management device roadmap: Summary
8.5. Strategy comparison amongst the largest players
8.6. New directions with glucometers: Connectivity
8.7. The case for CGM
8.8. CGM: Overview of key players
8.9. Skin patches are the form factor of choice
8.10. Smarter insulin delivery informing decisions
8.11. Smart Pen Platform Preventing Missed Doses
8.12. Smart insulin delivery device manufacturers
8.13. Diabetes apps
8.14. Growing ecosystem via acquisitions and partnerships
8.15. Roche & mySugr
8.16. Lilly & Rimidi, Lilly & Livongo
8.17. Blue Mesa Health & Merck
8.18. Glooko-Novo Nordisk in Diabetes Care
8.19. Other case studies: Digital diabetes management
8.20. BlueStar
8.21. Voluntis
8.22. DIABNEXT
8.23. Better Therapeutics
9. CONSUMER GENETIC TESTING
9.1. The Central Dogma: DNA, RNA and Proteins
9.2. What is Direct-to-Consumer Genetic Testing?
9.3. Genetic Variations: What Are We Testing For?
9.4. The Rise of Direct-to-Consumer Genetic Testing
9.5. Monetizing Genomic Testing
9.6. The Emergence of Genomics Analysis Companies
9.7. Not All Data is Created Equal
9.8. AI Driven Genomics and Drug Development
9.9. Alexa-Powered AI Genomics Platform
9.10. Using Genomics to Diagnose NHS Patients
9.11. AncestryDNA
9.12. 23andme
9.13. Foundation Medicine
9.14. Atlas Biomed Group
9.15. Orig3n
10. SMART HOME AS A CARER
10.1. Smart Home as a Carer Becomes Increasingly Important with Ageing Populations
10.2. Sensors in Smart Homes: Decentralization of Healthcare
10.3. Bringing Healthcare into the Home by Fitting Sensors
10.4. Medical Asset Tracking Allows for More Productive Refilling of Medicines and Vital Equipment in the Home
10.5. Philips Smart Home as a Carer Ecosystem is Able to Alert Carers in Case of an Emergency
10.6. Home Asthma Care
10.7. Dr Alexa
10.8. Health Information at Home Through Voice Technology
10.9. Digital Health Apps Using Amazon Alexa
10.10. 3rings
10.11. Artificial intelligence in health care diagnostics: state-of-the-art and competitive landscape
10.12. The rise of biomedical data
10.13. Measures in deep learning(1): sensitivity and specificity
10.14. Measures in deep learning(2): Area Under Curve (AUC) or area under curve of receiver operating characteristics (AUCROC).
10.15. Measures in deep learning(3): Reproducibility
10.16. F1-Score
10.17. When AUC is not a good measure of the algorithm success?
11. HOW AI HAS HELPED IN DIAGNOSTICS
11.1. 1) Skin disease: Dermoscopic melanoma recognition (2018)
11.2. Dermoscopic melanoma recognition and its challenges
11.3. SkinVision: a Netherland based firm to understand risk factors for skin cancer
11.4. Haut AI: Using machine learning to predict effect of everything in lifestyle on skin health
11.5. 2) Diagnosing diabetic retinopathy and diabetic macular edema from fundus photographs (2019)
11.6. Diabetic retinopathy: features of severity and learning structure
11.7. IDx: A company with $50M of funding with AI based diabetic retinopathy as the first focus
11.8. 3) Google DeepMind’s AI can detect over 50 sight-threatening eye conditions (2018)
11.9. DeepMind: automating triage based on OCT images
11.10. DeepMind: automating triage based on OCT images
11.11. DeepMind: Deep learning-based mind that knows about eye diseases
11.12. 4) Coronary Heart disease diagnosis (2018)
11.13. 5) Determination of ejection fraction from echocardiograms (2019)
11.14. Left ventricular ejection fraction assessment by deep learning procedure
11.15. Caption health (Bay Labs): Deep learning on echo cardiogram creation and interpretation
11.16. 6) Detection and quantification of breast densities via mammography (2018)
11.17. Breast cancer screening via mammograms and pathology slides
11.18. Reproducible breast cancer screening: Densitas, Kheiron Medical, and Therapixel
11.19. 7) Detection of stroke, brain bleeds, and other conditions from computerized axial tomography (2018)
11.20. Deep learning facilitates finding the rout cause of intracranial hemorrhage
11.21. Viz ai: Brain scan deep-learning-based analysis (a physician diagnostic assistant)
11.22. Brainomix: Brain imaging interpretation using deep learning
11.23. 8) Deep learning of lung cancer histopathological images is able to identify cancer cells, determine their type, and predict what somatic mutations are present in the tumor (2018)
11.24. Lung cancer detection made easier
11.25. Optellum: early lung cancer detection
11.26. 9) Facial image recognition to identify rare genetic disorders and to guide molecular diagnoses (2019)
11.27. Facial image and genetic disorder: FDNA approach vs NIH (US national institute of health) approach
11.28. 10) AI based time series analysis: AI applied to electrocardiograms can detect and classify arrhythmias (2019)
11.29. Cardiologist-Level Arrhythmia Detection by applying AI electrocardiograms: Stanford machine learning group (2017)
11.30. 11) AI based time series analysis: Detect atrial fibrillation (2018)
11.31. Current health (Snap40): a band for determining who is in health risk…how does this fit?
11.32. Cardiogram: accurately detecting various heart conditions with wearables
11.33. 12) AI based time series analysis: Detect cardiac contractile dysfunction(2019)
11.34. 12) AI based time series analysis: Detect blood chemistries linked to cardiac rhythm abnormalities
11.35. Cardiologs : A French based start up to help recognize abnormal heart conditions through deep learning
11.36. 14) AI based time series analysis: Detecting functional DNA sequence elements that are indicative of gene splicing (2014)
11.37. Deep genomics: Cell biology manipulation as intended, powered by AI
11.38. Electronic health records
11.39. Natural language processing to facilitate useful healthcare record taking and big data interpretation
11.40. Ubiquity of Electronic Health Records (EHRs) and learning from them
11.41. Information extraction from EHRs and EHR representation learning
11.42. Outcome Prediction, Computational Phenotyping, and Clinical Data De-identification from EHRs by deep learning